Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes.

Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing interactions, in creating dynamic and personalized blood glucose management plans. Methods: We developed a mathematical model specifically for patients with type IVP diabetes, validated with data from 10 patients and 17 key parameters. The model includes continuous glucose monitoring (CGM) noise and random carbohydrate intake to simulate real-life conditions. A closed-loop system was designed to enable the application of reinforcement learning algorithms. Results: By implementing a Policy Optimization (PPO) branch, we achieved an average Time in Range (TIR) metric of 73%, indicating improved blood glucose control. Conclusions: This study presents a personalized insulin therapy solution using reinforcement learning. Our closed-loop model offers a promising approach for improving blood glucose regulation, with potential applications in personalized diabetes management.

Personalized food consumption detection with deep learning and Inertial Measurement Unit sensor.

For individuals diagnosed with diabetes mellitus, it is crucial to keep a record of the carbohydrates consumed during meals, as this should be done at least three times daily, amounting to an average of six meals. Unfortunately, many individuals tend to overlook this essential task. For those who use an artificial pancreas, carbohydrate intake proves to be a critical factor, as it can activate the insulin pump in the artificial pancreas to deliver insulin to the body. To address this need, we have developed personalized deep learning model that can accurately detect carbohydrate intake with a high degree of accuracy. Our study employed a publicly available dataset gathered by an Inertial Measurement Unit (IMU), which included accelerometer and gyroscope data. The data was sampled at a rate of 15 Hz, necessitating preprocessing. For our tailored to the patient model, we utilized a recurrent network comprising Long short-term memory (LSTM) layers. Our findings revealed a median F1 score of 0.99, indicating a high level of accuracy. Additionally, the confusion matrix displayed a difference of only 6 s, further validating the model's accuracy. Therefore, we can confidently assert that our model architecture exhibits a high degree of accuracy. Our model performed well above 90% on the dataset, with most results between 98%-99%. The recurrent networks improved the problem-solving capabilities significantly, though some outliers remained. The model's average prediction latency was 5.5 s, suggesting that later meal predictions result in extended meal progress predictions. The dataset's limitation of mostly single-day data points raises questions about multi-day performance, which could be explored by collecting multi-day data, including night periods. Future enhancements might involve transformer networks and shorter time windows to improve model responsiveness and accuracy. Therefore, we can confidently assert that our model exhibits a high degree of accuracy.

Heart rate variability in horses with and without severe equine asthma.

BACKGROUND: Equine asthma in severe form (severe equine asthma [sEA]) shares remarkable similarities with human asthma. Human studies detected changes in the autonomic nervous system function in asthmatic patients based on heart rate variability (HRV) analysis. STUDY DESIGN: Observational study. OBJECTIVES: To investigate the relationship between sEA and HRV in horses. METHODS: Twenty horses diagnosed with sEA and 20 asymptomatic (non-sEA) horses were investigated. SEA horses showed clinical signs. The RR intervals of the ECG were recorded for 1 h at rest between 9 AM and 11 AM using a heart rate (HR) monitor. HRV data were calculated using Kubios software. Parameters recorded for the sEA and non-sEA groups were compared using one-way MANOVA model. The significance level was set at α = 0.05. RESULTS: SD2 (mean 99.6 ± SD 25.3 vs. 42.5 ± 17.1), SDNN (82.7 ± 20.7 vs. 41.3 ± 14.3), TINN (398.1 ± 104.9 vs. 209.3 ± 71.9), SD2/SD1 ratio (1.7 ± 0.2 vs. 1.1 ± 0.3), Total power (4740.2 ± 1977.9 vs. 1503.0 ± 1179.3), LF (2415.3 ± 1072.4 vs. 707.4 ± 649.9), SD1 (60.9 ± 15.9 vs. 39.2 ± 14.1), RMSSD (86.0 ± 22.6 vs. 55.3 ± 19.8) and HF (1575.8 ± 902.5 vs. 578.1 ± 491.1) were lower in sEA horses compared with the non-sEA horses (p < 0.01 for each variable). SD2, SDNN, TNN, the SD2/SD1 ratio and Total power showed the greatest discriminatory power in differentiating the sEA and non-sEA groups. MAIN LIMITATIONS: Small sample size. CONCLUSION: Our findings indicate that like humans, asthmatic horses show an overall reduction in autonomic control. A relative increase of the parasympathetic modulation of the heart was also observed. After further investigations, HRV measurement might be a non-invasive approach to monitor autonomic nervous system responses of sEA horses.

A Review of the Effects of Stress on Dairy Cattle Behaviour.

In this narrative review, the authors summarise the relationship between stress and behaviour and how dairy cattle cope with stressors. Based on the available literature, the most common stressors in intensive dairy cattle farming, such as pain, disease, heat stress, poor comfort caused by technology, and social stress, are surveyed. The authors describe how these stressors modify the behaviour of dairy cattle, influencing their feeding patterns, social interactions, and overall well-being. Additionally, the review explores the effectiveness of various coping mechanisms employed by dairy cattle to mitigate stress, including physiological adaptations and behavioural responses. This review is a valuable resource for understanding and grading stress in dairy cattle through behavioural reactions. Elucidating the intricate interplay between stressors and behaviour offers insights into potential interventions to improve animal welfare and productivity in dairy farming. Furthermore, this review highlights areas for future research, suggesting avenues for more comprehensive behavioural studies to enhance our understanding of stress management strategies in dairy cattle.

Pregnancy Rates of Holstein Friesian Cows with Cavitary or Compact Corpus Luteum.

Cavitary corpora lutea are commonly observed during the estrous cycle in bovines. Since the quality of the corpus luteum (CL) is extremely important during embryo transfer when embryos are implanted into the recipient, the ultrasonographic examination of the CL is becoming more and more important in terms of the outcome of the procedure. In the present study, a total of 2477 ultrasonographic transrectal diagnoses were performed, and data were collected between the years of 2018 and 2020 in a large-scale Holstein Friesian dairy farm in Hungary. In 91.1% (n = 2257) and in 8.9% (n = 220) of the cases, compact CLs and cavitary CLs, respectively, were diagnosed at pregnancy diagnosis. The presence of a cavitary CL on the ovary at pregnancy diagnosis increased the odds of remaining open after pregnancy by 21 times compared to the presence of a compact CL (OR = 21.0, p < 0.001) in the cows. The presence of cavitary CL was not influenced either by month or season. Ovarian cysts were detected in 196 cases (8.0%) in the examined animals. The presence of a cavitary CL decreased by 9 times when an ovarian cyst was also diagnosed (OR = 9.0, 1.6% vs. 9.5%, p < 0.001). The presence of an ovarian cyst decreased the odds of established pregnancy by 81 times (OR = 81.1, p < 0.001). Based on our results, the presence of a cavitary CL between days 31 and 42 after artificial insemination is associated with a smaller chance of conception in Holstein Friesian cows. The presence of an ovarian cyst decreases the occurrence of cavitary CL and the chance of conception.

Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks.

Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.

The Impact of Parental Electronic Health Literacy on Disease Management and Outcomes in Pediatric Type 1 Diabetes Mellitus: Cross-Sectional Clinical Study.

BACKGROUND: Despite the growing uptake of smart technologies in pediatric type 1 diabetes mellitus (T1DM) care, little is known about caregiving parents' skills to deal with electronic health information sources. OBJECTIVE: We aimed to assess the electronic health literacy of parents caring for children with T1DM and investigate its associations with disease management and children's outcomes. METHODS: A cross-sectional survey was performed involving 150 parent-child (8-14 years old with T1DM) dyads in a university pediatric diabetology center. Parents' electronic health literacy (eHealth Literacy Scale [eHEALS]), general health literacy (Chew questionnaire and Newest Vital Sign [NVS]), and attitudes toward T1DM care (Parental Self-Efficacy Scale for Diabetes Management [PSESDM] and Hypoglycemia Fear Survey [HFS]) were investigated. Children's treatment, HbA1c level, and quality of life (Pediatric Quality of Life Inventory Diabetes Module [PedsQL Diab] and EQ-5D-Y-3L) were assessed. Multiple linear regression analysis was performed to investigate the determining factors of 6-month average HbA1c. RESULTS: Of the 150 children, 38 (25.3%) used a pen, 55 (36.7%) used a pen plus a sensor, 6 (4.0%) used an insulin pump, and 51 (34.0%) used an insulin pump plus a sensor. Parents' average eHEALS score (mean 31.2, SD 4.9) differed significantly by educational level (P=.04) and the children's treatment (P=.005), being the highest in the pump + sensor subgroup. The eHEALS score showed significant Pearson correlations with the Chew score (r=-0.45; P<.001), NVS score (r=0.25; P=.002), and PSESDM score (r=0.35; P<.001) but not with the children's HbA1c (r=-0.143; P=.08), PedsQL Diab (r=-0.0002; P>.99), and EQ-5D-Y-3L outcomes (r=-0.13; P=.12). Regression analysis revealed significant associations of the child's HbA1c level with sex (ß=0.58; P=.008), treatment modality (pen + sensor: ß=-0.66; P=.03; pump + sensor: ß=-0.93; P=.007), and parents' self-efficacy (PSESDM; ß=-0.08; P=.001). CONCLUSIONS: Significantly higher parental electronic health literacy was found in T1DM children using a glucose sensor. The electronic health literacy level was associated with parents' diabetes management attitude but not with the child's glycemic control. Studies further investigating the role of parental electronic health literacy in T1DM children managed at different levels of care and the local context are encouraged.

Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey.

BACKGROUND: The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences. METHODS: A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist's and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents' electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions. RESULTS: Altogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD = 11.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first (p = 0.677), and a significantly higher average amount for radiologist vs. AI in the second task (p = 0.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen. CONCLUSIONS: Individuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies.

The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review.

BACKGROUND: Diabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine. OBJECTIVE: We aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies. METHODS: We searched the PubMed and Web of Science databases from 2016 to 2020. Studies were included if the use of ML was reported in children with DM aged 2 to 18 years, including studies on complications, screening studies, and in silico samples. In studies following the ML workflow of training, validation, and testing of results, reporting quality was assessed via MI-CLAIM by consensus judgments of independent reviewer pairs. Positive answers to the 17 binary items regarding sufficient reporting were qualitatively summarized and counted as a proxy measure of reporting quality. The synthesis of results included testing the association of reporting quality with publication and data type, participants (human or in silico), research goals, level of code sharing, and the scientific field of publication (medical or engineering), as well as with expert judgments of clinical impact and reproducibility. RESULTS: After screening 1043 records, 28 studies were included. The sample size of the training cohort ranged from 5 to 561. Six studies featured only in silico patients. The reporting quality was low, with great variation among the 21 studies assessed using MI-CLAIM. The number of items with sufficient reporting ranged from 4 to 12 (mean 7.43, SD 2.62). The items on research questions and data characterization were reported adequately most often, whereas items on patient characteristics and model examination were reported adequately least often. The representativeness of the training and test cohorts to real-world settings and the adequacy of model performance evaluation were the most difficult to judge. Reporting quality improved over time (r=0.50; P=.02); it was higher than average in prognostic biomarker and risk factor studies (P=.04) and lower in noninvasive hypoglycemia detection studies (P=.006), higher in studies published in medical versus engineering journals (P=.004), and higher in studies sharing any code of the ML pipeline versus not sharing (P=.003). The association between expert judgments and MI-CLAIM ratings was not significant. CONCLUSIONS: The reporting quality of ML studies in the pediatric population with DM was generally low. Important details for clinicians, such as patient characteristics; comparison with the state-of-the-art solution; and model examination for valid, unbiased, and robust results, were often the weak points of reporting. To assess their clinical utility, the reporting standards of ML studies must evolve, and algorithms for this challenging population must become more transparent and replicable.

The ubiquitin thioesterase YOD1 ameliorates mutant Huntingtin induced pathology in Drosophila.

Huntington's disease (HD) is a neurodegenerative disorder caused by a dominant gain-of-function mutation in the huntingtin gene, resulting in an elongated polyglutamine repeat in the mutant Huntingtin (mHtt) that mediates aberrant protein interactions. Previous studies implicated the ubiquitin-proteasome system in HD, suggesting that restoring cellular proteostasis might be a key element in suppressing pathology. We applied genetic interaction tests in a Drosophila model to ask whether modulating the levels of deubiquitinase enzymes affect HD pathology. By testing 32 deubiquitinase genes we found that overexpression of Yod1 ameliorated all analyzed phenotypes, including neurodegeneration, motor activity, viability, and longevity. Yod1 did not have a similar effect in amyloid beta overexpressing flies, suggesting that the observed effects might be specific to mHtt. Yod1 overexpression did not alter the number of mHtt aggregates but moderately increased the ratio of larger aggregates. Transcriptome analysis showed that Yod1 suppressed the transcriptional effects of mHtt and restored the expression of genes involved in neuronal plasticity, vesicular transport, antimicrobial defense, and protein synthesis, modifications, and clearance. Furthermore, Yod1 overexpression in HD flies leads to the upregulation of genes involved in transcriptional regulation and synaptic transmission, which might be part of a response mechanism to mHtt-induced stress.

Noninfectious Causes of Pregnancy Loss at the Late Embryonic/Early Fetal Stage in Dairy Cattle.

In cattle, initial pregnancy diagnosis takes place during the late embryonic/early fetal stage of gestation. From this point onward, pregnancy loss may occur in up to one fifth of pregnancies before the initial pregnancy diagnosis is confirmed. This means the early identification of risk factors is a key part of pregnancy diagnosis and herd management. The various factors responsible for pregnancy losses are classified into infectious and noninfectious. Among the noninfectious causes, several dam-related (circumstances of the individual pregnancy or milk production) and herd-related factors causing stress have been well established. In this review, we summarize the impacts of these noninfectious factors and predict associated risks of pregnancy loss.

The use of body surface temperatures in assessing thermal status of hutch-reared dairy calves in shaded and unshaded conditions.

The study was carried out in a Hungarian large-scale dairy farm during a 5-day period in hot August weather. Altogether 16 preweaning calves were chosen for the study. An agricultural mesh with 80% shielding was stretched over eight calf cages at 2 m from the ground to shield the cages in their entirety, while eight others were left unshaded. Ambient temperature and relative humidity were measured in 10 min intervals inside and outside one of the hutches in the shaded and unshaded groups during the total length of the study. The rectal temperature of the calves was measured by a digital thermometer every 4 h. Surface temperatures were measured on body parts, in the same intervals as rectal temperature with an infrared thermometer. Measuring sites included: the leg (metacarpus), muzzle, eye bulb, scapula, and ear. Statistical analyses were performed to assess the effects of shading on environmental and body temperatures and to also assess the strength of the association between core, skin and ambient temperatures; to estimate the temperature gradient between body shell and core; to compare the changes in heat dissipation capacity of the different body regions (as represented by temperatures of various sites) with increasing ambient temperature controlling for shaded or unshaded conditions; and to predict the risk of hyperthermia (rectal temperature not lower than 39.5°C) with the CART classification method. The average rectal temperatures suggest that the temperature conditions both in shaded and unshaded groups imposed a severe heat load on the calves. The temperature of the body shell, as represented by skin temperatures, shows a much more significant variation, similar to ambient temperature. As expected, areas that are closer to the core of the body (ear and eye) show less difference from rectal temperature and show a narrower range (lower variance), as more distal regions (leg, scapula) have a wider range. Body surface temperatures are more related to ambient temperature in calves than rectal temperature. The predictive value of infrared body surface temperatures for predicting heat stress or rectal temperature is low.

Effects of Short- and Medium-Chain Fatty Acids on Production, Meat Quality, and Microbial Attributes-A Review.

The non-therapeutic use of antimicrobials in poultry production contributes to the spread of drug-resistant pathogens in both birds and humans. Antibiotics are known to enhance feed efficiency and promote the growth and weight gain of poultry. New regulatory requirements and consumer preferences have led to a reduced use of antibiotics in poultry production and to the discovery of natural alternatives to antibiotic growth promoters. This interest is not only focused on the direct removal or inhibition of causative microorganisms but also on the prevention of diseases caused by enteric pathogens using a range of feed additives. A group of promising feed additives is composed of short- and medium-chain fatty acids (SCFAs and MCFAs) and their derivatives. MCFAs possess antibacterial, anticoccidial, and antiviral effects. In addition, it has been proven that these acids act in synergy if they are used together with organic acids, essential oils, or probiotics. These fatty acids also benefit intestinal health integrity and homeostasis in broilers. Other effects have been documented as well, such as an increase in intestinal angiogenesis and the gene expression of tight junctions. The aim of this review is to provide an overview of SCFAs and MCFAs as alternatives to antibiotic growth promoters and to summarize the current findings in the literature to show their possible benefits on production, meat quality, and gut health in poultry.

Fractional calculus in mathematical oncology.

Even though, nowadays, cancer is one of the leading causes of death, too little is known about the behavior of this disease due to its unpredictability from one patient to another. Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, improvements are still necessary on these models. The primary objective of the present research is to prove the efficiency of fractional order calculus in mathematical oncology, more specifically in tumor growth modeling. For this, a generalization of the four most used differential equation models in tumor volume measurements fitting is realized, using the corresponding fractional order equivalent. Are established the fractional order Exponential, Logistic, Gompertz, General Bertalanffy-Pütter and Classical Bertalanffy-Pütter models for a treated and untreated dataset. The obtained results are compared by Mean Squared Error (MSE) with the integer order correspondent of each model. The results prove the superiority of the fractional order models. The MSE of fractional order models are reduced at least at half in comparison with the MSE of the integer order equivalent. It is demonstrated in this way that fractional order deterministic models can offer a good starting point in finding a proper mathematical model for tumor evolution prediction. Fractional calculus is a suitable method in this case due to its memory property, aspect that particularly characterizes biological processes.

Mob4 is essential for spermatogenesis in Drosophila melanogaster.

Gamete formation is essential for sexual reproduction in metazoans. Meiosis in males gives rise to spermatids that must differentiate and individualize into mature sperm. In Drosophila melanogaster, individualization of interconnected spermatids requires the formation of individualization complexes that synchronously move along the sperm bundles. Here, we show that Mob4, a member of the Mps-one binder family, is essential for male fertility but has no detectable role in female fertility. We show that Mob4 is required for proper axonemal structure and its loss leads to male sterility associated with defective spermatid individualization and absence of mature sperm in the seminal vesicles. Transmission electron micrographs of developing spermatids following mob4RNAi revealed expansion of the outer axonemal microtubules such that the 9 doublets no longer remained linked to each other and defective mitochondrial organization. Mob4 is a STRIPAK component, and male fertility is similarly impaired upon depletion of the STRIPAK components, Strip and Cka. Expression of the human Mob4 gene rescues all phenotypes of Drosophila mob4 downregulation, indicating that the gene is evolutionarily and functionally conserved. Together, this suggests that Mob4 contributes to the regulation of the microtubule- and actin-cytoskeleton during spermatogenesis through the conserved STRIPAK complex. Our study advances the understanding of male infertility by uncovering the requirement for Mob4 in sperm individualization.

Prepartum Magnesium Butyrate Supplementation of Dairy Cows Improves Colostrum Yield, Calving Ease, Fertility, Early Lactation Performance and Neonatal Vitality.

Butyrate promotes rumen epithelium growth and function; however, the effect of prepartum butyrate supplementation on dairy cow productivity, health and their offspring has not been extensively studied. Furthermore, no studies have investigated the effect of magnesium butyrate (MgB), which is also a source of magnesium. A trial was performed to test the hypothesis that prepartum MgB supplementation (105 g/cow/day) would increase colostrum quality and improve calving, newborn calf vitality and cow health. Multiparous Holstein cows were randomly assigned to MgB supplemented (n = 107) and Control groups (n = 112). Colostrum yield and the total yield of IgG, protein and lactose were higher (p ≤ 0.05) in the supplemented group. The calving assistance rate was lower (p ≤ 0.012), and the neonatal vitality score was higher (p ≤ 0.001) in the MgB group. Improved parameters related to cow health and fertility were observed in the supplemented group. The MgB group also had higher milk yield during the first week of lactation (p ≤ 0.001), and a higher (p ≤ 0.05) body condition score from 3 to 9 weeks after calving. In conclusion, prepartum MgB supplementation provides a wide range of benefits for dairy cows, as well as their newborn calves.

Effect of Prepartum Magnesium Butyrate Supplementation on Rumination Time, Milk Yield and Composition, and Blood Parameters in Dairy Cows.

BACKGROUND: Magnesium butyrate (MgB) supplementation of dairy cows during the three-week close-up period was tested for its effects on blood energy analytes, rumination time, inflammation, and lactation performance. METHODS: Daily milk yield was recorded and weekly milk samples collected for the first 70 days of lactation from MgB supplemented (MgB, n = 34), and unsupplemented (Control, n = 31) multiparous Holstein-Friesian cows. During a period from week 3 to week 10 postpartum, blood samples were taken and analyzed for various parameters, and ruminant activity was measured. RESULTS: The MgB group yielded 25.2% more milk than the Control during week 1, and had increased milk fat and protein concentrations over a longer duration. Somatic cell counts (SCC) were decreased in the MgB group independent of days in milk. No differences were observed between groups in terms of plasma non-esterified fatty acids, ß-hydroxybutyrate, glucose, or blood iCa levels. The MgB group had lower haptoglobin (Hp) levels during lactation relative to the Control group. Time spent ruminating increased after calving with MgB due to a shorter post calving rumination delay relative to the Control group. CONCLUSIONS: Prepartum MgB supplementation improved lactation performance without affecting blood energy analytes. The basis by which MgB also improved rumination activity remains to be determined, as DMI could not be assessed. As MgB lowered SCC and Hp concentrations, it is speculated that MgB may help minimize postpartum inflammatory processes.

Epidemiology and patients' self-reported knowledge of implantable medical devices: Results of a cross-sectional survey in Hungary.

BACKGROUND: Implantable medical devices (IMDs) are medical instruments embedded inside the body. Well-informed and empowered patients living with IMDs are key players of improving IMD-related patient safety and health outcomes. However, little is known about IMD patients' epidemiology, characteristics, and current awareness levels. Our primary aim was to investigate the point and lifetime prevalence of patients living with IMDs. Patients' IMD-related knowledge and determinants of IMDs' impact on their life were also explored. METHODS: An online cross-sectional survey was conducted. Respondents' IMD history, whether they received instructions for use and IMD's overall impact on life were recorded by self-reports. Patients' knowledge about living with IMDs was assessed on visual analogue scales (VAS, 0-10). Shared decision-making was analyzed by the 9-item Shared Decision Making Questionnaire (SDM-Q-9). Descriptive statistics and subgroup comparisons between IMD wearers were performed for statistical differences. Significant determinants of IMD's overall impact on life were examined in linear regression analysis. RESULTS: In the total sample (N = 1400, mean age 58.1 ±11.1; female 53.7%), nearly one third of respondents were living with IMD (30.9%; 433/1400). Among them, the most frequent IMDs were tooth implants (30.9%) and intraocular lens (26.8%). Mean knowledge VAS scores were similar (range: 5.5 ±3.8-6.5 ±3.2) but differences by IMD types were observed. Patients who received instructions for use or reported better impact on life indicated higher self-reported knowledge. Regression confirmed that patients' knowledge was significant predictor of IMD's impact on life, but this effect was overwritten by the SDM-Q-9. CONCLUSIONS: This first comprehensive epidemiological study on IMDs provides basic data for public health strategy planning alongside the implementation of MDR. Improved self-perceived outcomes were associated with higher knowledge hence education of patients receiving IMD deserves consideration. We suggest to investigate further the role of shared decision-making on IMD's overall impact on patients' life in future prospective studies.

The Use of Extreme Value Statistics to Characterize Blood Glucose Curves and Patient Level Risk Assessment of Patients With Type I Diabetes.

OBJECTIVE: Characterizing blood glucose curves and providing precise patient level risk assessment of hyperglycemia using extreme value statistics and comparing these assessments with traditional indicators of glycemic variability which are not designed to specifically capture the risk of hyperglycemia. RESEARCH DESIGN AND METHODS: One year return level (blood glucose level exceeded exactly once every year on average) and probability of exceeding and expected time spent above certain thresholds (600 and 400 mg/dL) per year were calculated. As a comparison, traditional metrics for glycemic variability were determined too. The effect of body mass index on extremes was also investigated using non-stationary models. Metrics were calculated on a dataset containing 170.8 patient-years of measurements of 226 patients. RESULTS: Nine high-risk patients were identified with the novel metrics: their estimated time spent above 600 mg/dL per year were above 2 hours. These patients were at moderate risk according to the traditional metrics. Higher body mass index was associated with more extreme glucose levels. CONCLUSIONS: Through these estimates it is possible to assess each patient's individual clinical risk of hyperglycemia even beyond the observed blood glucose levels and detection limits. Additionally, it allows the assessment of the impact of clinical characteristics and treatments on blood glucose control in a novel, mathematically well-founded and potentially clinically more useful way than the already existing indicators.