Fibrinolytic enzymes, such as Nattokinases from Bacillus species are known to degrade the fibrin blood clots. They belong to serine protease group having commercial applications, such as therapeutic ...agents and functional food formulation.
The present study reports some characteristics and fibrinolytic activity of serine protease from B. subtilis C10 strain that was isolated from shrimp shell.
Extracellular enzyme from B. subtilis C10 culture was harvested and partially purified by ammonium sulphate precipitation. Fibrinolytic activity of the enzyme was determined by zymography and measured by spectrophotometry with fibrinogen and thrombin used as substrates. The optimal temperature and pH for fibrinolytic activity were studied in the range of 31-43ºC and 5-10, respectively. The thermal and pH stability of enzyme was studied by incubating enzyme for 30 min in the same range of temperature and pH as above. The effect of some metal ions and reagents on fibrinolytic activity of enzyme was evaluated by concentrations of 5 mM and 5%, respectively.
Zymogram analysis indicated the presence of four fibrinolytic enzymes with molecular weights of approximately 69, 67, 39 and 36 kDa. The optimal temperature and pH for enzyme activity were 37°C and 9, respectively. The thermal and pH stability ranged from 35-39°C and 8-10, respectively. Fibrinolytic activity reached a maximum value of about 400 U/mg protein after 16 h of C10 strain culture. Enzyme has been drastically inhibited by PMSF and SDS, and partially inhibited by EDTA, while Triton X-100 has significantly increased enzyme activity. Effects of ions such as Mg2+, Ca2+ and Mn2+ on enzyme were negligible, except Cu2+ and Zn2+ have strongly decreased its activity.
Results from the present study suggested that enzyme obtained from B. subtilis C10 could be serine protease that has a high fibrinolytic activity up to about 400 U/mg protein at the most appropriate temperature and pH of 37ºC and 9. This activity can be improved up to 142% by incubating enzyme with 5% Triton X-100 for 30 min.
This retrospective study evaluated the diagnostic efficacy of magnetic resonance imaging (MRI) for identifying acute appendicitis during pregnancy.
This retrospective study enrolled a total of 46 ...pregnant patients with clinically suspected acute appendicitis who underwent 1.5 T MRI and received a final pathological diagnosis. We evaluated the imaging characteristics associated with patients diagnosed with acute appendicitis, including the appendix diameter, the appendix wall thickness, intra-appendiceal fluid collection, and peri-appendiceal fat infiltration. A bright appendix on T1-weighted 3-dimensional imaging was identified as a negative sign for appendicitis.
Peri-appendiceal fat infiltration had the highest specificity of 97.1% for diagnosing acute appendicitis, whereas increasing appendiceal diameter had the highest sensitivity of 91.7%. The cut-off values for increasing appendiceal diameter and appendiceal wall thickness were 6.55 mm and 2.7 mm, respectively. Using these cut-off values, appendiceal diameter had a sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) of 91.7%, 91.2%, 78.4%, and 96.9%, respectively, whereas these values for appendiceal wall thickness were 75.0%, 91.2%, 75.0%, and 91.2%. The combination of increasing appendiceal diameter and appendiceal wall thickness resulted in an area under the receiver operating characteristic curve value of 0.958 with Se, Sp, PPV, and NPV values of 75.0%, 100.0%, 100.0%, and 91.9%, respectively.
All five MRI signs examined in this study had significant diagnostic value for detecting acute appendicitis during pregnancy, with p-values <0.01. The combined use of increasing appendiceal diameter and appendiceal wall thickness displayed the excellent ability to diagnose acute appendicitis in pregnant women.
Nanotechnology monitors a leading agricultural controlling process, especially by its miniature dimension. Additionally, many potential benefits such as enhancement of food quality and safety, ...reduction of agricultural inputs, enrichment of absorbing nanoscale nutrients from the soil, etc. allow the application of nanotechnology to be resonant encumbrance. Agriculture, food, and natural resources are a part of those challenges like sustainability, susceptibility, human health, and healthy life. The ambition of nanomaterials in agriculture is to reduce the amount of spread chemicals, minimize nutrient losses in fertilization and increased yield through pest and nutrient management. Nanotechnology has the prospective to improve the agriculture and food industry with novel nanotools for the controlling of rapid disease diagnostic, enhancing the capacity of plants to absorb nutrients among others. The significant interests of using nanotechnology in agriculture includes specific applications like nanofertilizers and nanopesticides to trail products and nutrients levels to increase the productivity without decontamination of soils, waters, and protection against several insect pest and microbial diseases. Nanotechnology may act as sensors for monitoring soil quality of agricultural field and thus it maintain the health of agricultural plants. This review covers the current challenges of sustainability, food security and climate change that are exploring by the researchers in the area of nanotechnology in the improvement of agriculture.
Context:
Body weight is the most important anthropometric determinant of bone mineral density (BMD). Body weight is mainly made up of lean mass (LM) and fat mass (FM), and which component is more ...important to BMD has been a controversial issue.
Objective:
This study sought to compare the magnitude of association between LM, FM, and BMD by using a meta-analytic approach.
Data Source:
Using an electronic and manual search, we identified 44 studies that had examined the correlation between LM, FM, and BMD between 1989 and 2013. These studies involved 20 226 men and women (4966 men and 15 260 women) aged between 18 and 92 years. We extracted the correlations between LM, FM, and BMD at the lumbar spine, femoral neck, and whole body. The synthesis of correlation coefficients was done by the random-effects meta-analysis model.
Results:
The overall correlation between LM and femoral neck BMD (FNBMD) was 0.39 (95% confidence interval, 0.34 to 0.43), which was significantly higher than the correlation between FM and FNBMD (0.28; 95% confidence interval, 0.22 to 0.33). The effect of LM on FNBMD in men (r = 0.43) was greater than that in women (r = 0.38). In premenopausal women, the effect of LM on BMD was greater than the effect of FM (r = 0.45 vs r = 0.30); however, in postmenopausal women, the effects of LM and FM on BMD were comparable (r = 0.33 vs r = 0.31).
Conclusion:
LM exerts a greater effect on BMD than FM in men and women combined. This finding underlines the concept that physical activity is an important component in the prevention of bone loss and osteoporosis in the population.
In this 14‐year prospective study, men and women were found to share a common set of risk factors for hip fracture: low BMD, postural instability and/or quadriceps weakness, a history of falls, and ...prior fracture. The combination of these risk factors accounted for 57% and 37% of hip fractures in women and men, respectively.
Introduction: Risk factors for hip fracture, including low BMD, identified in women, have not been shown to be useful in men. It is also not known whether fall‐related factors (muscle strength and postural instability) predict hip fracture. This study examined the association between falls‐related factors and hip fractures in elderly men and women.
Materials and Methods: This is an epidemiologic, community‐based prospective study, which included 960 women and 689 men ≥60 years of age who have been followed for a median of 12 years (interquartile range, 6–13). The number of person‐years was 9961 for women and 4463 for men. The outcome measure was incidence of hip fracture. Risk factors were femoral neck BMD (FNBMD), postural sway, quadriceps strength, prior fracture, and fall.
Results: Between 1989 and 2003, 115 (86 women) sustained a hip fracture. The risk of hip fracture (as measured by hazards ratio HR) was increased by 3.6‐fold (95% CI: 2.6–4.5) in women and 3.4‐fold (95% CI: 2.5–4.6) in men for each SD (0.12 g/cm2) reduction in FNBMD. After adjusting for BMD, the risk of hip fracture was also increased in individuals with the highest tertile of postural sway (HR: 2.7; 95% CI: 1.6–4.5) and low tertiles of quadriceps strength (HR: 3.0; 95% CI: 1.3–6.8). Furthermore, a history of fall during the preceding 12 months and a history of fracture were independent predictors of hip fracture. For each level of BMD, the risk of hip fracture increased linearly with the number of non‐BMD risk factors. Approximately 57% and 37% of hip fracture cases in women and men, respectively, were attributable to the presence of risk factors, osteoporosis (BMD T score ≤ −2.5), and advancing age.
Conclusions: Men and women had a common set of risk factors for hip fracture: low BMD, postural instability and/or quadriceps weakness, a history of falls, and prior fracture. Preventive strategies should simultaneously target reducing falls and improvement of bone strength in both men and women.
The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of ...multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning-an emerging machine learning field-and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.
A
bstract
We consider the strong dynamics associated with a composite Higgs model that simultaneously produces dynamical axions and solves the strong CP problem. The strong dynamics arises from a new ...Sp or SU(4) hypercolor gauge group containing QCD colored hyperfermions that confines at a high scale. The hypercolor global symmetry is weakly gauged by the Standard Model electroweak gauge group and an enlarged color group, SU(
N
+ 3)
×
SU(
N
)
′
. When hyperfermion condensates form, they not only lead to an SU(5)/SO(5) composite Higgs model but also spontaneously break the enlarged color group to SU(3)
c
×
SU(
N
)
D
. At lower energies, the SU(
N
)
D
group confines, producing two dynamical axions that eliminates all CP violation. Furthermore, small instantons from the SU(
N
)
′
group can enhance the axion mass, giving rise to TeV scale axion masses that can be detected at collider experiments. Our model provides a way to unify the composite Higgs with dynamical axions, without introducing new elementary scalar fields, while also extending the range of axion masses that addresses the strong CP problem.
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•Microalgae has more lipid content while macroalgae is rich of carbohydrate.•Algae is efficient in nutrients removal.•Combined processes, optimization and algae strains screening are ...important.•Revenues in algae-based biofuel production and wastewater remediation are limited.•Policy and social support for commercialization are lacking.
Algae is a well-known organism that its characteristic is prominent for biofuel production and wastewater remediation. This critical review aims to present the applicability of algae with in-depth discussion regarding three key aspects: (i) characterization of algae for its applications; (ii) the technical approaches and their strengths and drawbacks; and (iii) future perspectives of algae-based technologies. The process optimization and combinations with other chemical and biological processes have generated efficiency, in which bio-oil yield is up to 41.1%. Through life cycle assessment, algae bio-energy achieves high energy return than fossil fuel. Thus, the algae-based technologies can reasonably be considered as green approaches. Although selling price of algae bio-oil is still high (about $2 L−1) compared to fossil fuel’s price of $1 L−1, it is expected that the algae bio-oil’s price will become acceptable in the next coming decades and potentially dominate 75% of the market.
•This study predicts the residential solid waste generation rates in Vietnam•Six data-driven machine learning models were tested using different input variables•Urban population: the most essential ...and definitive variable for the models•Random forest, and k nearest neighbor were the most effective algorithms•The developed model provides reliable future data for integrated solid waste management
The main aim of this work was to compare six machine learning (ML) - based models to predict the municipal solid waste (MSW) generation from selected residential areas of Vietnam. The input data include eight variables that cover the economy, demography, consumption and waste generation characteristics of the study area. The model simulation results showed that the urban population, average monthly consumption expenditure, and total retail sales were the most influential variables for MSW generation. Among the ML models, the random forest (RF), and k-nearest neighbor (KNN) algorithms show good predictive ability of the training data (80% of the data), with an R2 value > 0.96 and a mean absolute error (MAE) of 121.5–125.0 for the testing data (20% of the data). The developed ML models provided reliable forecasting of the data on MSW generation that will help in the planning, design and implementation of an integrated solid waste management action plan for Vietnam. The limitations of this work may be the heterogeneity of the dataset, such as the lack of data from lower administrative units in the country. In such cases, the predictive ML algorithm can be updated and re-trained in the future when the reliable data is added.
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In a sample of 1358 women and 858 men, ≥60 yr of age who have been followed‐up for up to 15 yr, it was estimated that the mortality‐adjusted residual lifetime risk of fracture was 44% for women and ...25% for men. Among those with BMD T‐scores ≤ −2.5, the risks increased to 65% in women and 42% in men.
Introduction: Risk assessment of osteoporotic fracture is shifting from relative risk to an absolute risk approach. Whereas BMD is a primary predictor of fracture risk, there has been no estimate of mortality‐adjusted lifetime risk of fracture by BMD level. The aim of the study was to estimate the residual lifetime risk of fracture (RLRF) in elderly men and women.
Materials and Methods: Data from 1358 women and 858 men ≥60 yr of age as of 1989 of white background from the Dubbo Osteoporosis Epidemiology Study were analyzed. The participants have been followed for up to 15 yr. During the follow‐up period, incidence of low‐trauma, nonpathological fractures, confirmed by X‐ray and personal interview, were recorded. Incidence of mortality was also recorded. BMD at the femoral neck was measured by DXA (GE‐LUNAR) at baseline. Residual lifetime risk of fracture from the age of 60 was estimated by the survival analysis taking into account the competing risk of death.
Results: After adjusting for competing risk of death, the RLRF for women and men from age 60 was 44% (95% CI, 40–48) and 25% (95% CI, 19–31), respectively. For individuals with osteoporosis (BMD T‐scores ≤ −2.5), the mortality‐adjusted lifetime risk of any fracture was 65% (95% CI, 58–73) for women and 42% (95% CI, 24–71) for men. For the entire cohort, the lifetime risk of hip fracture was 8.5% (95% CI, 6–11%) for women and 4% (95% CI, 1.3–5.4%) for men; risk of symptomatic vertebral fracture was 18% (95% CI, 15–21%) for women and 11% (95% CI, 7–14%) for men.
Conclusions: These estimates provide a means to communicate the absolute risk of fracture to an individual patient and can help promote the identification and targeting of high‐risk individuals for intervention.
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