Longitudinal Metabolic Biomarker Profile of Hyperketonemic Cows from Dry-Off to Peak Lactation and Identification of Prognostic Classifiers

Currently about 30% to 50% of all dairy cows are affected by a metabolic or infectious disease during the transition period. A key factor for preventive actions is the ability to precisely predict metabolic diseases at an early stage. We report the longitudinal metabolic profile of non-esterified fatty acids, beta-hydroxybutyrate (BHB), total bilirubin, and aspartate aminotransferase in hyperketonemic dairy cows. Aiming for a novel measurement regime to improve metabolic health in dairy cows, we evaluated prognostic classifiers for hyperketonemia. In the observational longitudinal study, 99 healthy adult primiparous and multiparous Simmental dairy cows were included. Every cow was monitored weekly for 14 consecutive weeks, beginning two weeks prior to the expected day of parturition until peak lactation. Cows with serum concentrations of BHB > 0.8 mmol/L were considered hyperketonemic. Biomarker profiles were fitted by the maximum likelihood method using a mixed effects natural cubic spline model. In the hyperketonemic group, the BHB profile remained significantly higher than that of the control group until the end of the study period. As a prognostic classifier, the cut-off level of 0.54 mmol/L BHB measured on the 10th day post partum had the highest area under the curve. These results provide new longitudinal insights into the metabolic biomarker progression of dairy cows and enable an early onset diagnosis of hyperketonemia.

A review of biomarker and imaging monitoring to predict heart failure recovery

Heart failure is a clinical syndrome caused by structural cardiac abnormalities that lead to increased intracardiac pressures and decreased cardiac output. Following cardiovascular insult or direct myocardial injury, neurohormonal activation triggers hemodynamic changes and cardiac remodeling to preserve cardiac output. While initially adaptive, cardiac remodeling eventually causes pathologic changes in cardiac structure that often compromise cardiac function. Reverse remodeling is the regression of abnormal cardiac chamber geometry and function after myocardial injury. In recent years, several classes of therapeutics have been associated with greater likelihood of reverse remodeling. Heart failure recovery and heart failure remission, terms encompassing the clinical correlates of reverse remodeling, have been associated with improved survival in patients with heart failure with reduced ejection. As such, identifying predictors of heart failure recovery can have important implications for guiding clinical practice and therapeutic innovation. This review addresses the role of biomarkers and imaging monitoring in predicting structural, functional, and clinical recovery in patients with acute and chronic heart failure.

Dynamic Monitoring of Systemic Biomarkers with Gastric Sensors

Continuous monitoring in the intensive care setting has transformed the capacity to rapidly respond with interventions for patients in extremis. Noninvasive monitoring has generally been limited to transdermal or intravascular systems coupled to transducers including oxygen saturation or pressure. Here it is hypothesized that gastric fluid (GF) and gases, accessible through nasogastric (NG) tubes, commonly found in intensive care settings, can provide continuous access to a broad range of biomarkers. A broad characterization of biomarkers in swine GF coupled to time-matched serum is conducted . The relationship and kinetics of GF-derived analyte level dynamics is established by correlating these to serum levels in an acute renal failure and an inducible stress model performed in swine. The ability to monitor ketone levels and an inhaled anaesthetic agent (isoflurane) in vivo is demonstrated with novel NG-compatible sensor systems in swine. Gastric access remains a main stay in the care of the critically ill patient, and here the potential is established to harness this establishes route for analyte evaluation for clinical management.

Prospective Monitoring of Lyso-Gb1 on DBS Sample in Three Children Recognized at Newborn Screening for Gaucher Disease and Untreated

Background: Gaucher disease (GD) is an autosomal recessive lysosomal disease. Extended neonatal screening currently includes GD in several different regions. Decision on when to start enzyme replacement therapy (ERT) upon confirmed diagnosis or upon appearance of first clinical manifestation of the disease remains an unmet need. Methods: We report our preliminary experience in tightly monitoring blood levels of glucosyl-sphingosine (lyso-Gb1), on DBS at birth and then every 4 weeks, in the absence of ERT in three consecutive newborns identified for GD as part of a screening program. Results: Initial lyso-Gb1 values were above cut-off. In two cases, lyso-Gb1 levels showed a reduction during the first 3 months of life and, by month 4, they had reached a value lower than the upper normal value. In the case of the third child, after an initial drop to less than 50% of the initial value, lyso-Gb1 levels remained pretty stable at the following four time-points. At the time of writing, all remain free from any disease manifestation at the age of 20, 11 and 8 months, respectively, with normal physical growth and blood count; therefore, ERT has not been started yet. Conclusions: A specific threshold for lyso-Gb1 value to be considered as associated with non-reversible progression to disease is not yet defined. We hypothesize that a trend toward stable increase of this biomarker, confirmed at repeated evaluation, rather than a single threshold, could be convincing for starting ERT even before clinical manifestation of the disease.

Influence of Different Lactation Stages on Circadian Rhythmicity of Metabolic Biomarkers in Dairy Cows: A Pilot Study

Simple Summary

Circadian rhythms are present in most species and play an important role in their metabolism. Metabolic biomarkers, which are commonly used to assess the health status, are also affected by those rhythms. In this study, we investigate the influence of lactation and time on four metabolic biomarkers in dairy cows. Our findings provide new insights into the physiology of circadian rhythms in dairy cows, which enables novel metabolic monitoring approaches.

Abstract

Currently, subclinical metabolic imbalances at the individual cow and herd level are detected by measuring biomarkers in single blood samples. However, diurnal variations have not been fully described yet but need to be considered when sampling for a robust ad consistent analysis. The study describes the influence of lactation phases on circadian rhythms and diurnal variations for non-esterified fatty acids (NEFA), beta-hydroxybutyrate (BHB), total bilirubin (tBIL) and aspartate aminotransferase (AST) in dairy cows. In an observational pilot study, we used 16 clinically healthy Simmental dairy cows subdivided in four different lactation stages (dry-off, fresh, high and late lactating). Every cow was monitored for 24 h, with blood sampling and assessment of clinical parameters every 2 h. Time and lactation stage influence the concentration of the biomarkers NEFA, BHB and tBIL in serum. Further, circadian rhythmicity was found in high lactating cows for NEFA peaking at 5:39 am and BHB peaking at 4:20 pm. We suggest blood sampling for single-point measurements within three hours after the first feeding until two hours after the last feeding of the day. The results provide a new insight into the physiology of circadian rhythms in dairy cows and enable improved metabolic monitoring.

Microneedles at the Forefront of Next Generation Theranostics

Theranostics, combining therapeutic and diagnostic functions, marks a revolutionary advancement in modern medicine, with microneedle technology at its forefront. This review explores the substantial developments and multifaceted applications of microneedles, which have evolved from basic transdermal drug delivery devices to sophisticated diagnostic and therapeutic platforms. Microneedles enhance access to biomarkers via interstitial fluid, enabling real-time monitoring of physiological conditions, such as glucose and hormone levels, thus facilitating continuous health tracking. The evolution of microneedle design from solid to dissolvable forms broadens their utility from mere drug delivery to complex sensing and therapeutic applications, including insulin delivery for diabetes management, vaccination, and gene therapy. This paper delves into the integration of microneedles with wearable technologies, highlighting their role in closed-loop systems that combine real-time monitoring with dynamic, precise therapeutic delivery. By addressing gaps in the literature regarding their integrated diagnostic and treatment capabilities, this review underscores the pivotal role of microneedles in personalizing medicine. It concludes with a visionary perspective on the future trajectory of microneedle technology, emphasizing its potential to revolutionize therapeutic strategies through enhanced efficacy, safety, and patient compliance.

Wavelet-Mixed Landmark Survival Models for the Effect of Short-Term Changes of Potassium in Heart Failure Patients

Statistical methods to study the association between a longitudinal biomarker and the risk of death are very relevant for the long-term care of subjects affected by chronic illnesses, such as potassium in heart failure patients. Particularly in the presence of comorbidities or pharmacological treatments, sudden crises can cause potassium to undergo very abrupt yet transient changes. In the context of the monitoring of potassium, there is a need for a dynamic model that can be used in clinical practice to assess the risk of death related to an observed patient's potassium trajectory. We considered different landmark survival approaches, starting from the simple approach considering the most recent measurement. We then propose a novel method based on wavelet filtering and landmarking to retrieve the prognostic role of past short-term potassium shifts. We argue that while taking into account the smooth changes in the biomarker, short-term changes cannot be overlooked. State-of-the-art dynamic survival models are prone to give more importance to the smooth component of the potassium profiles. However, our findings suggest that it is essential to also take into account recent potassium instability to capture all the relevant prognostic information. The data used comes from over 2000 subjects, with a total of over 80,000 repeated potassium measurements collected through administrative health records. The proposed wavelet landmark method revealed the prognostic role of past short-term changes in potassium. We also performed a simulation study to assess how and when to apply the proposed wavelet-mixed landmark model.