Characterization of dry-cured ham microbiota at 12 months of seasoning obtained from different rearing strategies using 16S rRNA profiling.

In this study, we investigated the microbiota of 72 Italian ham samples collected after 12 months of seasoning. The hams were elaborated from pigs fed different rearing methods, including the traditional restricted medium protein diet chosen as control (C group); restrictive low protein diet (LP group); two ad libitum high-protein diet groups (HP9M group: slaughter at 9 months of age; HP170 group: slaughter at 170 kg). A multi-amplicon 16S metabarcoding approach was used, and a total of 2845 Amplicon Sequence Variants were obtained from the 72 ham samples. Main phyla included: Firmicutes (90.8%), Actinobacteria (6.2%), Proteobacteria (2.7%), and Bacteroidota (0.12%). The most common genera were Staphylococcus, Tetragenococcus, and Brevibacterium. Shannon index for α-diversity was found statistically significant, notably for the HP9M group, indicating higher diversity compared to C. PERMANOVA test on ß-diversity showed significant differences in rearing methods between HP170 and C, HP170 and LP, and HP9M vs. C. All three rearing methods revealed associations with characteristic communities: the HP9M group had the highest number of associations, many of which were due to spoilage bacteria, whereas the LP group had the highest number of seasoning-favourable genera.

Lactation modeling and the effects of rotational crossbreeding on milk production traits and milk-spectra-predicted enteric methane emissions.

Rotational crossbreeding has not been widely studied in relation to the enteric methane emissions of dairy cows, nor has the variation in emissions during lactation been modeled. Milk infrared spectra could be used to predict proxies of methane emissions in dairy cows. Therefore, the objective of this work was to study the effects of crossbreeding on the predicted infrared proxies of methane emissions and the variation in the latter during lactation. Milk samples were taken once from 1,059 cows reared in 2 herds, and infrared spectra of the milk were used to predict milk fat (mean ± SD; 3.79 ± 0.81%) and protein (3.68 ± 0.36%) concentrations, yield (21.4 ± 1.5 g/kg dry matter intake), methane intensity (14.2 ± 2.0 g/kg corrected milk), and daily methane production (358 ± 108 g/d). Of these cows, 620 were obtained from a 3-breed (Holstein, Montbéliarde, and Viking Red) rotational mating system, and the rest were purebred Holsteins. Milk production data and methane traits were analyzed using a nonlinear model that included the fixed effects of herd, genetic group, and parity, and the 4 parameters (a, b, c, and k) of a lactation curve modeled using the Wilmink function. Milk infrared spectra were found to be useful for direct prediction of qualitative proxies, such as methane yield and intensity, but not quantitative traits, such as daily methane production, which appears to be better estimated (450 ± 125 g/d) by multiplying a measured daily milk yield by infrared-predicted methane intensity. Lactation modeling of methane traits showed daily methane production to have a zenith curve, similar to that of milk yield but with a delayed peak (53 vs. 37 d in milk), whereas methane intensity is characterized by an upward curve that increases rapidly during the first third of lactation and then slowly till the end of lactation (10.5 g/kg at 1 d in milk to 15.2 g/kg at 300 d in milk). However, lactation modeling was not useful in explaining methane yield, which is almost constant during lactation. Lastly, the methane yield and intensity of cows from 3-breed rotational crossbreeding are not greater, and their methane production is lower than that of purebred Holsteins (452 vs. 477 g/d). Given the greater longevity of crossbred cows, and their lower replacement rate, rotational crossbreeding could be a way of mitigating the environmental impact of milk production.

Novel insights into the associations between immune cell population distribution in mammary glands and milk minerals in Holstein cows.

Udder health has a crucial role in sustainable milk production, and various reports have pointed out that changes in udder condition seem to affect milk mineral content. The somatic cell count (SCC) is the most recognized indicator for the determination of udder health status. Recently, a new parameter, the differential somatic cell count (DSCC), has been proposed for a more detailed evaluation of intramammary infection patterns. Specifically, the DSCC is the combined proportions of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) on the total SCC, with macrophages (MAC) representing the remainder proportion. In this study, we evaluated the association between DSCC in combination with SCC on a detailed milk mineral profile in 1,013 Holstein-Friesian cows reared in 5 herds. An inductively coupled plasma-optical emission spectrometry was used to quantify 32 milk mineral elements. Two different linear mixed models were fitted to explore the associations between the milk mineral elements and first, the DSCC combined with SCC, and second, DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the proportion of PMN-LYM and MAC by SCC. We observed a significant positive association between SCC and milk Na, S, and Fe levels. Differential somatic cell count showed an opposite behavior to the one displayed by SCC, with a negative association with Na and positive association with K milk concentrations. When considering DSCC as count, Na and K showed contrasting behavior when associated with PMN-LYM or MAC counts, with decreasing of Na content and increasing K when associated with increasing PMN-LYM counts, and increasing Na and decreasing K when associated with increasing MAC count. These findings confirmed that an increase in SCC is associated with altered milk Na and K amounts. Moreover, MAC count seemed to mirror SCC patterns, with the worsening of inflammation. Differently, PMN-LYM count exhibited patterns of associations with milk Na and K contents attributable more to LYM than PMN, given the nonpathological condition of the majority of the investigated population. An interesting association was observed for milk S content, which increased with increasing of inflammatory conditions (i.e., increased SCC and MAC count) probably attributable to its relationship with milk proteins, especially whey proteins. Moreover, milk Fe content showed positive associations with the PMN-LYM population, highlighting its role in immune regulation during inflammation. Further studies including individuals with clinical condition are needed to achieve a comprehensive view of milk mineral behavior during udder health impairment.

The challenge of ecological validity in temporomandibular disorders research.

OBJECTIVE: To review the ecological validity of outcomes from current research involving temporomandibular disorders (TMDs), with an emphasis on chronic myofascial pain and the precocious development of degenerative disease of the temporomandibular joint (TMJ). MATERIALS AND METHODS: Current approaches used to study TMDs in terms of neuromechanics, masticatory muscle behaviours, and the dynamics of the autonomic nervous system (ANS) were assessed for ecological validity in this review. In particular, the available literature was scrutinized regarding the effects of sampling, environmental and psychophysiological constraints and averaging data across biological rhythms. RESULTS: Validated computer-assisted numerical modelling of the neuromechanics used biological objective functions to accurately predict muscle activation patterns for jaw-loading tasks that were individual-specific. With respect to masticatory muscle behaviour, current findings refute the premise that sustained bruxing and clenching at high jaw-loading magnitudes were associated with painful TMDs such as myofascial pain. Concerning the role of the ANS in TMDs, there remains the need for personalized assessments based on biorhythms, and where the detection of dysregulated physiologic oscillators may inform interventions to relieve pain and restore normal function. CONCLUSIONS: Future human research which focuses on TMD myofascial pain or the precocious development and progression of TMJ degenerative joint disease requires experimental designs with ecological validity that capture objectively measured data which meaningfully reflect circadian and ultradian states.

Standardised Tool for the Assessment of Bruxism.

OBJECTIVE: This paper aims to present and describe the Standardised Tool for the Assessment of Bruxism (STAB), an instrument that was developed to provide a multidimensional evaluation of bruxism status, comorbid conditions, aetiology and consequences. METHODS: The rationale for creating the tool and the road map that led to the selection of items included in the STAB has been discussed in previous publications. RESULTS: The tool consists of two axes, specifically dedicated to the evaluation of bruxism status and consequences (Axis A) and of bruxism risk and etiological factors and comorbid conditions (Axis B). The tool includes 14 domains, accounting for a total of 66 items. Axis A includes the self-reported information on bruxism status and possible consequences (subject-based report) together with the clinical (examiner report) and instrumental (technology report) assessment. The Subject-Based Assessment (SBA) includes domains on Sleep Bruxism (A1), Awake Bruxism (A2) and Patient's Complaints (A3), with information based on patients' self-report. The Clinically Based Assessment (CBA) includes domains on Joints and Muscles (A4), Intra- and Extra-Oral Tissues (A5) and Teeth and Restorations (A6), based on information collected by an examiner. The Instrumentally Based Assessment (IBA) includes domains on Sleep Bruxism (A7), Awake Bruxism (A8) and the use of Additional Instruments (A9), based on the information gathered with the use of technological devices. Axis B includes the self-reported information (subject-based report) on factors and conditions that may have an etiological or comorbid association with bruxism. It includes domains on Psychosocial Assessment (B1), Concurrent Sleep-related Conditions Assessment (B2), Concurrent Non-Sleep Conditions Assessment (B3), Prescribed Medications and Use of Substances Assessment (B4) and Additional Factors Assessment (B5). As a rule, whenever possible, existing instruments, either in full or partial form (i.e. specific subscales), are included. A user's guide for scoring the different items is also provided to ease administration. CONCLUSIONS: The instrument is now ready for on-field testing and further refinement. It can be anticipated that it will help in collecting data on bruxism in such a comprehensive way to have an impact on several clinical and research fields.

The response of relativistic outflowing gas to the inner accretion disk of a black hole.

The brightness of an active galactic nucleus is set by the gas falling onto it from the galaxy, and the gas infall rate is regulated by the brightness of the active galactic nucleus; this feedback loop is the process by which supermassive black holes in the centres of galaxies may moderate the growth of their hosts. Gas outflows (in the form of disk winds) release huge quantities of energy into the interstellar medium, potentially clearing the surrounding gas. The most extreme (in terms of speed and energy) of these-the ultrafast outflows-are the subset of X-ray-detected outflows with velocities higher than 10,000 kilometres per second, believed to originate in relativistic (that is, near the speed of light) disk winds a few hundred gravitational radii from the black hole. The absorption features produced by these outflows are variable, but no clear link has been found between the behaviour of the X-ray continuum and the velocity or optical depth of the outflows, owing to the long timescales of quasar variability. Here we report the observation of multiple absorption lines from an extreme ultrafast gas flow in the X-ray spectrum of the active galactic nucleus IRAS 13224-3809, at 0.236 ± 0.006 times the speed of light (71,000 kilometres per second), where the absorption is strongly anti-correlated with the emission of X-rays from the inner regions of the accretion disk. If the gas flow is identified as a genuine outflow then it is in the fastest five per cent of such winds, and its variability is hundreds of times faster than in other variable winds, allowing us to observe in hours what would take months in a quasar. We find X-ray spectral signatures of the wind simultaneously in both low- and high-energy detectors, suggesting a single ionized outflow, linking the low- and high-energy absorption lines. That this disk wind is responding to the emission from the inner accretion disk demonstrates a connection between accretion processes occurring on very different scales: the X-ray emission from within a few gravitational radii of the black hole ionizing the disk wind hundreds of gravitational radii further away as the X-ray flux rises.

Body and milk production traits as indicators of energy requirements and efficiency of purebred Holstein and 3-breed rotational crossbred cows from Viking Red, Montbéliarde, and Holstein sires.

This study aimed to compare rotational 3-breed crossbred cows of Viking Red, Montbéliarde, and Holstein breeds with purebred Holstein cows for a range of body measurements, as well as different metrics of the cows' productivity and production efficiency. The study involved 791 cows (440 crossbreds and 351 purebreds), that were managed across 2 herds. Within each herd, crossbreds and purebreds were reared and milked together, fed the same diets, and managed as one group. The heart girth, height at withers, and body length were measured, and body condition score (BCS) was determined on all the cows on a single test day. The body weight (BW) of 225 cows were used to develop an equation to predict BW from body size traits, parity, and days in milk, which was then used to estimate the BW of all the cows. Equations from the literature were used to estimate body protein and lipid contents using the predicted BW and BCS. Evidence suggests that maintenance energy requirements may be closely related to body protein mass, and Holstein and crossbred cows may be different in body composition. Therefore, we computed the requirements of net energy for maintenance (NEM) on the basis either of the metabolic weight (NEM-MW: 0.418 MJ/kg of metabolic BW) or of the estimated body protein mass according to a coefficient (NEM-PM: 0.631 MJ/kg body protein mass) computed on the subset comprising the purebred Holstein. On the same day when body measurements were collected, individual test-day milk yield and fat and protein contents were retrieved once from the official Italian milk recording system, and milk was sampled to determine fresh cheese yield. Measures of NEM were used to scale the production traits. Statistical analyses of all variables included the fixed effects of herd, days in milk, parity, and genetic group (purebred Holstein and crossbred), and the herd × genetic group interaction. External validation of the equation predicting BW yielded a correlation coefficient of 0.94 and an average bias of -4.95 ± 36.81 kg. The crossbreds had similar predicted BW and NEM-MW compared with the Holsteins. However, NEM-PM of crossbreds was 3.8% lower than that of the Holsteins, due to their 11% greater BCS and different estimated body composition. The crossbred cows yielded 4.8% less milk and 3.4% less milk energy than the purebred Holsteins. However, the differences between genetic groups were no longer significant when the production traits were scaled on NEM-PM, suggesting that the crossbreds and purebreds have the same productive ability and efficiency per unit of body protein mass. In conclusion, measures of productivity and efficiency that combine the cows' production capability with traits related to body composition and the energy cost of production seem to be more effective criteria for comparing crossbred and purebred Holstein cows than just milk, fat, and protein yields.

Associations between the detailed milk mineral profile, milk composition, and metabolic status in Holstein cows.

The causes of variation in the milk mineral profile of dairy cattle during the first phase of lactation were studied under the hypothesis that the milk mineral profile partially reflects the animals' metabolic status. Correlations between the minerals and the main milk constituents (i.e., protein, fat, and lactose percentages), and their associations with the cows' metabolic status indicators were explored. The metabolic status indicators (MET) that we used were blood energy-protein metabolites [nonesterified fatty acids, ß-hydroxybutyrate (BHB), glucose, cholesterol, creatinine, and urea], and liver ultrasound measurements (predicted triacylglycerol liver content, portal vein area, portal vein diameter and liver depth). Milk and blood samples, and ultrasound measurements were taken from 295 Holstein cows belonging to 2 herds and in the first 120 d in milk (DIM). Milk mineral contents were determined by ICP-OES; these were considered the response variable and analyzed through a mixed model which included DIM, parity, milk yield, and MET as fixed effects, and the herd/date as a random effect. The MET traits were divided in tertiles. The results showed that milk protein was positively associated with body condition score (BCS) and glucose, and negatively associated with BHB blood content; milk fat was positively associated with BHB content; milk lactose was positively associated with BCS; and Ca, P, K and S were the minerals with the greatest number of associations with the cows' energy indicators, particularly BCS, predicted triacylglycerol liver content, glucose, BHB and urea. We conclude that the protein, fat, lactose, and mineral contents of milk partially reflect the metabolic adaptation of cows during lactation and within 120 DIM. Variations in the milk mineral profile were consistent with changes in the major milk constituents and the metabolic status of cows.

Milk metagenomics and cheese-making properties as affected by indoor farming and summer highland grazing.

The study of the complex relationships between milk metagenomics and milk composition and cheese-making efficiency as affected by indoor farming and summer highland grazing was the aim of the present work. The experimental design considered monthly sampling (over 5 mo) of the milk produced by 12 Brown Swiss cows divided into 2 groups: the first remained on a lowland indoor farm from June to October, and the second was moved to highland pastures in July and then returned to the lowland farm in September. The resulting 60 milk samples (2 kg each) were used to analyze milk composition, milk coagulation, curd firming, and syneresis processes, and to make individual model cheeses to measure cheese yields and nutrient recoveries in the cheese. After DNA extraction and Illumina Miseq sequencing, milk microbiota amplicons were also processed by means of an open-source pipeline called Quantitative Insights Into Microbial Ecology (Qiime2, version 2018.2; https://qiime2.org). Out of a total of 44 taxa analyzed, 13 bacterial taxa were considered important for the dairy industry (lactic acid bacteria, LAB, 5 taxa; and spoilage bacteria, 4) and for human (other probiotics, 2) and animal health (pathogenic bacteria, 2). The results revealed the transhumant group of cows transferred to summer highland pastures showed an increase in almost all the LAB taxa, bifidobacteria, and propionibacteria, and a reduction in spoilage taxa. All the metagenomic changes disappeared when the transhumant cows were moved back to the permanent indoor farm. The relationships between 17 microbial traits and 30 compositional and technological milk traits were investigated through analysis of correlation and latent explanatory factor analysis. Eight latent factors were identified, explaining 75.3% of the total variance, 2 of which were mainly based on microbial traits: pro-dairy bacteria (14% of total variance, improving during summer pasturing) and pathogenic bacteria (6.0% of total variance). Some bacterial traits contributed to other compositional-technological latent factors (gelation, udder health, and caseins).

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle.

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by ß-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CVr) and herd-out (CVh). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R2 value from 5% to 75% for CVr and 2% to 139% for CVh, whereas the stacking ensemble increased the R2 value from 4% to 70% for CVr and 4% to 150% for CVh. Considering the best model, with the CVr scenario, good prediction accuracies were obtained for glucose (R2 = 0.81), urea (R2 = 0.73), albumin (R2 = 0.75), total reactive oxygen metabolites (R2 = 0.79), total thiol groups (R2 = 0.76), ceruloplasmin (R2 = 0.74), total proteins (R2 = 0.81), globulins (R2 = 0.87), and Na (R2 = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

Labor Analgesia: A Systematic Review and Meta-Analysis of Non-Pharmacological Complementary and Alternative Approaches to Pain during First Stage of Labor.

the aim of the study was to conduct a meta-analysis to evaluate the efficacy of non-invasive and non-pharmacological techniques on labor first-stage pain intensity. Literature databases were searched from inception to May 2021, and research was expanded through the screening of previous systematic reviews. Inclusion criteria were: (1) population: women in first stage of labor; (2) intervention: non-pharmacological, non-invasive, or minimally invasive intrapartum analgesic techniques alternative and/or complementary to pharmacological analgesia; (3) comparison: routine intrapartum care or placebos; (4) outcomes: subjective pain intensity; and (5) study design: randomized controlled trial. Risk of bias of included studies was investigated, data analysis was performed using R version 3.5.1. Effect size was calculated as difference between the control and experimental groups at posttreatment in terms of mean pain score. A total of 63 studies were included, for a total of 6146 patients (3468 in the experimental groups and 2678 in the control groups). Techniques included were massage (n = 11), birth balls (n = 5) mind-body interventions (n = 8), heat application (n = 12), music therapy (n = 9), dance therapy (n = 2), acupressure (n = 16), and transcutaneous electrical nerve stimulation (TENS) (n = 8). The present review found significant evidence in support of the use of complementary and alternative medicine for labor analgesia, and different methods showed different impact. However, more high-quality trials are needed.

Real-time milk analysis integrated with stacking ensemble learning as a tool for the daily prediction of cheese-making traits in Holstein cattle.

Cheese-making traits in dairy cattle are important to the dairy industry but are difficult to measure at the individual level because there are limitations on collecting phenotypic information. Mid-infrared spectroscopy has its advantages, but it can only be used during monthly milk recordings. Recently, in-line devices for real-time analysis of milk quality have been developed. The AfiLab recording system (Afimilk) offers significant benefits as phenotypes can be collected from each cow at each milking session. The objective of this study was to assess the potential of integrating AfiLab real-time milk analyzer measures with the stacking ensemble learning technique using heterogeneous base learners for the in-line daily monitoring of cheese-making traits in Holstein cattle with a view to developing a precision livestock farming system for monitoring the technological quality of milk. Data and samples for wet-laboratory analyses were collected from 499 Holstein cows belonging to 2 farms where the AfiLab system was installed. The traits of concern were 9 milk coagulation traits [3 milk coagulation properties (MCP), and 6 curd firming traits (CFt)], and 7 cheese-making traits [3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits]. The near-infrared AfiLab spectral data and on-farm information (days in milk and parity) were used to assess the predictive ability of different statistical methods [elastic net (EN), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and artificial neural network (ANN)] across different cross-validation scenarios. These statistical methods were considered the base learners, which were then combined in a stacking ensemble learning. Results indicate that including information on the cows (days in milk and parity) in the AfiLab infrared prediction increased its accuracy by 10.3% for traditional MCP, 13.8% for curd firming, 9.8% for CY, and 11.2% for REC traits compared with those obtained from near-infrared AfiLab alone. The statistical approaches exhibited high prediction accuracies (R2) averaged across the cross-validation scenarios for traditional MCP (0.58 for ANN, 0.55 for EN and GBM, 0.52 for XGBoost, and 0.62 for stacking ensemble), CFt (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble), and similar R2 averages for CY and REC (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble). The ANN approach was more accurate than the other base learners (EN, GBM, and XGBoost) and improved accuracy across cross-validation scenarios on average by 7% for traditional MCP, 5% for CFt, 8% for CY, and 7% for REC. The stacking ensemble method improved prediction accuracy by 3% to 31% for traditional MCP, 2% to 26% for CFt, 1% to 38% for CY traits, and 2% to 27% for REC traits compared with the base learners. The prediction accuracies of the different approaches evaluated tended to decrease from the 10-fold cross-validation to the independent validation scenario, although there was a smaller reduction in prediction accuracy with the stacking ensemble learning technique across all the cross-validation scenarios. Our results show that combining in-line on-farm information with stacking ensemble machine learning represents an effective alternative for obtaining robust daily predictions of milk cheese-making traits.

The development of the Standardised Tool for the Assessment of Bruxism (STAB): An international road map.

This paper summarises the background reasoning and work that led to the selection of the items included in the Standardised Tool for the Assessment of Bruxism (STAB), also introducing the list of items. The instrument is currently being tested for face validity and on-field comprehension. The underlying premise is that the different motor activities included in the bruxism spectrum (e.g. clenching vs. grinding, with or without teeth contact) potentially need to be discriminated from each other, based on their purportedly different aetiology, comorbidities and potential consequences. Focus should be on a valid impression of the activities' frequency, intensity and duration. The methods that can be used for the above purposes can be grouped into strategies that collect information from the patient's history (subject-based), from the clinical assessment performed by an examiner (clinically based) or from the use of instruments to measure certain outcomes (instrumentally based). The three strategies can apply to all aspects of bruxism (i.e. status, comorbid conditions, aetiology and consequences). The STAB will help gathering information on many aspects, factors and conditions that are currently poorly investigated in the field of bruxism. To this purpose, it is divided into two axes. Axis A includes the self-reported information on bruxism status and potential consequences (subject-based report) together with the clinical (examiner report) and instrumental assessment (technology report). Axis B includes the self-reported information (subject-based report) on factors and conditions that may have an etiological or comorbid role for bruxism. This comprehensive multidimensional assessment system will allow building predictive model for clinical and research purposes.

Signal acquisition and analysis of ambulatory electromyographic recordings for the assessment of sleep bruxism: A scoping review.

BACKGROUND: Ambulatory electromyographic (EMG) devices are increasingly being used in sleep bruxism studies. EMG signal acquisition, analysis and scoring methods vary between studies. This may impact comparability of studies and the assessment of sleep bruxism in patients. OBJECTIVES: (a) To provide an overview of EMG signal acquisition and analysis methods of recordings from limited-channel ambulatory EMG devices for the assessment of sleep bruxism; and (b) to provide an overview of outcome measures used in sleep bruxism literature utilising such devices. METHOD: A scoping review of the literature was performed. Online databases PubMed and Semantics Scholar were searched for studies published in English until 7 October 2020. Data on five categories were extracted: recording hardware, recording logistics, signal acquisition, signal analysis and sleep bruxism outcomes. RESULTS: Seventy-eight studies were included, published between 1977 and 2020. Recording hardware was generally well described. Reports of participant instructions in device handling and of dealing with failed recordings were often lacking. Basic elements of signal acquisition, for example amplifications factors, impedance and bandpass settings, and signal analysis, for example rectification, signal processing and additional filtering, were underreported. Extensive variability was found for thresholds used to characterise sleep bruxism events. Sleep bruxism outcomes varied, but typically represented frequency, duration and/or intensity of masticatory muscle activity (MMA). CONCLUSION: Adequate and standardised reporting of recording procedures is highly recommended. In future studies utilising ambulatory EMG devices, the focus may need to shift from the concept of scoring sleep bruxism events to that of scoring the whole spectrum of MMA.

Virtual reality and music therapy as distraction interventions to alleviate anxiety and improve mood states in breast cancer patients during chemotherapy.

Psychological distress is a common consequence of breast cancer diagnosis and treatment and could further exacerbate therapy side effects. Interventions increasing treatment tolerance are crucial to improve both patients' quality of life and adherence to therapies. Virtual reality (VR) has emerged as an effective distraction tool for different medical procedures. Here, we assessed the efficacy of immersive and interactive VR in alleviating chemotherapy-related psychological distress in a cohort of Italian breast cancer patients, also comparing its effects with those of music therapy (MT). Thirty patients were included in the VR group, 30 in the MT group, and 34 in the control group, consisting of patients receiving standard care during chemotherapy. Our data suggest that both VR and MT are useful interventions for alleviating anxiety and for improving mood states in breast cancer patients during chemotherapy. Moreover, VR seems more effective than MT in relieving anxiety, depression, and fatigue.

Temporomandibular Joint Bioengineering Conference: Working Together Toward Improving Clinical Outcomes.

The sixth temporomandibular joint (TMJ) Bioengineering Conference (TMJBC) was held on June 14-15 2018, in Redondo Beach, California, 12 years after the first TMJBC. Speakers gave 30 presentations and came from the United States, Europe, Asia, and Australia. The goal of the conference has remained to foster a continuing forum for bioengineers, scientists, and surgeons and veterinarians to advance technology related to TMJ disorders. These collective multidisciplinary interactions over the past decade have made large strides in moving the field of TMJ research forward. Over the past 12 years, in vivo approaches for tissue engineering have emerged, along with a wide variety of degeneration models, as well as with models occurring in nature. Furthermore, biomechanical tools have become more sensitive and new biologic interventions for disease are being developed. Clinical directives have evolved for specific diagnoses, along with patient-specific biological and immunological responses to TMJ replacement devices alloplastic and/or bioengineered devices. The sixth TMJBC heralded many opportunities for funding agencies to advance the field: (1) initiatives on TMJ that go beyond pain research, (2) more training grants focused on graduate students and fellows, (3) partnership funding with government agencies to translate TMJ solutions, and (4) the recruitment of a critical mass of TMJ experts to participate on grant review panels. The TMJ research community continues to grow and has become a pillar of dental and craniofacial research, and together we share the unified vision to ultimately improve diagnoses and treatment outcomes in patients affected by TMJ disorders.

Shift in the cow milk microbiota during alpine pasture as analyzed by culture dependent and high-throughput sequencing techniques.

In the present study, two groups of cows from a permanent lowland farm (PF) were divided during summer and reared in the PF or in a temporary alpine farm (ALP), respectively. Microbiological analyses were performed with the objective to investigate the microbial evolution of milk before, during, and after summer transhumance comparing, in particular, the two groups of cows to determine whether the alpine pasture could directly influence the milk microbiota. A significant increase of all microbial groups was registered in milk samples collected in the ALP. Interestingly, many strains belonging to species with well reported technological and probiotic activities were isolated from Alpine milk (20% Lactococcus lactis subsp. lactis/cremoris, 18% Lactobacillus paracasei, 14% Bifidobacterium crudilactis and 18% Propionibacterium sp.), whereas only 16% of strains isolated from the permanent farm milk belonged to the species Lactococcus lactis subsp. lactis/cremoris, 6% to Lactobacillus paracasei, 2% to Bifidobacterium crudilactis and 5% to Propionibacterium sp. The MiSeq Illumina data showed that Alpine milk presented a significant reduction of Pseudomonas and an increase of Lactococcus, Bifidobacterium and Lactobacillus genera. These data confirmed the practice of Alpine pasture as one of the main drivers affecting the milk microbiota. All the microbial changes disappeared when cows were delivered back from Alpine pasture to the indoor farm.

Towards a Standardized Tool for the Assessment of Bruxism (STAB)-Overview and general remarks of a multidimensional bruxism evaluation system.

The aim of the present paper was to give an overview of the general project and to present the macrostructure of a comprehensive multidimensional toolkit for the assessment of bruxism, viz. a bruxism evaluation system. This is a necessary intermediate step that will be detailed in a successive extended publication and will ultimately lead to the definition of a Standardized Tool for the Assessment of Bruxism (STAB) as the final product. Two invitation-only workshops were held during the 2018 and 2019 General Session & Exhibition of the International Association for Dental Research (IADR) meetings. Participants of the IADR closed meetings were split into two groups, to put the basis for a multidimensional evaluation system composed of two main axes: an evaluation Axis A with three assessment domains (ie subject-based, clinically based and instrumentally based assessment) and an aetiological/risk factors Axis B assessing different groups of factors and conditions (ie psychosocial assessment; concurrent sleep and non-sleep conditions; drug and substance use or abuse; and additional factors). The work of the two groups that led to the identification of different domains for assessment is summarised in this manuscript, along with a road map for future researches. Such an approach will allow clinicians and researchers to modulate evaluation of bruxism patients with a comprehensive look at the clinical impact of the different bruxism activities and aetiologies. The ultimate goal of this multidimensional system is to facilitate the refinement of decision-making algorithms in the clinical setting.

Night-time autonomic nervous system ultradian cycling and masticatory muscle activity.

OBJECTIVES: To test if there was a correlation between night-time masticatory muscle activity, as measured by duty factors, and ultradian cycling of autonomic nervous system (ANS) spectral powers in subjects without temporomandibular disorder (TMD)-related pain. SETTING AND SAMPLE POPULATION: The University of Missouri-Kansas City School of Dentistry. Three women and four men of average ages 38 ±8 and 56 ± 17 years, respectively, gave informed consent to participate. MATERIAL AND METHODS: Investigators taught subjects to record heart (electrocardiography, ECG) and masticatory muscle activities (electromyography, EMG). ECG recordings were analysed for ANS ultradian cycling by a polynomial fit to the ratio of sympathetic and parasympathetic spectral powers (ms2 ). Masseter and temporalis EMG recordings were analysed over 20-minute epochs around peaks and valleys in the ANS ultradian cycles. Duty factors (% time of masticatory muscle activity/20-minute epoch) were determined relative to average threshold EMG (TEMG ) required to produce a given bite force (N). Regression analyses quantified relationships between normalized muscle duty factors and ANS spectral powers. RESULTS: Subjects made a total of 27 sets of night-time ECG and EMG recordings that averaged 6.6 ± 1.1 hours per recording. Highest average duty factors were associated with TEMG of 1-2 N and showed cumulative masseter and temporalis activities of 9.2 and 8.8 seconds/20-minute epoch, respectively. Normalized masticatory muscle duty factors showed non-linear relationships with normalized sympathetic (R2  = +0.82), parasympathetic (R2  = -0.70) and sympathetic/parasympathetic spectral powers (R2  = +0.75). CONCLUSIONS: Night-time ANS spectral powers showed ultradian cycling and were correlated with masseter and temporalis muscle activities in adult subjects without TMD.

Hitomi X-ray studies of Giant Radio Pulses from the Crab pulsar.

To search for giant X-ray pulses correlated with the giant radio pulses (GRPs) from the Crab pulsar, we performed a simultaneous observation of the Crab pulsar with the X-ray satellite Hitomi in the 2 - 300 keV band and the Kashima NICT radio observatory in the 1.4 - 1.7 GHz band with a net exposure of about 2 ks on 25 March 2016, just before the loss of the Hitomi mission. The timing performance of the Hitomi instruments was confirmed to meet the timing requirement and about 1,000 and 100 GRPs were simultaneously observed at the main and inter-pulse phases, respectively, and we found no apparent correlation between the giant radio pulses and the X-ray emission in either the main or inter-pulse phases. All variations are within the 2 sigma fluctuations of the X-ray fluxes at the pulse peaks, and the 3 sigma upper limits of variations of main- or inter-pulse GRPs are 22% or 80% of the peak flux in a 0.20 phase width, respectively, in the 2 - 300 keV band. The values become 25% or 110% for main or inter-pulse GRPs, respectively, when the phase width is restricted into the 0.03 phase. Among the upper limits from the Hitomi satellite, those in the 4.5-10 keV and the 70-300 keV are obtained for the first time, and those in other bands are consistent with previous reports. Numerically, the upper limits of main- and inter-pulse GRPs in the 0.20 phase width are about (2.4 and 9.3) ×10-11 erg cm-2, respectively. No significant variability in pulse profiles implies that the GRPs originated from a local place within the magnetosphere and the number of photon-emitting particles temporally increases. However, the results do not statistically rule out variations correlated with the GRPs, because the possible X-ray enhancement may appear due to a > 0.02% brightening of the pulse-peak flux under such conditions.