This study investigated the effects of automated short-term incomplete milking (IM) during peak- to mid-lactation on milk production, udder health, and selected metabolic parameters in multiparous Holstein cows. Forty-six cows (94 ± 47 DIM) were randomly assigned to the complete milking treatment (COMP; n = 23, 95 ± 49 DIM, lactation number 3.0 ± 1.3) or the incomplete milking treatment (INCL; n = 23, 93 ± 46 DIM, lactation number 2.6 ± 0.8). In INCL cows, IM was implemented using specialized software that precisely controlled milk withdrawal. Concentrate allocation was dynamically adjusted based on milk yield and DIM via transponder-controlled stations. All cows had ad libitum access to a partial mixed ration and water via automated weighing troughs and bowls. The IM protocol included a gradual reduction phase (first IM phase: 9% daily reduction to ∼40% over 5.5 d), followed by 3 complete milkings, and a constant reduction phase (second IM phase: 40% reduction over 5.5 d). No clinical signs of mastitis or mastitis-relevant bacteria were observed, confirming udder health. In the week prior to IM, milk yield (reference milk yield, REF) did not differ between treatments, with INCL cows achieving between 26 and 59 kg/d. Compared to REF, the milk yield of the INCL cows decreased by 19% (32.7 ± 1.54 kg/d) on the first day after the first IM phase and by 24% (30.7 ± 1.54 kg/d) on the first day after the second IM phase. After cessation of IM, milk yield increased again by 19% within 2 wk and stabilized further at the REF level. Compared to the COMP cows, milk fat content of the INCL cows decreased by 21% and 43% with the onset of the first and second IM phases. Concentrate intake of INCL cows decreased by 65% from 5.4 ± 0.52 kg/d at the beginning of IM to 1.9 ± 0.39 kg/d at the end of IM due to the feed-to-yield feeding system, reflecting lower energy requirements, and increased thereafter. Udder health indicators remained stable during IM. In the INCL cows that completed the trial, SCC were consistently below mastitis thresholds and no relevant pathogens were detected. Milk leakage occurred exclusively in INCL cows during IM, but did not compromise udder health. Serum nonesterified fatty acid concentrations decreased by 63% and 58% during the first IM and second IM phases, respectively, indicating reduced lipid mobilization. Concurrently, IGF-1 concentrations increased by 30% and 28% during the respective phases, indicating metabolic adaptations to the reduced energy demand. In summary, automated short-term IM performed on dairy cows in peak- to mid-lactation using specialized software was successful in temporarily reducing milk production and lactation-induced metabolic load without negatively affecting udder health or long-term performance.

The introduction of precision dairy farming has led to increasing automation of core tasks in dairy farming. However, the impact of these technologies on animal welfare remains the subject of ongoing debate. A previous study using the Welfare Quality® (WQ) Assessment protocol to examine the impact of dairy farm automation on cattle welfare found an effect on animals’ behavior. While the WQ protocol is widely used to evaluate dairy cattle welfare, it is often criticized for subjectivity. Thus, more objective indicators are demanded. Concurrently, hair cortisol concentration (HCC) has emerged as a promising objective indicator of long-term stress in animals, offering a potential indirect welfare indicator. Thus, the present study aimed to investigate the relationship between farm automation levels and dairy cattle welfare, using HCC as a biomarker of animal stress. Furthermore, associations between HCC and WQ indicators are examined. Therefore, German farms (n = 32) were categorized into three automation levels based on a newly developed classification system. On each farm, welfare assessment was performed using the WQ protocol, and hair samples were collected from 15 cows to determine HCCs. Median HCC values were compared across automation levels using the non-parametric Kruskal–Wallis test. Associations between HCC and WQ indicators were examined using multiple linear regression analysis. A trend of lower HCC levels with increasing automation was observed. Even so, the differences were not statistically significant, likely due to substantial variabilities in housing, management, and settings of automatic systems such as individual milking intervals or frequencies of feeding, bedding, and so forth. among farms. Significant correlations were found between median HCC per farm and the WQ protocol indicators “percentage of moderately lame cows,” “cows with at least one hairless patch and no lesion,” “tendency to be apathetic,” and “absence of injuries.” However, these indicators are not recommended as standalone measures of welfare. Nevertheless, consistent with the existing literature, our findings support lameness and integument alterations as key indicators of poor welfare in dairy cattle, which was also reflected in elevated HCC levels. As the number of highly automated farms is expected to increase in upcoming years, future studies with larger sample sizes are recommended.

An increasing number of automation technologies for dairy cattle farming, including automatic milking, feeding, manure removal and bedding, are now commercially available. The effects of these technologies on individual aspects of animal welfare have already been explored to some extent. However, as of now, there are no studies that analyze the impact of increasing farm automation through various combinations of these technologies. The objective of this study was to examine potential correlations between welfare indicators from the Welfare Quality® Assessment protocol and dairy farms with varying degrees of automation. To achieve this, 32 trial farms in Northern and Central Germany were categorized into varying automation levels using a newly developed classification system. The Welfare Quality® Assessment protocol was used to conduct welfare assessments on all participating farms. Using analysis of variance (ANOVA), overall welfare scores and individual measures from the protocol were compared across farms with differing automation levels. No significant differences were observed in overall welfare scores, suggesting that the impact of automation does not exceed other farm-related factors influencing animal wellbeing, such as housing environment or management methods. However, significant effects of milking, feeding, and bedding systems on the appropriate behavior of cattle were observed. Higher levels of automation had a positive impact on the human–animal relationship and led to positive emotional states. Moreover, farms with higher automation levels had significantly lower scores for the prevalence of severe lameness and dirtiness of lower legs. It could be concluded that a higher degree of automation could help to improve animal welfare on dairy farms.

When implementing the transition from regular milk production to the dry period, drying off is mostly conducted simply by abrupt cessation of milking. Efforts to reduce milk synthesis before cessation of milking aim to reduce stress in cows as well as to lower the risk of mastitis. A previous study demonstrated that incomplete milking during the last ten days of lactation gradually reduced the milk yield of healthy, high-yielding cows. However, a reduction period of ten days might be too long for cows with lower yields. Therefore, a follow-up study was conducted on an organic dairy research farm with a lower average milk yield. We investigated whether automated incomplete milking can reduce milk synthesis within one week without impairing the health status of udder quarters infected with minor pathogens. Before drying off, 15 German-Holstein cows with 58 lactating quarters, 21 of which were infected with minor pathogens (coagulase-negative Staphylococci (CNS) and Corynebacterium bovis), were milked twice daily using the software module AutoDry (GEA Farm Technologies) with the 5%-step-down-per-day-program. The level of udder emptying was gradually reduced over 8-10 milkings beginning at a mean milk yield of 17.2 ± 4.4 kg d-1. During the last three milkings before drying off, milking clusters were automatically removed when a milk flow rate of 0.3 kg min-1 was reached. Quarter fore-milk samples were collected at two time points (14 d before and at the date of the last milking, ie drying off) and were analysed for mastitis pathogens and somatic cell count. The gradual reduced emptying of the udder induced a clear decline in milk yield by 0.8 ± 0.3 kg d-1. Within the reduction period, the somatic cell count of quarter foremilk did not change regardless of the infection status. Furthermore, no cow suffered from clinical mastitis. Thus, a reduction in milk synthesis could be achieved within less than one week before drying off without any impairment in udder health of cows, even when infected with minor mastitis pathogens.

Drying-off practices to reduce milk production before dry-off are gaining attention because high milk yields at dry-off are becoming more common and increase the risk to cow health and welfare during the dry period. Incomplete milking for the last days before dry-off is one approach for reducing milk production. We conducted an online survey to determine the currently used drying-off practices on Swiss dairy farms and to identify the adoption potential of integrating incomplete milking before dry-off. In March 2021, the online survey was sent to a representative sample of 1,974 Swiss dairy farmers. A total of 518 completed questionnaires were analyzed. The mean number of dairy cows per farm was 39 (range: 11-140 cows). Thirty-five percent of cows produced considerable quantities of milk (>15 kg/d) at dry-off, and milk yield at dry-off increased with increasing annual milk yield. Abrupt dry-off was applied on 45% of the farms. The participants reported observing behavioral changes of cows such as increased vocalizations and decreased lying time associated with dry-off. Selective dry cow therapy was applied on 74% of the farms, and 44% of the participants indicated the use of antibiotics at dry-off as being "rather often," "often," or "always." Correlation analysis revealed that with increasing annual milk yields, the frequency of observed behavioral changes and antibiotic use at dry-off increased as well. Therefore, drying-off approaches that reduce milk production while supporting cow welfare are needed. We found that farmers showed an interest in testing the presented drying-off approach of incomplete milking. In addition, the farmers indicated that they would be more willing to test incomplete milking before dry-off if it became available for automated use in milking parlors or robots. Uncertainties regarding udder health appeared to be the main barrier for the adoption potential of this approach.

Molecular spoked wheels with intraannular functionalizable pillars are synthesized in a modular approach. The functionalities at their ends are variable, and a propargyl alcohol, a [6,6]-phenyl-C61-butyrate, and a perylene monoimide are investigated. All compounds form two-dimensional crystals on highly oriented pyrolytic graphite at the solid-liquid interface. As determined by submolecularly resolved scanning tunneling microscopy, the pillars adopt equilibrium distances of 6.0 nm. The fullerene has a residual mobility, limited by the length of the flexible connector unit. The experimental results are supported and rationalized by molecular dynamics simulations. These also show that, in contrast, the more rigidly attached perylene monoimide units remain oriented along the surface normal and maintain a smallest distance of 2 nm above the graphite substrate. The robust packing concept also holds for cocrystals with molecular hexagons that expand the pillar-pillar distances by 15% and block unspecific intercalation.

The prediction of health disorders is the goal of many sensor systems in dairy farming. Although mastitis and lameness are the most common health disorders in dairy cows, these diseases or treatments are a rare event related to a single day and cow. A number of studies already developed and evaluated models for classifying cows in need of treatment for mastitis and lameness with machine learning methods, but few have illustrated the effects of the positive predictive value (PPV) on practical application. The objective of this study was to investigate the importance of low-frequency treatments of mastitis or lameness for the applicability of these classification models in practice. Data from three German dairy farms contained animal individual sensor data (milkings, activity, feed intake) and were classified using machine learning models developed in a previous study. Subsequently, different risk criteria (previous treatments, information from milk recording, early lactation) were designed to isolate high-risk groups. Restricting selection to cows with previous mastitis or hoof treatment achieved the highest increase in PPV from 0.07 to 0.20 and 0.15, respectively. However, the known low daily risk of a treatment per cow remains the critical factor that prevents the reduction of daily false-positive alarms to a satisfactory level. Sensor systems should be seen as additional decision-support aid to the farmers’ expert knowledge.

The aim of this study was to develop classification models for mastitis and lameness treatments in Holstein dairy cows as the target variables based on continuous data from herd management software with modern machine learning methods. Data was collected over a period of 40 months from a total of 167 different cows with daily individual sensor information containing milking parameters, pedometer activity, feed and water intake, and body weight (in the form of differently aggregated data) as well as the entered treatment data. To identify the most important predictors for mastitis and lameness treatments, respectively, Random Forest feature importance, Pearson's correlation and sequential forward feature selection were applied. With the selected predictors, various machine learning models such as Logistic Regression (LR), Support Vector Machine (SVM), K-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), Extra Trees Classifier (ET) and different ensemble methods such as Random Forest (RF) were trained. Their performance was compared using the receiver operator characteristic (ROC) area-under-curve (AUC), as well as sensitivity, block sensitivity and specificity. In addition, sampling methods were compared: Over- and undersampling as compensation for the expected unbalanced training data had a high impact on the ratio of sensitivity and specificity in the classification of the test data, but with regard to AUC, random oversampling and SMOTE (Synthetic Minority Over-sampling) even showed significantly lower values than with non-sampled data. The best model, ET, obtained a mean AUC of 0.79 for mastitis and 0.71 for lameness, respectively, based on testing data from practical conditions and is recommended by us for this type of data, but GNB, LR and RF were only marginally worse, and random oversampling and SMOTE even showed significantly lower values than without sampling. We recommend the use of these models as a benchmark for similar self-learning classification tasks. The classification models presented here retain their interpretability with the ability to present feature importances to the farmer in contrast to the "black box" models of Deep Learning methods.

The transition period, from pregnancy to lactation, implies comprehensive metabolic and endocrine changes including a systemic inflammatory reaction and oxidative stress around calving in dairy cows. The aim of the present study was a longitudinal characterization of the serum concentration of acute phase proteins (APP), i.e., haptoglobin (Hp), serum amyloid A (SAA) and acidic glycoprotein (AGP), as well as of markers for oxidative stress in another large dairy animal, i.e. water buffalo, during the transition from late pregnancy to early lactation. As indicators of oxidative status, derivatives of reactive oxygen metabolites (dROM), ferric reducing ability (FRAP), thiobarbituric acid reactive substances (TBARS), and advanced oxidation protein products (AOPP) were determined in serum. Indicators for metabolic stress included nonesterified fatty acids (NEFA), sshydroxybutyrate (BHB) and adiponectin. Bovine specific ELISA methods for Hp and adiponectin were adapted and validated for their application to water buffalo samples. Blood samples were collected weekly from 11 pluriparous water buffalo cows (lactation number 4.6 +/- 1.6; daily milk yield 9.0 +/- 1.9 kg; means +/- SD) from 6 weeks (wk) ante partum (ap) until 8 wk post partum (pp). The maximum concentrations of Hp were observed in wk 1 pp, followed by a decrease towards values lower than before calving starting from wk 3 pp. The concentrations of SAA also peaked in wk 1 pp and then returned to basal values. The AGP serum concentrations increased suddenly from the first to the second wk pp and remained elevated for all the observation period. Indicators of oxidative status which changed in concentration during the transition period were dROM, AOPP and the oxidative stress index (OSi) (dROM/FRAP ratio). Briefly, dROM and AOPP values were lower pp as compared to ap, and OSi was largely following the pattern of dROM due to the constant FRAP values. The TBARS values did not change during the observation period. From the metabolic indicators, adiponectin was not changing with time, whereas greater NEFA and BHB values were observed ap than pp. The time course of NEFA and of some indicators for oxidative status (dROM, OSi and AOPP) point to greater metabolic load in late pregnancy as compared with the first wk of lactation - contrary to the common situation in dairy cows. Both BHB and NEFA values remained below the thresholds applied for dairy cows to define subclinical or clinical ketosis, thus indicating that the buffaloes included in this study were not under metabolic stress. The increase in concentration of the APP around calving supports the concept that an inflammatory reaction is a physiological epiphenomenon of the onset of lactation in water buffalos that is independent of metabolic stress.

In the last decades, there has been an increasing interest in animal protection and welfare issues. Heart rate variability (HRV) measurement with portable heart rate monitors on cows has established itself as a suitable method for assessing physiological states. However, more forward-looking technologies, already successfully applied to evaluate HRV data, are pushing the market. This study examines the validity and usability of collecting HRV data by exchanging the Polar watch V800 as a receiving unit of the data compared to a custom smartphone application on cows. Therefore, both receivers tap one signal sent by the Polar H7 transmitter simultaneously. Furthermore, there is a lack of suitable methods for the preparation and calculation of HRV parameters, especially for livestock. A method is presented for calculating more robust time domain HRV parameters via median formation. The comparisons of the respective simultaneous recordings were conducted after artifact correction for time domain HRV parameters. High correlations (r = 0.82–0.98) for cows as well as for control data set in human being (r = 0.98–0.99) were found. The utilization of smart devices and the robust method to determine time domain HRV parameters may be suitable to generate valid HRV data on cows in field-based settings.

A second affiliation for author H. Sadri was missing from this paper. The second affiliation is “Department of Clinical Science, Faculty of Veterinary Medicine, University of Tabriz, 516616471 Tabriz, Iran.” The authors regret the error. Different milk feeding intensities during the first 4 weeks of rearing dairy calves: Part 2: Effects on the metabolic and endocrine status during calfhood and around the first lactationJournal of Dairy ScienceVol. 100Issue 4PreviewFeeding dairy calves at high intensity has been demonstrated to increase milk yield in later life. We investigated the effect of 3 different feeding regimens in the preweaning period on the metabolic and endocrine status during calfhood and in heifers at the onset of the first lactation. In trial 1, 57 German Holstein calves were allocated to 3 different feeding groups: milk replacer restricted to 6.78 kg/calf per day, 11.5% solids (MR-res, n = 20), milk replacer 13.8% solids, ad libitum (MR-ad lib, n = 17), and whole milk ad libitum (WM-ad lib, n = 20). Full-Text PDF Open Archive

Tracking the spatio-temporal activity is highly relevant for domains like security, health, and quality management. Since animal welfare became a topic in politics and legislation locomotion patterns of livestock have received increasing interest. In contrast to the monitoring of pedestrians cattle activity tracking poses special challenges to both sensors and data analysis. Interesting states are not directly observable by a single sensor. In addition, sensors must be accepted by cattle and need to be robust enough to cope with a rough environment. In this article, we introduce the novel combination of heart rate and positioning sensors. Attached to neck and chest they are less interfering than accelerometers at the ankles. Exploiting the potential of such combined sensor system that records locomotion and non-spatial information from the heart rate sensor however is challenging. We introduce a novel two level method for the activity tracking focused on the duration and sequence of activity states. We combine Support Vector Machine (SVM) with Conditional Random Field (CRF) and extend Conditional Random fields by an explicit representation of duration. The SVM characterizes local activity states, whereas the CRF addresses sequences of local states to sequences incorporating spatial and non-spatial contextual knowledge. This combination provides a reliable and comprehensive identification of defined activity patterns, as well as their chronology and durations, suitable for the integration in an activity data base. This data base is used to extract physiological parameters and promises insights into internal states such as fitness, well-being and stress. Interestingly we were able to demonstrate a significant correlation between resting pulse rate and the day of pregnancy.

The objective was to use longitudinal data of blood nonesterified fatty acids (NEFA) and β-hydroxybutyrate (BHBA) concentrations to describe the relationship between NEFA and BHBA in dairy cows during the periparturient period. Blood NEFA and BHBA concentration data collected from d −21 prepartum to 21 postpartum for 269 multiparous Holstein cows were recruited from 4 different studies carried out within our research groups. Of the 269 cows enrolled in the data set, 117 cows (43.5%) had at least 1 postpartum hyperketonemic event (BHBA ≥ 1.2 mmol/L), and 202 cows (75.1%) had at least 1 event of elevated postpartum NEFA (≥700 μEq/L) between 3 and 21 d in milk. Area under the curve (AUC) was used to investigate relationships between metabolites over time. Overall, Pearson correlation relationships between transition period NEFA and BHBA AUC were poor. There was a negative correlation between postpartum NEFA AUC and prepartum BHBA AUC, although the correlation coefficient was low (r = −0.26). A positive correlation existed between postpartum NEFA AUC and postpartum BHBA AUC; however, the correlation coefficient was low (r = 0.26), reinforcing a poor relationship between these metabolites during the periparturient period. Large variation was found between the day of maximum NEFA concentration within the first 21 d in milk and day of maximum BHBA concentration for the same time period. The mean and median days of maximum NEFA concentration were 6.8 and 6 d, respectively, whereas the mean and median days of maximum BHBA concentration were 9.6 and 8 d, respectively; however the range in days for both the mean and median day of maximum concentrations was very large. Overall, our data set indicates a poor relationship between blood concentrations of NEFA and BHBA during the periparturient period of dairy cows, suggesting that elevated concentrations of one should not be extrapolated to suggest elevated concentrations of the other metabolite.

Adiponectin and intracellular 5'adenosine monophosphate-activated protein kinase (AMPK) are important modulators of glucose and fat metabolism. Cinnamon exerts beneficial effects by improving insulin sensitivity and blood lipids, e.g., through increasing adiponectin concentrations and AMPK activation. The underlying mechanism is unknown. The Gi/Go-protein-coupled receptor (GPR) 109A stimulates adiponectin secretion after binding its ligand niacin. Trans-cinnamic acid (tCA), a compound of cinnamon is another ligand. We hypothesize whether AMPK activation and adiponectin secretion by tCA is transmitted by GPR signaling. Differentiated 3T3-L1 cells were incubated with pertussis toxin (PTX), an inhibitor of Gi/Go-protein-coupling, and treated with different tCA concentrations. Treatment with tCA increased adiponectin and the pAMPK/AMPK ratio (p ≤ 0.001). PTX incubation abolished the increased pAMPK/AMPK ratio and adiponectin secretion. The latter remained increased compared to controls (p ≤ 0.002). tCA treatment stimulated adiponectin secretion and AMPK activation; the inhibitory effect of PTX suggests GPR is involved in tCA stimulated signaling.

Activity patterns of dairy cattle have received increasing interest in recent years because they promise insights into health state and well-being. The fusion with data from additional sensor signals promises a comprehensive monitoring of activity patterns composed of sequences of single activity states. We used a combination of a Support Vector Machine (SVM), a state of the art classification method, and a Conditional Random Field (CRF). SVMs distinguish single states, whereas CRFs label state sequences under consideration of specified constraints. In a preliminary experiment, a Local Positioning System was combined with a heart rate sensor in order to estimate seven spatiotemporal activity states. The application of the CRF to the SVM result caused a slight increase in accuracy (5%) but a major improvement at the correct determination of long sequences (increasing length of the longest common subsequence from 3481 to 6207 periods). This robust detection of long lying sequences allowed for the unaffected extraction of the resting pulse.

Two groups of Gram-stain positive, aerobic bacterial strains were isolated from raw cow's milk, from a milking machine and from bulk tank milk. Based on their 16S rRNA gene sequences these isolates formed two distinct groups within the genus Corynebacterium. The sequence similarities of the isolates to the type strains of species of the genus Corynebacterium were below 98.4%. The presence of menaquinones MK-8(H-2) and MK-9(H-2), the predominant fatty acid 18:1 cis 9 and a polar lipid pattern with several phospholipids but without aminolipids was in accord with the characteristics of this genus. The results of DNA DNA hybridization, biochemical tests and chemotaxonomic properties allowed genotypic and phenotypic differentiation of the strains from all known species of the genus Corynebacterium. Therefore, the isolates were assigned to two novel species of this genus for which the names Corynebacterium frankenforstense sp. nov. (type strain ST18(T)=DSM 45800(T)=CCUG 63371(T)), and Corynebacterium lactis sp. nov. (type strain RW2-5(T)=DSM 45799(T)=CCUG 63372(T)) are proposed, respectively.

Saliva is a colorless viscous liquid secreted from salivary glands that plays a major role in oral and general health. Saliva shares several compositional similarities with other body fluids e.g. blood and urine and is therefore interesting for biological assays (Lac et al., 1993). When compared to other biological samples as analytical matrix, saliva is advantageous for practical purposes due to its simple and minimally stressful, non-invasive collection. In addition to being more straightforward and economical to obtain than blood, saliva has the further advantage of being easier to handle for diagnostic purposes because it does not clot (Wong, 2006). The use of saliva for analytical purposes in mostly focused on clinical investigations (i.e. in the field of endocrinology, neuroendocrinology) and in physiological research (i.e. in sport and exercise science). Recent developments comprise proteomic and metabolomic analyses of saliva for identifying markers of disease (Lamy, 2012), however, the most frequent application of saliva analysis in praxi is probably the assessment of cortisol as a biomarker of stress. In veterinary medicine and in animal science, salivary cortisol is considered particularly important to assess welfare under different conditions of keeping and handling. However, besides cortisol, the use of saliva in veterinary science is still quite limited. With regard to acute phase proteins (APP) in animal saliva, the available literature is largely limited pigs and dogs. The APPs are blood proteins that can be monitored to assess the innate immune system’s systemic response to infection, inflammation, and trauma or stress (Ceron et al., 2005; Murata et al., 2004). Serum amyloid A (SAA), C-reactive protein and haptoglobin (Hp) have been identfied and quantified in porcine saliva and were demonstrated to be well correlated with the circulating concentrations (Gutierrez et al., 2009; Hiss et al., 2003; Hiss, 2009; Soler et al., 2012). To the best of our knowledge, there are no data available about the presence of APP in ruminant saliva. Alpha 1 acid glycoprotein (AGP), lipopolysacccharide binding protein (LBP), Hp and SAA have been reported to be expressed in bovine salivary glands (Lecchi et al., 2009; Lecchi et al., 2012; Rahman et al., 2010), thus their presence in saliva is probable and might be due to local expression and also to transfer from blood. In order to investigate the presence of four major APP, i.e. AGP, Hp, LBP and SAA in bovine saliva, we used samples collected from calves before and after dehorning. Dehorning of calves is a routine procedure carried out under local anesthesia with the purpose to facilitate management in later life. Dehorning is considered as stressful but the lesions are probably only mild since an acute phase reaction is reportedly not occurring (Doherty et al., 2007). Using quantitative assays for Hp as well as qualitative Western Blots (WB) for AGP, LBP and SAA, we aimed to evaluate whether these APP are detectable in saliva.