Unveiling the effect of Ni on the formation and structure of Earth's inner core

Ni is the second most abundant element in the Earth's core. Yet, its effects on the inner core's structure and formation process are usually disregarded because of its electronic and size similarity with Fe. Using ab initio molecular dynamics simulations, we find that the bcc phase can spontaneously crystallize in liquid Ni at temperatures above Fe's melting point at inner core pressures. The melting temperature of Ni is shown to be 700 to 800 K higher than that of Fe at 323 to 360 GPa. hcp, bcc, and liquid phase relations differ for Fe and Ni. Ni can be a bcc stabilizer for Fe at high temperatures and inner core pressures. A small amount of Ni can accelerate Fe's crystallization at core pressures. These results suggest that Ni may substantially impact the structure and formation process of the solid inner core.

Mapping stress inside living cells by atomic force microscopy in response to environmental stimuli

The response of cells to environmental stimuli, under either physiological or pathological conditions, plays a key role in determining cell fate toward either adaptive survival or controlled death. The efficiency of such a feedback mechanism is closely related to the most challenging human diseases, including cancer. Since cellular responses are implemented through physical forces exerted on intracellular components, more detailed knowledge of force distribution through modern imaging techniques is needed to ensure a mechanistic understanding of these forces. In this work, we mapped these intracellular forces at a whole-cell scale and with submicron resolution to correlate intracellular force distribution to the cytoskeletal structures. Furthermore, we visualized dynamic mechanical responses of the cells adapting to environmental modulations in situ. Such task was achieved by using an informatics-assisted atomic force microscope (AFM) indentation technique where a key step was Markov-chain Monte Carlo optimization to search for both the models used to fit indentation force-displacement curves and probe geometry descriptors. We demonstrated force dynamics within cytoskeleton, as well as nucleoskeleton in living cells which were subjected to mechanical state modulation: myosin motor inhibition, micro-compression stimulation and geometrical confinement manipulation. Our results highlight the alteration in the intracellular prestress to attenuate environmental stimuli; to involve in cellular survival against mechanical signal-initiated death during cancer growth and metastasis; and to initiate cell migration.

A theoretical characterization method for non-spherical core-shell nanoparticles by XPS

Core-shell nanoparticles (NPs) are active research areas for their unique properties and wide applications. By changing the elemental composition in the core and shell, a series of core-shell NPs with specific functions can be obtained, where the sizes of the core and shell also influence the properties. X-ray photoelectron spectroscopy (XPS) is useful in this context as a means of quantitatively analyzing such NPs. The empirical formula proposed by Shard [J. Phys. Chem. C, 2012, 116(31), 16806-16813] for calculating the shell thickness of the spherical core-shell NPs has been verified by Powell et al. [J. Phys. Chem. C, 2016, 120(39), 22730-22738] through a simulation of XPS with Simulation of Electron Spectra for Surface Analysis (SESSA) software. However, real core-shell NPs are not necessarily ideal spheres; such NPs can have rich shapes and uneven thicknesses. This work aims to extend the Shard formula to non-ideal core-shell NPs. We have used a Monte Carlo simulation method to study the XPS signal variation with the shell thickness for several modeled non-spherical shapes of core-shell NPs including some complex geometric structures which are numerically constructed with finite-element triangular meshes. Five types of non-spherical shapes, i.e. egg, ellipsoid, rod, rough-surface, and star shapes, are considered, while the size parameters are varied over a wide range. The equivalent radius and equivalent thickness are defined to characterize the average size of the nanoparticles for the use of the Shard formula. We have thus derived an extended Shard formula for the specific core-shell NPs, with which the relative error between the predicted shell thickness and the real thickness can be reduced to less than 10%.

Improved reverse Monte Carlo analysis of optical property of Fe and Ni from reflection electron energy loss spectroscopy spectra

The energy loss functions (ELFs) of Fe and Ni have been derived from measured reflection electron energy loss spectroscopy (REELS) spectra by a reverse Monte Carlo analysis in our previous work. In this work, we present further improvements of ELFs for these metals. For Fe, we have updated ELFs at primary electron energies of 2 keV and 3 keV in a wider photon energy region (0-180 eV) with a better accuracy, which is verified by sum rules. Regarding to Ni, we supplement the ELF at primary energy of 5 keV and we also improve the data accuracy at 3 keV. Applying these new and more accurate ELFs we present the optical constants and dielectric functions for the two metals. The improvements were highlighted by comparing our present results with the previous data.

Establishment and validation of an electron inelastic mean free path database for narrow bandgap inorganic compounds with a machine learning approach

Narrow bandgap inorganic compounds are extremely important in many areas of physics. However, their basic parameter database for surface analysis is incomplete. Electron inelastic mean free paths (IMFPs) are important parameters in surface analysis methods, such as electron spectroscopy and electron microscopy. Our previous research has presented a machine learning (ML) method to describe and predict IMFPs from calculated IMFPs for 41 elemental solids. This paper extends the use of the same machine learning method to 42 inorganic compounds based on the experience in predicting elemental electron IMFPs. The in-depth discussion extends to including material dependence discussion and parameter value selections. After robust validation of the ML method, we have produced an extensive IMFP database for 12 039 narrow bandgap inorganic compounds. Our findings suggest that ML is very efficient and powerful for IMFP description and database completion for various materials and has many advantages, including stability and convenience, over traditional methods.

Exploring the absolute yield curve of secondary electrons using machine learning methods

Knowledge of absolute secondary electron yield (δ) is important for various applications of electron emission materials. Besides, it is also crucial to know the dependence of δ on primary electron energy E_p and material properties like atomic number Z. The available experimental database of δ reveals a large discrepancy among the measurement data, while the oversimplified semi-empirical theories of secondary electron emission can only present the general shape of the yield curve but not the absolute yield value. This limits not only the validation of a Monte Carlo model for theoretical simulations but also presents large uncertainties in the applications of different materials for various purposes. In applications, it is highly desirable to have the knowledge of the absolute yield of a material. Therefore, it is highly desirable to establish the relationship of the absolute yield with material and electron energy based on the available experimental data. Recently, machine learning (ML) methods have been increasingly used for the prediction of material properties mainly based on the atomistic calculations with the first-principles theory. We propose here the application of ML models to a material property study, starting with experimental observations and unfolding the relationship of δ with basic material properties and primary electron energy. Our ML models are able to predict δ(E_p)-curve covering a wide energy range of 10 eV-30 keV for unknown elements within the uncertainty range of the experimental data and can suggest more reliable data among the scattered experimental data.

Application of optical flow algorithm for drift correction in electron microscopy images

Transmission electron microscopy (TEM) image drift correction has been effectively addressed using diverse approaches, including the cross correlation algorithm (CC) and other strategies. However, most of the strategies fall short of achieving sufficient accuracy or cannot strike a balance between time consumption and accuracy. The present study proposes a TEM image drift correction strategy that enhances accuracy without any additional time consumption. Unlike the CC algorithm that matches pixels one by one, our approach involves the extraction of multiple feature points from the first TEM image and then uses the Lucas-Kanade (LK) optical flow algorithm to calculate the optical field of these feature points in the subsequent TEM images. The LK algorithm is used to calculate the instantaneous velocity of these feature points, which can help track the movement of the TEM image series. In addition, a high-precision sub-pixel level correction strategy by the utilization of linear interpolation during the correction process is developed in this work. Experimental results confirm that this strategy offers superior accuracy in comparison with the CC algorithm and also is insensitive to the size of the image. Furthermore, we offer a semantic segmentation neural network for electron microscope image pre-processing, thereby expanding the applicability of our methodology.

Understanding Complex Electron Radiolysis in Saline Solution by Big Data Analysis

In this article, we developed a new method to analyze the complex chemical reactions induced by electron beam radiolysis based on big data analysis. At first, we built an element transport network to show the chemical reactions. Furthermore, the linearity between the species was quantified by Pearson correlation coefficient analysis. Based on the analysis, the mechanism of the high linearity between the special species pairs was interpreted by the element transport roadmap and chemical equations. The time variation of the pH of the solution and bubble formation in the solution were analyzed by simulation and data analysis. The simulation indicates that O₂ and H₂ can easily oversaturate and form bubbles. Finally, the radiolysis of high-energy electrons in pure water was analyzed as a reference for the radiolysis of high-energy electrons in saline solution. This work provides a new method for investigating a high-energy electron radiolysis process and for simplifying a complex chemical reaction based on quantitative analysis of the species variation in the reaction.

Optical properties of amorphous carbon determined by reflection electron energy loss spectroscopy spectra

We present the combined experimental and theoretical investigations of the optical properties of amorphous carbon. The reflection electron energy loss spectra (REELS) spectra of carbon were measured using a cylindrical mirror analyzer under ultrahigh vacuum conditions at primary electron energies of 750, 1000 and 1300 eV. The energy loss function and thereby the refractive index n and the extinction coefficient k were determined from these REELS spectra in a wide loss energy range of 2-200 eV by applying our reverse Monte Carlo method. The high accuracy of the obtained optical constants is justified with the ps- and f-sum rules. We found that our present optical constants of amorphous carbon fulfill the sum rules with the highest accuracy compared with the previously published data. Therefore, we highly recommend to replace the previous data with the present ones for practical applications. Moreover, we present the atomic scattering factors of amorphous carbon obtained from the dielectric function to predict its optical constants at a given density.

Charging effect induced by electron beam irradiation: a review

Charging effect frequently occurs when characterizing nonconductive materials using electrons as probes and/or signals and can impede the acquisition of useful information about the material under investigation. It is not adequate to investigate it merely by experiments, but theoretical investigations, for which the Monte Carlo method is a suitable tool, are also necessary. In this paper we review Monte Carlo simulations and selected experiments, intending to provide general insight into the charging effects induced by electron beam irradiation. We will introduce categories of the charging effect, the theoretical framework that is adopted in Monte Carlo modeling of the charging effect and present some typical simulation results. At last, with the knowledge on charging effect imparted by the above contents, we will discuss the measures that can be used for minimizing it.

Particle Size Effect of Oyster Shell on Mortar: Experimental Investigation and Modeling

In order to solve the problem of lack of natural river sand, crushed waste oyster shells (WOS) were used to replace river sand. By replacing 20% river sand, WOS mortar with different particle sizes of WOS were made for the experiment. Through experimental observation, the initial slump and slump flow loss rate were studied. The effects of different particle sizes and curing times on the compressive strength, flexural strength, static elastic modulus, and dry shrinkage of WOS mortar were analyzed. The relationship formulas between the compressive strength, flexural strength, particle size, and curing age were proposed. The results showed that the setting time and slump flow decreased with a decrease in the particle size of WOS. It was also found that the mortar with fine crushed WOS had high compressive strength, flexural strength, and static elastic modulus at both early and long-term curing age. A formula was proposed to describe the development of the compressive strength with the particle size of WOS and curing time, and the relations among these mechanical properties were discussed. Furthermore, drying shrinkage increased when WOS was used and could not satisfy the standard requirement of 0.075%. In contrast, the addition of fine WOS and double-dose sulfonated naphthalene-formaldehyde superplasticizer (SNF SP) reduced the shrinkage rate of the mortar by 8.35% and provided better workability and mechanical properties for mortar.

Mechanomics Biomarker for Cancer Cells Unidentifiable through Morphology and Elastic Modulus

Cellular mechanical properties are potential cancer biomarkers used for objective cytology to replace the current subjective method relying on cytomorphology. However, heterogeneity among intra/intercellular mechanics and the interplay between cytoskeletal prestress and elastic modulus obscured the difference detectable between malignant and benign cells. In this work, we collected high density nanoscale prestress and elastic modulus data from a single cell by AFM indentation to generate a cellular mechanome. Such high dimensional mechanome data was used to train a malignancy classifier through machine learning. The classifier was tested on 340 single cells of various origins, malignancy, and degrees of similarity in morphology and elastic modulus. The classifier showed instrument-independent robustness and classification accuracy of 89% with an AUC-ROC value of 93%. A signal-to-noise ratio 8 times that of the human-cytologist-based morphological method was also demonstrated, in differentiating precancerous hyperplasia cells from normal cells derived from the same lung cancer patient.

Ensemble machine learning methods: predicting electron stopping powers from a small experimental database

Electron stopping power (SP) is of great importance in theoretical and applied research areas specifically for Monte Carlo simulation studies in many microanalysis and surface analysis techniques, radiation dosimetry, and the design of particle detectors. However, experimental data are available for a dozen elemental materials only. On the other hand, the Bethe analytical expression of the SP is applicable at high energies only whereas no generally accepted formula exists at lower energies. We employed ensemble machine learning (ML) methods with the available experimental database for the prediction of SPs of electrons with energies from 100 keV down to 1 eV, in elements over the entire periodic table. With a small training database for electron SPs, we applied various algorithms individually as well as their ensembles, which have the credibility to enhance the prediction accuracy in the case of a small training database. Based on the model's performance evaluation tests, we concluded that the stacked generalization is more accurate than the individual algorithms. Using this method, we were able to predict the electron SPs for 54 elements (in total) including 12 elements that were present in the training database as well as for 42 elements beyond the training database over a wide energy range (1 eV to 100 keV). Compared to other theoretical approaches, the ML predicted SPs show very good agreement with the available experimental data at all energies. Moreover, unlike other theoretical approaches, the ML model does not need dielectric function data and other physical parameters which involve complex calculations. Using our ML model, we have predicted SPs for a further 14 elements for which no theoretical SPs are available because of the lack of good dielectric function data.

Rational Design of Nanoporous MoS2 /VS2 Heteroarchitecture for Ultrahigh Performance Ammonia Sensors

2D transition metal dichalcogenides (TMDs) have received widespread interest by virtue of their excellent electrical, optical, and electrochemical characteristics. Recent studies on TMDs have revealed their versatile utilization as electrocatalysts, supercapacitors, battery materials, and sensors, etc. In this study, MoS₂ nanosheets are successfully assembled on the porous VS₂ (P-VS₂ ) scaffold to form a MoS₂ /VS₂ heterostructure. Their gas-sensing features, such as sensitivity and selectivity, are investigated by using a quartz crystal microbalance (QCM) technique. The QCM results and density functional theory (DFT) calculations reveal the impressive affinity of the MoS₂ /VS₂ heterostructure sensor toward ammonia with a higher adsorption uptake than the pristine MoS₂ or P-VS₂ sensor. Furthermore, the adsorption kinetics of the MoS₂ /VS₂ heterostructure sensor toward ammonia follow the pseudo-first-order kinetics model. The excellent sensing features of the MoS₂ /VS₂ heterostructure render it attractive for high-performance ammonia sensors in diverse applications.

A comparative study on Monte Carlo simulations of electron emission from liquid water

PURPOSE

Liquid water being the major constituent of the human body, is of fundamental importance in radiobiological research. Hence, the knowledge of electron-water interaction physics and particularly the secondary electron yield is essential. However, to date, only very little is known experimentally on the low energy electron interaction with liquid water because of certain practical limitations. The purpose of this study was to gain some useful information about electron emission from water using a Monte Carlo (MC) simulation technique that can numerically model electron transport trajectories in water.

METHODS

In this study, we have performed MC simulations of electron emission from liquid water in the primary energy range of 50 eV-30 keV by using two different codes, i.e., a classical trajectory MC (CMC) code developed in our laboratory and the Geant4-DNA (G4DNA) code. The calculated secondary electron yield and electron backscattering coefficient are compared with experimental results wherever applicable to verify the validity of physical models for the electron-water interaction.

RESULTS

The secondary electron yield vs. primary energy curves calculated using the two codes present the same generic curve shape as that of metals but in rather different absolute values. G4DNA underestimates the secondary electron yield due to the application of one step thermalization model by setting a cutoff energy at 10 eV so that the low energy losses due to phonon excitations are omitted. Our CMC code, using a full energy loss spectrum to model electron inelastic scattering, allows the simulation of individual phonon scattering events for very low energy losses down to 10 meV, which then enables the calculated secondary electron yields much closer to the experimental data and also gives quite reasonable energy distribution curve of secondary electrons.

CONCLUSIONS

It is concluded that full dielectric function data at low energy loss values below 10 eV are recommended for modeling of low energy electrons in liquid water.

Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework

The TPP-2M formula is the most popular empirical formula for the estimation of the electron inelastic mean free paths (IMFPs) in solids from several simple material parameters. The TPP-2M formula, however, poorly describes several materials because it relies heavily on the traditional least-squares analysis. Herein, we propose a new framework based on machine learning to overcome the weakness. This framework allows a selection from an enormous number of combined terms (descriptors) to build a new formula that describes the electron IMFPs. The resulting framework not only provides higher average accuracy and stability but also reveals the physics meanings of several newly found descriptors. Using the identified principle descriptors, a complete physics picture of electron IMFPs is obtained, including both single and collective electron behaviors of inelastic scattering. Our findings suggest that machine learning is robust and efficient to predict the IMFP and has great potential in building a regression framework for data-driven problems. Furthermore, this method could be applicable to find empirical formula for given experimental data using a series of parameters given a priori, holds potential to find a deeper connection between experimental data and a priori parameters.

Observation of Plasmon Energy Gain for Emitted Secondary Electron in Vacuo

Plasmon gain by core-level electrons or elastic electrons observed in past studies seems to be of no practical value in material characterization, mainly because of their ultralow signal intensities. Nevertheless, in the emission spectra of Au samples, we have observed plasmon gain in secondary electrons. The electrons gain energy from surface plasmons after escaping from the surface and thereby only carry surface-plasmon information in the vacuum above the surface. Because the intensity of the emitted SEs is strong, rivaling that of core-level or elastic electrons, the observed phenomenon has in practice the potential to image directly in space the surface plasmon near but exterior to the metal surface.

Coupling of Surface Plasmon Modes and Refractive Index Sensitivity of Hollow Silver Nanoprism

Localized surface plasmon (LSP) modes depend strongly on the morphology of nanoparticle and the surrounding dielectric medium. The hollow nanostructure provides a new way to modulate the surface plasmon modes due to the additional cavity surface. In this work, we study systematically the multipolar surface plasmon modes of hollow silver nanoprism (HSN) by simulation of electron energy loss spectroscopy (EELS) spectra based on the boundary element method (BEM). Herein the effects of the cavity size and position are taken into account. The LSP modes of HSNs are compared with those of perfect silver nanoprism (SN). The red-shift behaviors of multipolar modes can be found as increasing the cavity size. Modes A and C have similar red-shift tendency and obey the plasmon ruler equation, which can be explained by dipole-dipole coupling mode. Meanwhile, the degenerate modes will be split by changing the cavity position, and opposite shift tendencies of split degenerate states are observed. These are caused by different coupling nature of degenerate modes. Moreover, high refractive index sensitivity (RIS) can be obtained for HSN by changing the cavity size and position.

Growth of Black Phosphorus Nanobelts and Microbelts

Black phosphorus nanobelts are fabricated with a one-step solid-liquid-solid reaction method under ambient pressure, where red phosphorus is used as the precursor instead of white phosphorus. The thickness of the as-fabricated nanobelts ranges from micrometers to tens of nanometers as studied by scanning electron microscopy. Energy dispersive X-ray spectroscopy and X-ray diffraction indicate that the nanobelts have the composition and the structure of black phosphorus, transmission electron microscopy reveals a typical layered structure stacked along the b-axis, and scanning transmission electron microscopy with energy dispersive X-ray spectroscopy analysis demonstrates the doping of bismuth into the black phosphorus structure. The nanobelt can be directly measured in scanning tunneling microscopy in ambient conditions.

Calculations of Electron Inelastic Mean Free Paths. XI. Data for Liquid Water for Energies from 50 eV to 30 keV

We calculated electron inelastic mean free paths (IMFPs) for liquid water from its optical energy-loss function (ELF) for electron energies from 50 eV to 30 keV. These calculations were made with the relativistic full Penn algorithm (FPA) that has been used for previous IMFP and electron stopping-power calculations for many elemental solids. We also calculated IMFPs of water with three additional algorithms: the relativistic single-pole approximation (SPA), the relativistic simplified SPA, and the relativistic extended Mermin method. These calculations were made using the same optical ELF in order to assess any differences of the IMFPs arising from choice of the algorithm. We found good agreement among the IMFPs from the four algorithms for energies over 300 eV. For energies less than 100 eV, however, large differences became apparent. IMFPs from the relativistic TPP-2M equation for predicting IMFPs were in good agreement with IMFPs from the four algorithms for energies between 300 eV and 30 keV but there was poorer agreement for lower energies. We calculated values of the static structure factor as a function of momentum transfer from the FPA. The resulting values were in good agreement with results from first-principles calculations and with inelastic X-ray scattering spectroscopy experiments. We made comparisons of our IMFPs with earlier calculations from authors who had used different algorithms and different ELF data sets. IMFP differences could then be analyzed in terms of the algorithms and the data sets. Finally, we compared our IMFPs with measurements of IMFPs and of a related quantity, the effective attenuation length (EAL). There were large variations in the measured IMFPs and EALs (as well as their dependence on electron energy). Further measurements are therefore required to establish consistent data sets and for more detailed comparisons with calculated IMFPs.

Quantum-trajectory Monte Carlo method for study of electron–crystal interaction in STEM

A quantum trajectory Monte Carlo method is developed to simulate electron scattering and secondary electron cascade process in crystalline specimen.

Extended Mermin method for calculating the electron inelastic mean free path

We propose an improved method for calculating electron inelastic mean free paths (IMFPs) in solids from experimental energy-loss functions based on the Mermin dielectric function. The "extended Mermin" method employs a nonlimited number of Mermin oscillators and allows negative oscillators to take into account not only electronic transitions, as is common in the traditional approaches, but also infrared transitions and inner shell electron excitations. The use of only Mermin oscillators naturally preserves two important sum rules when extending to infinite momentum transfer. Excellent agreement is found between calculated IMFPs for Cu and experimental measurements from elastic peak electron spectroscopy. Notably improved fits to the IMFPs derived from analyses of x-ray absorption fine structure measurements for Cu and Mo illustrate the importance of the contribution of infrared transitions in IMFP calculations at low energies.

Validity of the semi-classical approach for calculation of the surface excitation parameter

The problem of surface plasmon excitation by moving charges has been elaborated by several different approaches, mainly based on dielectric response theory within either semi-classical or quantum mechanical frameworks. In this work, a comparison of the surface excitation effect between two different frameworks is made by calculation of the differential inverse inelastic mean free path (DIIMFP) and a Monte Carlo simulation of reflection electron energy loss spectroscopy (REELS) spectra. A semi-classical modeling of the interaction between electrons and a solid surface is based on analyzing the work done by moving electrons; the stopping power and inelastic cross section are derived with the induced potential. On the other hand, a quantum mechanical approach is based on derivation of the complex inhomogeneous self-energy of the electrons. The numerical calculation shows that the semi-classical model presents almost the same values of DIIMFP as by the quantum model except at the glancing condition. The simulation of REELS spectra for Ag and SiO(2) as well as a comparison with experimental spectra also confirms that a good agreement with the spectral shape is found among the two simulation results and the experimental data.

N'Dribala (Cochlospermum planchonii) versus chloroquine for treatment of uncomplicated Plasmodium falciparum malaria

The aim of this work was to assess the efficacy of oral N'Dribala (tuberous roots decoction of Cochlospermum planchonii Hook) treatment versus chloroquine in non-severe malaria. The study included 85 patients with uncomplicated Plasmodium falciparum infection in Banfora, Burkina Faso. Forty-six patients that received N'Dribala beverage were compared to 21 patients treated with chloroquine. All patients were monitored with clinical examination and a parasitemia control by Giemsa-stained thick films. N'Dribala appeared safe and statistically as efficient as chloroquine for the treatment of uncomplicated Plasmodium falciparum malaria. At day 5 (D5), 57% of chloroquine-treated and 52% of N'Dribala-treated patients were cured with no detectable parasitemia (parasite density (Pd): 0) and more than 90% of whole patients were asymptomatic. N'Dribala is easily available in this country, cheap, without significant side effects and efficient with a clearly demonstrated activity on Plasmodium falciparum blood stages. This study enhances the traditional use of the Cochlospermum planchonii as alternative therapy for treatment of non-severe malaria.

[Effect of transforming growth factor beta 2 on phagocytosis in cultured bovine trabecular meshwork cells]

AIM

This study was performed to investigate the effect of transforming growth factor beta 2 (TGF-beta 2) on phagocytosis in bovine trabecular meshwork cells in vitro.

METHODS

After cultured bovine trabecular meshwork cells were treated for 24 h with 0 ng/ml (control), 0.32 ng/ml, 1 ng/ml, and 3.2 ng/ml TGF-beta 2, latex beads were added to the incubation medium, and the numbers of latex beads in 20 adjacent cells were then counted under a microscope after treatment with Wright's stain.

RESULTS

The average numbers of latex beads in the trabecular meshwork cells treated with TGF-beta 2 of different concentrations were 53.1+/-1.7 beads/cell, 56.4+/-2.9 beads/cell, and 77.9+/-6.5 beads/cell, respectively, compared to 45.5+/-3.3 beads/cell in the nontreated control group. Thus, TGF-beta 2 significantly increased the numbers of latex beads phagocytosed by cultured bovine trabecular meshwork cells in a dose-dependent manner.

CONCLUSION

TGF-beta 2 can promote the phagocytosis of bovine trabecular meshwork cells in vitro. It may be involved in the reduced cellularity of the trabecular meshwork in patients with primary open angle glaucoma by promoting the phagocytosis of these cells.

[Insulin-like growth factor 1 (IGF-1) mRNA and IGF-1 protein. Expression in cells of the trabecular meshwork of the bovine eye]

BACKGROUND

The purpose of the study was to determine whether cultured bovine trabecular meshwork cells and trabecular tissue ex vivo express insulin-like growth factor-1 (IGF-1) mRNA and protein.

METHODS

The reverse transcriptase-polymerase chain reaction (RT-PCR) was used for detection of IGF-1 mRNA. To detect the protein on the cells an IGF-1-specific immunohistochemical stain was used on trabecular meshwork cells.

RESULTS

A single 240 bp RT-PCR product was obtained, the RT-PCR product was verified by sequencing and the derived sequence was homologous to the known bovine sequence. IGF-1 immunostaining was positive in the cytoplasm of trabecular meshwork cells.

CONCLUSIONS

We conclude that trabecular meshwork cells produce IGF-1 mRNA and contribute to the presence of IGF-1 protein in the trabecular meshwork microenvironment as well as aqueous humor. Trabecular meshwork cells were affected by IGF-1 not only through paracrine but also through autocrine action. Whether regulations in IGF-1 production may contribute to the pathogenesis of primary open-angle glaucoma and the possibility of promoting the autocrine action of IGF-1 by trabecular meshwork cells to treat the disease is worth further investigation.

Effect of transforming growth factor-beta 2 on phagocytosis in cultured bovine trabecular meshwork cells

The effect of transforming growth factor-beta 2 (TGF-beta 2) on phagocytosis in bovine trabecular meshwork cells in vitro was investigated. After the cultured bovine trabecular meshwork cells were treated with 0 ng/ml, 0.32 ng/ml, 1 ng/ml, 3.2 ng/ml TGF-beta 2 for 24 h, latex beads were added into the incubation medium, and the numbers of the latex beads in 20 adjacent cells were counted under a microscope 24 h later, after treatment with Wright's stain. Our results showed that the average numbers of the latex beads in the trabecular meshwork cells treated with TGF-beta 2 of different concentrations were 53.1 +/- 1.7 beads/cell, 56.4 +/- 2.9 beads/cell and 77.9 +/- 6.5 beads/cell respectively, in comparison with 45.5 +/- 3.3 beads/cell of the control group. TGF-beta 2 significantly increased the number of the latex beads phagocytosed by cultured bovine trabecular meshwork cells in a dose-dependent manner. TGF-beta 2 could promote the phagocytosis of bovine trabecular meshwork cells in vitro. It may be involved in the cellularity decrease of the trabecular meshwork in the patients of primary open angle glaucoma through promoting the phagocytosis of trabecular meshwork cells.