Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Thermomechanical and Static Loading on the Load to Fracture of Metal‐Ceramic, Monolithic, and Veneered Zirconia Posterior Fixed Partial Dentures

PURPOSE To evaluate the influence of static (not preloaded) and thermomechanical loading on the load to fracture of metal-ceramic, monolithic and veneered zirconia computer-aided design/computer-aided manufacturing (CAD/CAM) posterior fixed partial dentures (FPDs). MATERIALS AND METHODS One hundred standardized specimens with 2 abutments screwed onto a platform were prepared from stainless steel to receive a posterior 3-unit FPD with an intermediate pontic. Specimens were randomly divided into 5 groups (n = 20): Metal-ceramic (control group), Lava Zirconia system, Vita In-Ceram YZ, IPS e.max ZirCAD, and Lava Plus. Half of the specimens of each group (n = 10) underwent no preloading, and the other half were subjected to thermomechanical loading in a masticatory simulator, and then all FPDs were loaded until fracture using a universal testing machine at a 1 mm/min crosshead speed. The load to fracture of the veneering ceramic and the load to fracture of framework (total fracture) were recorded for each specimen. Data were statistically analyzed using 2-way ANOVA, Tukey's HSD post-hoc test, Student's t test, and Weibull statistics, α = 0.05. RESULTS Significant differences were recorded between the metal-ceramic and veneered zirconia groups for the veneering ceramic load (p < 0.001; f = 36.62; f = 57.76) in no preloading and thermomechanical loading subgroups, respectively, but no differences were observed between the static and thermomechanical loading conditions. No differences were observed among the veneered zirconia groups. For the total load to fracture, significant differences were observed according to the material (p < 0.001; f = 500.8), between the metal-ceramic and Lava Plus group and the other zirconia groups in no preloading subgroup, and between metal-ceramic and the other groups (p < 0.001; f = 303.33) in thermomechanical loading subgroup. For the type of preloading, significant differences were observed (p = 0.02; f = 5.24) between the Lava Plus group and the other groups. Thermomechanical loading significantly decreased the fracture load of the Lava Plus group (p = 0.005). The Weibull statistics corroborated the results. CONCLUSIONS Monolithic zirconia restorations provided the highest load to fracture values among the zirconia groups tested; however, the results indicate that they must be used in the oral environment with caution, because their load to fracture was influenced by the aging simulation.
Impact of Promoter Polymorphisms on the Transcriptional Regulation of the Organic Cation Transporter OCT1 (SLC22A1)

The organic cation transporter 1 (OCT1, SLC22A1) is strongly expressed in the human liver and facilitates the hepatic uptake of drugs such as morphine, metformin, tropisetron, sumatriptan and fenoterol and of endogenous substances such as thiamine. OCT1 expression is inter-individually highly variable. Here, we analyzed SNPs in the OCT1 promoter concerning their potential contribution to the variability in OCT1 expression. Using electrophoretic mobility shift and luciferase reporter gene assays in HepG2, Hep3B, and Huh7 cell lines, we identified the SNPs −1795G>A (rs6935207) and −201C>G (rs58812592) as having effects on transcription factor binding and/or promoter activity. The A-allele of the −1795G>A SNP showed allele-specific binding of the transcription factor NF-Y leading to 2.5-fold increased enhancer activity of the artificial SV40 promoter. However, the −1795G>A SNP showed no significant effects on the native OCT1 promoter activity. Furthermore, the −1795G>A SNP was not associated with the pharmacokinetics of metformin, fenoterol, sumatriptan and proguanil in healthy individuals or tropisetron efficacy in patients undergoing chemotherapy. Allele-dependent differences in USF1/2 binding and nearly total loss in OCT1 promoter activity were detected for the G-allele of −201C>G, but the SNP is apparently very rare. In conclusion, common OCT1 promoter SNPs have only minor effects on OCT1 expression.
Response Surface Methodology for the Optimization of Ultrasound-Assisted Extraction of Tetrodotoxin from the Liver of Takifugu pseudommus

Tetrodotoxin (TTX) is a marine biotoxin that has high scientific value. However, the lack of efficient TTX extraction and preparation methods has led to a scarcity of TTX samples for clinical application. In this study, TTX from the liver of Takifugu pseudommus was ultrasound-assisted extracted with acidified organic solvents. The extraction process was analyzed and optimized by single factor method and response surface methodology (RSM). The optimal extraction conditions predicted by a response surface model were as follows: liquid:material ratio, 2.8:1; extraction temperature, 60 °C; extraction time, 23.3 min. Under these conditions, the extraction of TTX had a yield of 89.65%, and the results were further verified by experimental extraction, and analyzed by ultra performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS). It was found that the extracts of T. pseudommus liver contained TTX and its four analogues at certain proportions (TTX: 10.4%; 5,6,11-trideoxyTTX: 83.3%; 5,11-dideoxyTTX:2.4%; 4,9-anhydro TTX:2.6%; 5-deoxyTTX:1.3%). This study demonstrates a stable and efficient extraction process of TTX from pufferfish liver, which can be helpful for further research and analysis, as well as the utilization of TTX from pufferfish.
Structure, heterogeneity and developability assessment of therapeutic antibodies

ABSTRACT Increasing attention has been paid to developability assessment with the understanding that thorough evaluation of monoclonal antibody lead candidates at an early stage can avoid delays during late-stage development. The concept of developability is based on the knowledge gained from the successful development of approximately 80 marketed antibody and Fc-fusion protein drug products and from the lessons learned from many failed development programs over the last three decades. Here, we reviewed antibody quality attributes that are critical to development and traditional and state-of-the-art analytical methods to monitor those attributes. Based on our collective experiences, a practical workflow is proposed as a best practice for developability assessment including in silico evaluation, extended characterization and forced degradation using appropriate analytical methods that allow characterization with limited material consumption and fast turnaround time.
Network-based features enable prediction of essential genes across diverse organisms

Machine learning approaches to predict essential genes have gained a lot of traction in recent years. These approaches predominantly make use of sequence and network-based features to predict essential genes. However, the scope of network-based features used by the existing approaches is very narrow. Further, many of these studies focus on predicting essential genes within the same organism, which cannot be readily used to predict essential genes across organisms. Therefore, there is clearly a need for a method that is able to predict essential genes across organisms, by leveraging network-based features. In this study, we extract several sets of network-based features from protein–protein association networks available from the STRING database. Our network features include some common measures of centrality, and also some novel recursive measures recently proposed in social network literature. We extract hundreds of network-based features from networks of 27 diverse organisms to predict the essentiality of 87000+ genes. Our results show that network-based features are statistically significantly better at classifying essential genes across diverse bacterial species, compared to the current state-of-the-art methods, which use mostly sequence and a few ‘conventional’ network-based features. Our diverse set of network properties gave an AUROC of 0.847 and a precision of 0.320 across 27 organisms. When we augmented the complete set of network features with sequence-derived features, we achieved an improved AUROC of 0.857 and a precision of 0.335. We also constructed a reduced set of 100 sequence and network features, which gave a comparable performance. Further, we show that our features are useful for predicting essential genes in new organisms by using leave-one-species-out validation. Our network features capture the local, global and neighbourhood properties of the network and are hence effective for prediction of essential genes across diverse organisms, even in the absence of other complex biological knowledge. Our approach can be readily exploited to predict essentiality for organisms in interactome databases such as the STRING, where both network and sequence are readily available. All codes are available at https://github.com/RamanLab/nbfpeg.
Prediction Algorithm of the Cat Spinal Segments Lengths and Positions in Relation to the Vertebrae

Detailed knowledge of the topographic organization and precise access to the spinal cord segments is crucial for the neurosurgical manipulations as well as in vivo neurophysiological investigations of the spinal networks involved in sensorimotor and visceral functions. Because of high individual variability, accurate identification of particular portion of the lumbosacral enlargement is normally possible only during postmortem dissection. Yet, it is often necessary to determine the precise location of spinal segments prior to in vivo investigation, targeting spinal cord manipulations, neurointerface implantations, and neuronal activity recordings. To solve this problem, we have developed an algorithm to predict spinal segments locations based on their relation to vertebral reference points. The lengths and relative positions of the spinal cord segments (T13‐S3) and the vertebrae (VT13‐VL7) were measured in 17 adult cats. On the basis of these measurements, we elaborated the estimation procedure: the cubic regression of the ratio of the segment's length to the lengths of the VL2 vertebra was used for the determination of segment's length; and the quadratic regression of the ratio of their positions in relation to the VL2 rostral part was used to determine the position of the segments. The coefficients of these regressions were calculated at the training sample (nine cats) and were then confirmed at the testing sample (eight cats). Although the quality of the prediction is decreased in the caudal direction, we found high correlations between the regressions and real data. The proposed algorithm can be further translated to other species including human. Anat Rec, 302:1628–1637, 2019. © 2018 American Association for Anatomy
Using Smart Socks to Detect Step-count at Slow Walking Speeds in Healthy Adults

Abstract We examined the accuracy of Smart Socks – a device that measures foot pressure during gait for detecting step-count across various walking speeds. Thirty-six participants (17 men; 19 women) wore Smart Socks (Sock), a pedometer (Pedometer), and a smartphone with a commercially available Phone Application (Phone) pedometer to measure step-count during 3-min of treadmill or over-ground walking at 1.3, 2.2, 3.0, 3.8, and 4.7 km/h. Steps were compared to a gold-standard tally-counter (Count), collected by independent assessors. All devices (Sock, Pedometer, and Phone) underestimated step-count when compared to Count at 1.3 km/h (p<0.05); however, Sock (27±18%) demonstrated a lower percent error compared to Phone (40±28%) and Pedometer (98±5%) (both p<0.01). At 2.2 km/h, Sock was not different compared to Count (Sock: 213±39; Count: 229±24 steps, p=0.25); however, both Phone (271±55 steps) and Pedometer (169±166 steps) were different compared to Count (p<0.05). At 3.0 km/h, both Sock (258±30 steps) and Pedometer (254±45 steps) were similar to Count (267±22 steps) (p>0.05); however, Phone (291±28 steps) overestimated step-count (p<0.01). All devices (Sock, Pedometer, and Phone) were similar to Count at 3.8, and 4.7 km/h (p>0.05). These findings demonstrate that Smart Socks are more accurate than pedometers used in the present study for detecting step-count during treadmill or over-ground ambulation at slower walking speeds.
Tuning model parameters in class‐imbalanced learning with precision‐recall curve

An issue for class‐imbalanced learning is what assessment metric should be employed. So far, precision‐recall curve (PRC) as a metric is rarely used in practice as compared with its alternative of receiver operating characteristic (ROC). This study investigates the performance of PRC as the evaluating criterion to address the class‐imbalanced data and focuses on the comparison of PRC with ROC. The advantages of PRC over ROC on assessing class‐imbalanced data are also investigated and tested on our proposed algorithm by tuning the whole model parameters in simulation studies and real data examples. The result shows that PRC is competitive with ROC as performance measurement for handling class‐imbalanced data in tuning the model parameters. PRC can be considered as an alternative but effective assessment for preprocessing (such as variable selection) skewed data and building a classifier in class‐imbalanced learning.
One-Step Synthesis of Ag@TiO2 Nanoparticles for Enhanced Photocatalytic Performance

Polyamide network polymers (PNP) modified TiO2 nanoparticles (NPs) were decorated with Ag NPs in hydrothermal gel method, forming one-step synthesized photocatalysts, Ag@TiO2 NPs. The effect of PNP and the amount of Ag NPs added were investigated in this work. PNP acted as a nanocage to prevent TiO2 aggregation and capture Ag accurately, which could effectively control product sizes and improve dispersibility in solvents. Simultaneously, TiO2 NPs modified with Ag NPs exhibited remarkable photocatalytic effects. One-step synthesis simplified the experimental process and avoided the agglomeration of silver ions during the secondary reaction, achieving the purpose of uniform distribution at a specific location of TiO2 NPs. The prepared Ag@TiO2 NPs-0.5 could remove 79.49% of Methyl Orange (MO) after 3 h of ultraviolet light irradiation, which was 2.7 times higher than the reaction rate of pure TiO2 NPs. It also exhibited good photoactivity under Visible light conditions. Moreover, the mineralization rate of MO over the Ag@TiO2 NPs-0.5 could be up to 72.32% under UV light and 47.08% under Visible light irradiation, which revealed that the prepared catalysts could effectively degrade most of the MO to CO2 and H2O. The samples also demonstrated the excellent stability and easy recyclability with over 90% of the original catalytic level for MO degradation. The photocatalysts studied also exerted broad application prospects such as photovoltaic hydrogen production, electronic sensors and biomedicine.
Ward-Based Non-Invasive Ventilation in Acute Exacerbations of COPD: A Narrative Review of Current Practice and Outcomes in the UK

Non-invasive ventilation (NIV) is frequently used as a treatment for acute hypercapnic respiratory failure (AHRF) in hospitalised patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). In the UK, many patients with AHRF secondary to AECOPD are treated with ward-based NIV, rather than being treated in critical care. NIV has been increasingly used as an alternative to invasive ventilation and as a ceiling of treatment in patients with a ‘do not intubate’ order. This narrative review describes the evidence base for ward-based NIV in the context of AECOPD and summarises current practice and clinical outcomes in the UK.
Activated Rho GTPases in Cancer—The Beginning of a New Paradigm

Involvement of Rho GTPases in cancer has been a matter of debate since the identification of the first members of this branch of the Ras superfamily of small GTPases. The Rho GTPases were ascribed important roles in the cell, although these were restricted to regulation of cytoskeletal dynamics, cell morphogenesis, and cell locomotion, with initially no clear indications of direct involvement in cancer progression. This paradigm has been challenged by numerous observations that Rho-regulated pathways are often dysregulated in cancers. More recently, identification of point mutants in the Rho GTPases Rac1, RhoA, and Cdc42 in human tumors has finally given rise to a new paradigm, and we can now state with confidence that Rho GTPases serve as oncogenes in several human cancers. This article provides an exposé of current knowledge of the roles of activated Rho GTPases in cancers.
Tranilast Treatment Attenuates Cerebral Ischemia‐Reperfusion Injury in Rats Through the Inhibition of Inflammatory Responses Mediated by NF‐κB and PPARs

Ischemia‐reperfusion injury (IRI) occurs when blood supply returns to tissue after interruption, which is associated with life‐threatening inflammatory response. Tranilast is a widely used antiallergic agent in the treatment against bronchial asthma and keloid. To study the function of tranilast, we used IRI in rat models. The brain tissues of IRI rats with or without tranilast treatment were collected. Neuronal apoptosis in the brain was detected by terminal deoxynucleotidyl transferase nick end labeling assay, and proinflammatory cytokine levels were measured by quantitative real‐time polymerase chain reaction and enzyme‐linked immunosorbent assay. The expression levels of nuclear factor‐kappa B (NF‐κB), inhibitor of κB (IκB) and peroxisome proliferator‐activated receptors (PPARs) were detected by Western blot. The results showed that tranilast treatment reduced neuronal apoptosis in the brain of IRI rats. Tranilast enhanced the short‐term memory and long‐term memory to novel object recognition paradigm. Tranilast treatment decreased the messenger RNA (mRNA) and protein levels of multiple proinflammatory cytokines, and affected NF‐κB and inhibitor of kappa B protein expressions. Tranilast promoted the expressions of PPAR‐α and PPAR‐γ. Our findings demonstrate that tranilast treatment could attenuate cerebral IRI by regulating the inflammatory cytokine production and PPAR expression. Tranilast is a potential drug for IRI treatment in the clinic.
Modulation of Innate Immune Responses by the Influenza A NS1 and PA-X Proteins

Influenza A viruses (IAV) can infect a broad range of animal hosts, including humans. In humans, IAV causes seasonal annual epidemics and occasional pandemics, representing a serious public health and economic problem, which is most effectively prevented through vaccination. The defense mechanisms that the host innate immune system provides restrict IAV replication and infection. Consequently, to successfully replicate in interferon (IFN)-competent systems, IAV has to counteract host antiviral activities, mainly the production of IFN and the activities of IFN-induced host proteins that inhibit virus replication. The IAV multifunctional proteins PA-X and NS1 are virulence factors that modulate the innate immune response and virus pathogenicity. Notably, these two viral proteins have synergistic effects in the inhibition of host protein synthesis in infected cells, although using different mechanisms of action. Moreover, the control of innate immune responses by the IAV NS1 and PA-X proteins is subject to a balance that can determine virus pathogenesis and fitness, and recent evidence shows co-evolution of these proteins in seasonal viruses, indicating that they should be monitored for enhanced virulence. Importantly, inhibition of host gene expression by the influenza NS1 and/or PA-X proteins could be explored to develop improved live-attenuated influenza vaccines (LAIV) by modulating the ability of the virus to counteract antiviral host responses. Likewise, both viral proteins represent a reasonable target for the development of new antivirals for the control of IAV infections. In this review, we summarize the role of IAV NS1 and PA-X in controlling the antiviral response during viral infection.
Carbon‐Based Fingerprint Powder as a One‐Step Development and Matrix Application for High‐Resolution Mass Spectrometry Imaging of Latent Fingerprints

Carbon‐based materials are often used as matrices for matrix‐assisted laser desorption/ionization mass spectrometry (MALDI‐MS) and its imaging (MALDI‐MSI). However, researchers have refrained from using carbon‐based fingerprint powder (CFP) as a matrix due to high background and contamination. In this work, the compatibility of CFP is reevaluated with MALDI‐MSI using a high‐resolution mass spectrometer (HRMS) and compared to traditional organic matrices. Relevant fingerprint compounds were easily distinguished from carbon cluster peaks when using HRMS. For fair comparison, half of a fingerprint was dusted with CFP while the other half was dusted with traditional organic matrices. All compounds studied had comparable, or higher, signal‐to‐noise (S/N) ratios when CFP was used as the matrix. Additionally, chemical image qualities closely followed the trend of S/N ratios. CFP proved to be an effective one‐step development and matrix application technique for MALDI‐MSI of latent fingerprints, when carbon cluster peaks are well separated by a HRMS.
New Insights in Bladder Cancer Diagnosis: Urinary miRNAs and Proteins

Bladder cancer is the 10th-most common cancer worldwide. The diagnosis and follow-up of patients require costly invasive methods and due to these expenses, bladder cancer continues to be one of the expensive malignancies. Early diagnosis is crucial in bladder cancer as it is in other cancers; therefore, non-invasive biomarkers for early diagnosis are very important. In this review, we aimed to focus on the most recent investigations on potential urinary micro RNA (miRNA) and protein biomarkers for bladder cancer diagnosis and their associated pathways. Studies performed by different groups were compiled and the biomarker properties of various proteins and miRNAs in the urine of bladder cancer patients were evaluated. Key studies were obtained by searching keywords “bladder cancer, urinary miRNA, urinary protein, urinary biomarker”. Targets and the pathways of the miRNAs and proteins were analyzed according to mirBase Catalogue and Panther Database. The major pathways that are targeted by aberrantly expressed miRNAs are Cholecystokinin receptor (CCKR), p53, Wnt signaling pathway, and feedback loops. We hereby conclude that urinary micro RNAs and proteins are promising candidates for bladder cancer diagnosis. It should be noted that urine collection, storage conditions, choice of fraction, and normalization strategies should be standardized.
Proteomic Characterization of Bradyrhizobium diazoefficiens Bacteroids Reveals a Post-Symbiotic, Hemibiotrophic-Like Lifestyle of the Bacteria within Senescing Soybean Nodules

The form and physiology of Bradyrhizobium diazoefficiens after the decline of symbiotic nitrogen fixation has been characterized. Proteomic analyses showed that post-symbiotic B. diazoefficiens underwent metabolic remodeling as well-defined groups of proteins declined, increased or remained unchanged from 56 to 119 days after planting, suggesting a transition to a hemibiotrophic-like lifestyle. Enzymatic analysis showed distinct patterns in both the cytoplasm and the periplasm. Similar to the bacteroid, the post-symbiotic bacteria rely on a non-citric acid cycle supply of succinate and, although viable, they did not demonstrate the ability to grow within the senescent nodule.