Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review.

Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.

Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories.

South African legislation regulates the classification/labelling and compositional specifications of raw beef patties, to combat processed meat fraud and to protect the consumer. A near-infrared hyperspectral imaging (NIR-HSI) system was investigated as an alternative authentication technique to the current destructive, time-consuming, labour-intensive and expensive methods. Eight hundred beef patties (ca. 100 g) were made and analysed to assess the potential of NIR-HSI to distinguish between the four patty categories (200 patties per category): premium 'ground patty'; regular 'burger patty'; 'value-burger/patty' and the 'econo-burger'/'budget'. Hyperspectral images were acquired with a HySpex SWIR-384 (short-wave infrared) imaging system using the Breeze® acquisition software, in the wavelength range of 952-2517 nm, after which the data was analysed using image analysis, multivariate techniques and machine learning algorithms. It was possible to distinguish between the four patty categories with accuracies ≥97%, indicating that NIR-HSI offers an accurate and reliable solution for the rapid identification and authentication of processed beef patties. Furthermore, this study has the potential of providing an alternative to the current authentication methods, thus contributing to the authenticity and fair-trade of processed meat products locally and internationally.

The Imperative to Reimagine Assisted Living.

Assisted living (AL) has existed in the United States for decades, evolving in response to older adults' need for supportive care and distaste for nursing homes and older models of congregate care. AL is state-regulated, provides at least 2 meals a day, around-the-clock supervision, and help with personal care, but is not licensed as a nursing home. The key constructs of AL as originally conceived were to provide person-centered care and promote quality of life through supportive and responsive services to meet scheduled and unscheduled needs for assistance, an operating philosophy emphasizing resident choice, and a residential environment with homelike features. As AL has expanded to constitute half of all long-term care beds, the increasing involvement of the real estate, hospitality, and health care sectors has raised concerns about the variability of AL, the quality of AL, and standards for AL. Although the intent to promote person-centered care and quality of life has remained, those key constructs have become mired under tensions related to models of AL, regulation, financing, resident acuity, and the workforce. These tensions have resulted in a model of care that is not as intended, and which must be reimagined if it is to be an affordable care option truly providing quality, person-centered care in a suitable environment. Toward that end, 25 stakeholders representing diverse perspectives conferred during 2 half-day retreats to identify the key tensions in AL and discuss potential solutions. This article presents the background regarding those tensions, as well as potential solutions that have been borne out, paving the path to a better future of assisted living.

Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud.

In recent years, meat authenticity awareness has increased and, in the fight to combat meat fraud, various analytical methods have been proposed and subsequently evaluated. Although these methods have shown the potential to detect low levels of adulteration with high reliability, they are destructive, time-consuming, labour-intensive, and expensive. Therefore, rendering them inappropriate for rapid analysis and early detection, particularly under the fast-paced production and processing environment of the meat industry. However, modern analytical methods could improve this process as the food industry moves towards methods that are non-destructive, non-invasive, simple, and on-line. This review investigates the feasibility of different non-destructive techniques used for processed meat authentication which could provide the meat industry with reliable and accurate real-time monitoring, in the near future.

Application of ANOVA-simultaneous component analysis to quantify and characterise effects of age, temperature, syrup adulteration and irradiation on near-infrared (NIR) spectral data of honey.

NIR spectroscopy combined with chemometric analysis has proven to be a rapid and cost-effective screening tool for the detection of syrup-adulterated honey. Processing and storage conditions which alter the chemical and physical state of honey may affect the spectra. The effects of age, storage temperature, syrup adulteration (10 and 20% w/w) and irradiation treatment on the NIR spectra of honey were investigated as a function of time with ANOVA-simultaneous component analysis (ASCA), an experimental design-focused exploratory data analysis method. The factors 'time', 'temperature' and 'adulteration' were found to have significant effects (p < 0.05), but no significant effect was observed for irradiation treatment. A significant interaction effect was found between factors 'time' and 'adulteration', with the greatest disparity between authentic and adulterated class signals found immediately after adulteration and decreasing within three months thereafter.

Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling.

Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908-1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%-93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%-100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy's potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination.

Near infrared hyperspectral imaging in quality and safety evaluation of cereals.

The requirements of cereal research, as well as grading and evaluation of food products, have encouraged the development of nondestructive, rapid, and accurate analytical techniques to evaluate grain quality and safety. NIR hyperspectral imaging integrates spectroscopy and imaging techniques in one analytical system, allowing direct identification of chemical components and their distribution within the sample. It is a promising technique that may be implemented on-line, enabling the cereal industry to move away from subjective, manual classification and measuring methods. NIR hyperspectral imaging has gained popularity for rapidly acquiring information to enable the quantification, identification or differentiation of a variety of cereal properties. The technique can potentially replace multiple conventional chemical, microbial or physical tests with a single, automated image acquisition. Individual kernels can be analyzed nondestructively, enabling one to follow changes in the same kernel over time (e.g. fungal development). Although NIR hyperspectral imaging has not been extensively implemented in industry, it shows great potential for the development of an evaluation system to assess cereal grains, especially regarding variety discrimination and grading/classification properties. This review outlines the theory and principles of NIR hyperspectral imaging, and focuses specifically on its application in cereal science research and industry.

Pharmacokinetic and Pharmacodynamic Analyses To Determine the Optimal Fixed Dosing Regimen of Iclaprim for Treatment of Patients with Serious Infections Caused by Gram-Positive Pathogens.

Iclaprim is a bacterial dihydrofolate reductase inhibitor that is currently being evaluated in two phase 3 trials for the treatment of patients with acute bacterial skin and skin structure infections (ABSSSI). Prior animal infection model studies suggest that the pharmacokinetic/pharmacodynamic (PK/PD) drivers for efficacy are area under the concentration-time curve from 0 to 24 h at steady state (AUC0-24ss), AUC/MIC, and time above the MIC during the dosing interval (T > MIC), while QTc prolongation was associated with the maximal concentration at steady state (Cmaxss) in a thorough QTc phase 1 study. Using PK data collected from 470 patients from the previously conducted phase 3 complicated skin and skin structure infection (cSSSI) trials, population PK modeling and Monte Carlo simulation (MCS) were used to identify a fixed iclaprim dosage regimen for the ongoing phase 3 ABSSSI studies that maximizes AUC0-24ss, AUC/MIC, and T > MIC while minimizing the probability of a Cmaxss of ≥800 ng/ml relative to the values for the previously employed cSSSI regimen of 0.8 mg/kg of body weight infused intravenously over 0.5 h every 12 h. The MCS analyses indicated that administration of 80 mg as a 2-h infusion every 12 h provides 28%, 28%, and 32% increases in AUC0-24ss, AUC/MIC, and T > MIC, respectively, compared to values for the 0.8-mg/kg cSSSI regimen, while decreasing the probability of a Cmaxss of ≥800 ng/ml, by 9%. Based on PK/PD analyses, 80 mg iclaprim administered over 2 h every 12 h was selected as the dosing scheme for subsequent phase 3 clinical trials.

Classification of white maize defects with multispectral imaging.

Multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to grade whole white maize kernels. The types of defective materials regarded in grading legislation were divided into 13 classes, and were imaged with a multispectral imaging instrument spanning the UV, visible and NIR regions (19 wavelengths ranging from 375 to 970nm). Object-wise partial least squares discriminant analysis (PLS-DA) models were developed and validated with an independent data set. Results demonstrated good performance in distinguishing between sound maize and undesirable materials, with cross-validated coefficients of determination (Q2) and classification accuracies ranging from 0.35 to 0.99 and 83 to 100%, respectively. Wavelengths related to absorbance of green, yellow and orange colour indicated the presence of lycopene and anthocyanin (505, 525, 570 and 590 nm). NIR wavelengths 890, 940 nm (associated with fat) and 970 nm (associated with water) were generally identified as important features throughout the study.

Untargeted analysis of chromatographic data for green and fermented rooibos: Problem with size effect removal.

While analyzing chromatographic data, it is necessary to preprocess it properly before exploration and/or supervised modeling. To make chromatographic signals comparable, it is crucial to remove the scaling effect, caused by differences in overall sample concentrations. One of the efficient methods of signal scaling is Probabilistic Quotient Normalization (PQN) [1]. However, it can be applied only to data for which the majority of features do not vary systematically among the studied classes of signals. When studying the influence of the traditional "fermentation" (oxidation) process on the concentration of 56 individual peaks detected in rooibos plant material, this assumption is not fulfilled. In this case, the only possible solution is the analysis of pairwise log-ratios, which are not influenced by the scaling constant. To estimate significant features, i.e., peaks differentiating the studied classes of samples (green and fermented rooibos plant material), we propose the application of rPLR (robust pair-wise log-ratios) as proposed by Walach et al. [2]. It allows for fast computation and identification of the significant features in terms of original variables (peaks) which is problematic, while working with the unfolded pair-wise log ratios. As demonstrated, it can be applied to designed data sets and in the case of contaminated data, it allows proper conclusions.

Non-destructive techniques for the detection of fungal infection in cereal grains.

Infection of cereal grains by fungi is a serious problem worldwide. Depending on the environmental conditions, cereal grains may be colonised by different species of fungi. These fungi cause reduction in yield, quality and nutritional value of the grain; and of major concern is their production of mycotoxins which are harmful to both humans and animals. Early detection of fungal contamination is an essential control measure for ensuring storage longevity and food safety. Conventional methods for detection of fungal infection, such as culture and colony techniques or immunological methods are either slow, labour intensive or difficult to automate. In recent years, there has been an increasing need to develop simple, rapid, non-destructive methods for early detection of fungal infection and mycotoxins contamination in cereal grains. Methods such as near infrared (NIR) spectroscopy, NIR hyperspectral imaging, and electronic nose were evaluated for these purposes. This paper reviews the different non-destructive techniques that have been considered thus far for detection of fungal infection and mycotoxins in cereal grains, including their principles, application and limitations.

Differentiation of foodborne bacteria using NIR hyperspectral imaging and multivariate data analysis.

The potential for near-infrared (NIR) hyperspectral imaging and multivariate data analysis to be used as a rapid non-destructive tool for detection and differentiation of bacteria was investigated. NIR hyperspectral images were collected of Bacillus cereus, Escherichia coli, Salmonella enteritidis, Staphylococcus aureus and Staphylococcus epidermidis grown on agar for 20 h at 37 °C. Principal component analysis (PCA) was applied to mean-centred data. Standard normal variate (SNV) correction and the Savitzky-Golay technique was applied (2nd derivative, 3rd-order polynomial; 25 point smoothing) to wavelengths in the range of 1103 to 2471 nm. Chemical differences between colonies which appeared similar in colour on growth media (B. cereus, E. coli and S. enteritidis.) were evident in the PCA score plots. It was possible to distinguish B. cereus from E. coli and S. enteritidis along PC1 (59 % sum of squares (SS)) and between E. coli and S. enteritidis in the direction of PC2 (6.85 % SS). S. epidermidis was separated from B. cereus and S. aureus along PC1 (37.5 % SS) and was attributed to variation in amino acid and carbohydrate content. Two clusters were evident in the PC1 vs. PC2 PCA score plot for the images of S. aureus and S. epidermidis, thus permitting distinction between species. Differentiation between genera (similarly coloured on growth media), Gram-positive and Gram-negative bacteria and pathogenic and non-pathogenic species was possible using NIR hyperspectral imaging. Partial least squares discriminant analysis (PLS-DA) models were used to confirm the PCA data. The best predictions were made for B. cereus and Staphylococcus species, where results ranged from 82.0 to 99.96 % correctly predicted pixels.

Classification of maize kernels using NIR hyperspectral imaging.

NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual kernels and did not give acceptable results because of high misclassification. However by using a predefined threshold and classifying entire kernels based on the number of correctly predicted pixels, improved results were achieved (sensitivity and specificity of 0.75 and 0.97). Object-wise classification was performed using two methods for feature extraction - score histograms and mean spectra. The model based on score histograms performed better for hard kernel classification (sensitivity and specificity of 0.93 and 0.97), while that of mean spectra gave better results for medium kernels (sensitivity and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale.

A dominant-negative F-box deleted mutant of E3 ubiquitin ligase, ß-TrCP1/FWD1, markedly reduces myeloma cell growth and survival in mice.

Treatment of multiple myeloma with bortezomib can result in severe adverse effects, necessitating the development of targeted inhibitors of the proteasome. We show that stable expression of a dominant-negative F-box deleted (∆F) mutant of the E3 ubiquitin ligase, SCFß-TrCP/FWD1, in murine 5TGM1 myeloma cells dramatically attenuated their skeletal engraftment and survival when inoculated into immunocompetent C57BL/KaLwRij mice. Similar results were obtained in immunodeficient bg-nu-xid mice, suggesting that the observed effects were independent of host recipient immune status. Bone marrow stroma offered no protection for 5TGM1-∆F cells in cocultures treated with tumor necrosis factor (TNF), indicating a cell-autonomous anti-myeloma effect. Levels of p100, IκBα, Mcl-1, ATF4, total and cleaved caspase-3, and phospho-ß-catenin were elevated in 5TGM1-∆F cells whereas cIAP was down-regulated. TNF also activated caspase-3 and downregulated Bcl-2, correlating with the enhanced susceptibility of 5TGM1-∆F cells to apoptosis. Treatment of 5TGM1 tumor-bearing mice with a ß-TrCP1/FWD1 inhibitor, pyrrolidine dithiocarbamate (PDTC), significantly reduced tumor burden in bone. PDTC also increased levels of cleaved Mcl-1 and caspase-3 in U266 human myeloma cells, correlating with our murine data and validating the development of specific ß-TrCP inhibitors as an alternative therapy to nonspecific proteasome inhibitors for myeloma patients.

DHA is a more potent inhibitor of breast cancer metastasis to bone and related osteolysis than EPA.

Breast cancer patients often develop bone metastasis evidenced by osteolytic lesions, leading to severe pain and bone fracture. Attenuation of breast cancer metastasis to bone and associated osteolysis by fish oil, rich in EPA and DHA, has been demonstrated previously. However, it was not known whether EPA and DHA differentially or similarly affect breast cancer bone metastasis and associated osteolysis. In vitro culture of parental and luciferase gene encoded MDA-MB-231 human breast cancer cell lines treated with EPA and DHA revealed that DHA inhibits proliferation and invasion of breast cancer cells more potently than EPA. Intra-cardiac injection of parental and luciferase gene encoded MDA-MB-231 cells to athymic NCr nu/nu mice demonstrated that DHA-treated mice had significantly less breast cancer cell burden in bone, and also significantly less osteolytic lesions than EPA-treated mice. In vivo cell migration assay as measured by luciferase intensity revealed that DHA attenuated cell migration specifically to the bone. Moreover, the DHA-treated group showed reduced levels of CD44 and TRAP positive area in bone compared to EPA-treated group. Breast cancer cell burden and osteolytic lesions were also examined in intra-tibially breast cancer cell injected mice and found less breast cancer cell growth and associated osteolysis in DHA-treated mice as compared to EPA-treated mice. Finally, doxorubicin-resistant MCF-7 (MCF-7dox) human breast cancer cell line was used to examine if DHA can improve sensitization of MCF-7dox cells to doxorubicin. DHA improved the inhibitory effect of doxorubicin on proliferation and invasion of MCF-7dox cells. Interestingly, drug resistance gene P-gp was also down-regulated in DHA plus doxorubicin-treated cells. In conclusion, DHA attenuates breast cancer bone metastasis and associated osteolysis more potently than EPA, possibly by inhibiting migration of breast cancer cell to the bone as well as by inhibiting osteoclastic bone resorption.

Concentrated fish oil (Lovaza(R)) extends lifespan and attenuates kidney disease in lupus-prone short-lived (NZBxNZW)F1 mice.

A growing number of reports indicate that anti-inflammatory actions of fish oil (FO) are beneficial against systemic lupus erythematosus (SLE). However, the majority of pre-clinical studies were performed using 5-20% FO, which is higher than the clinically relevant dose for lupus patients. The present study was performed in order to determine the effective low dose of FDA-approved concentrated FO (Lovaza®) compared to the commonly used FO-18/12 (18-Eicosapentaenoic acid [EPA]/12-Docosahexaenoic acid [DHA]). We examined the dose-dependent response of Lovaza® (1% and 4%) on an SLE mouse strain (NZBxNZW)F1 and compared the same with 1% and 4% placebo, as well as 4% FO-18/12, maintaining standard chow as the control. Results show for the first time that 1% Lovaza® extends maximal lifespan (517 d) and 4% Lovaza® significantly extends both the median (502 d) and maximal (600 d) life span of (NZBxNZW)F1 mice. In contrast, FO-18/12 extends only median lifespan (410 d) compared to standard chow diet (301 d). Additionally, 4% Lovaza® significantly decreased anti-dsDNA antibodies, reduced glomerulonephritis and attenuated lipopolysaccharide-induced pro-inflammatory cytokines (IL-1ß, IL-6, TNF-α) in splenocytes compared to placebo. 4% Lovaza® was also shown to reduce the expression of inflammatory cytokines, including IL-1ß, IL-6 and TNF-α, while increasing renal anti-oxidant enzymes in comparison to placebo. Notably, NFκB activation and p65 nuclear translocation were lowered by 4% Lovaza® compared to placebo. These data indicate that 1% Lovaza® is beneficial, but 4% Lovaza® is more effective in suppressing glomerulonephritis and extending life span of SLE-prone short-lived mice, possibly via reducing inflammation signaling and modulating oxidative stress.

Dietary coral calcium and zeolite protects bone in a mouse model for postmenopausal bone loss.

In patients diagnosed with osteoporosis, calcium is lost from bones making them weaker and easily susceptible to fractures. Supplementation of calcium is highly recommended for such conditions. However, the source of calcium plays an important role in the amount of calcium that is assimilated into bone. We hypothesize that naturally occurring coral calcium and zeolite may prevent ovariectomy-induced bone loss. We have measured bone loss in ovariectomized mice supplemented with coral calcium and Zeolite. Female C57BL/6 mice were either sham-operated or ovariectomized and fed diets containing coral calcium or zeolite for 6 months. Serum was analyzed for bone biochemical markers and cytokines. Bones were analyzed using dual x-ray absorbtiometry, peripheral quantitative computed tomography, and micro-computed tomography densitometry. In the distal femoral metaphysis, total bone and cortical bone mass was restored and the endocortical surface was significantly decreased in coral calcium and zeolite fed ovariectomized (OVX) mice. Trabecular number and the ratio of bone volume to total volume was higher in OVX mice after coral calcium and zeolite feeding, while trabecular separation decreased in the different treatment OVX groups. Coral calcium protected bone to a lesser extent in the proximal tibia and lumbar vertebrae. Overall, coral calcium and zeolite may protect postmenopausal bone loss.

Growth characteristics of three Fusarium species evaluated by near-infrared hyperspectral imaging and multivariate image analysis.

Colony growth of three Fusarium spp. on potato dextrose agar was followed by collecting near-infrared (NIR) hyperspectral images of the colonies at regular intervals after inoculation up to 55 h. After principal component analysis (PCA), two clusters were apparent in the score plot along principal component 1. Using the brushing technique, these clusters were divided into four groups of pixels with similar score values. These could be visualised as growth zones within the colonies in the corresponding score image. Three spectral bands, i.e. 1,166, 1,380 and 1,918 nm, were prominent in the multiplicative scatter corrected and Savitzky-Golay second derivative spectra. These indicated chemical changes, associated with carbohydrates (1,166 and 1,380 nm) and protein (1,918 nm), that occurred as the mycelium grew and matured. The protein band was more prominent in the mature fungal material while the carbohydrate band was less pronounced. The younger material and the agar were characterised by the carbohydrate spectral band. Integrating whole mycelium colonies as the sum of pixels over time made it possible to construct curves that resembled growth curves; this included the lag phase, active growth phase, deceleration phase and phase of constant growth. Growth profiles constructed from individual growth zones indicated more detailed growth characteristics. The use of NIR hyperspectral imaging and multivariate image analysis (MIA) allowed one to visualise radial growth rings in the PCA score images. This would not have been possible with bulk spectroscopy. Interpreting spectral data enabled better understanding of microbial growth characteristics on agar medium. NIR hyperspectral imaging combined with MIA is a powerful tool for the evaluation of growth characteristics of fungi.

Near-infrared (NIR) hyperspectral imaging and multivariate image analysis to study growth characteristics and differences between species and strains of members of the genus Fusarium.

Near-infrared (NIR) hyperspectral imaging was used to study three strains of each of three Fusarium spp. (Fusarium subglutinans, Fusarium proliferatum and Fusarium verticillioides) inoculated on potato dextrose agar in Petri dishes after either 72 or 96 h of incubation. Multivariate image analysis was used for cleaning the images and for making principal component analysis (PCA) score plots and score images and local partial least squares discriminant analysis (PLS-DA) models. The score images, including all strains, showed how different the strains were from each other. Using classification gradients, it was possible to show the change in mycelium growth over time. Loading line plots for principal component (PC) 1 and PC2 explained variation between the different Fusarium spp. as scattering and chemical differences (protein production), respectively. PLS-DA prediction results (including only the most important strain of each species) showed that it was possible to discriminate between species with F. verticillioides the least correctly predicted (between 16 and 47 % pixels correctly predicted). For F. subglutinans, 78-100 % pixels were correctly predicted depending on the training and test sets used. Similarly, the percentage correctly predicted values of F. proliferatum were 60-80 %. Visualisation of the mycelium radial growth in the PCA score images was made possible due to the use of NIR hyperspectral imaging. This is not possible with bulk spectroscopy in the visible or NIR regions.

HDAC inhibitor trichostatin A suppresses osteoclastogenesis by upregulating the expression of C/EBP-ß and MKP-1.

Histone deacetylases (HDACs) remove the acetyl groups from the lysine residues of histone tails, leading to the formation of a condensed and transcriptionally silenced chromatin. HDAC inhibitors (HDACi) block this action and can result in hyperacetylation of histones, leading to a less compact and more transcriptionally active chromatin and thereby, gene expression. Previously, we have shown that HDACi inhibit osteoclast differentiation. However, which genes are transcriptionally activated following hyperacetylation of histones, and lead to the suppression of osteoclastogenesis, has yet to be elucidated. In this study, we show that an HDACi, trichostatin A (TSA), inhibits the receptor activator of the nuclear factor-κB (NF-κB) ligand (RANKL)-stimulated TNF-α production, NF-κB activation, and bone resorbing pit formation, and downregulates c-Fos and NFATc1 in RAW 264.7 cells. Interestingly, expression of antiosteoclastogenic factors CCAAT enhancer binding protein (C/EBP)-ß and mitogen-activated protein kinase phosphatase (MKP)-1 was significantly upregulated in TSA-treated, RANKL-stimulated RAW 264.7 cells. These findings suggest that TSA upregulates the expression of C/EBP-ß and MKP-1, which may downregulate pro-osteoclastogenic factors and signaling molecules, ultimately suppressing osteoclastogenesis.