Prediction Equations Research Articles

Components of Total Energy Expenditure in Healthy and Critically Ill Children: A Comprehensive Review.

Total energy expenditure (TEE) is the total energy expended by an individual to sustain life, activities, and growth. TEE is formed by four components: resting energy expenditure (REE), activity energy expenditure (AEE), growth-related energy expenditure (GEE), and the thermic effect of feeding (TEF). Some energy expenditure (EE) components may change throughout childhood and cannot be reliably estimated using prediction formulae. To summarize measured TEE components as reported in the literature in healthy and critically ill children. We searched MEDLINE, EMBASE, and CINAHL for studies published between 1946 and 7 September 2023. The primary outcome was energy expenditure. Included studies were published in English and measured one or more of TEE, AEE, GEE, and TEF with Indirect Calorimetry or Doubly Labeled Water in participants between 1 month and 18 years of age. We excluded studies reporting only REE or using predictive equations. Following abstraction, reported values were converted into kcal/kg/day or kcal/day as possible. Weighted mean values were calculated using median or means of EE measurements. We found 138 studies, 8163 patients, and 16,636 eligible measurements. The median (IQR) study sample size was 20 (12, 35) patients. TEE was the most evaluated component. The median (IQR) TEE in infants was 73.1 (67.0, 76.5), in children 78.0 (66.0, 81.3), and in adolescents was 44.2 (41.8, 51.9) kcal/kg/day. Very few studies reported on GEE and TEF. This is one of the first studies that summarizes components of total energy expenditure in different pediatric age groups in healthy and critically ill children. Growth- and feeding-associated energy expenditure are poorly reported in healthy children, while all components of TEE (except REE) are poorly reported in critically ill children.

Ultra-wideband microwave precisely and accurately predicts sheepmeat hot carcase GR tissue depth

A portable ultra-wideband microwave system (MiS) coupled with an antipodal slot Vivaldi patch antenna (VPA) was used as an objective measurement technology to predict sheep meat carcase GR tissue depth, tested against AUS-MEAT national accreditation standards. Experiment one developed the MiS GR tissue depth prediction equation using lamb carcasses (n = 832) from two slaughter groups. To create the prediction equations, a two layered machine learning stacking ensemble technique was used. The performance of this equation was tested within the dataset using a k-fold cross validation (k = 5), which demonstrated excellent precision and accuracy with an average R2 of 0.91, RMSEP 2.11, bias 0.39 and slope 0.03. Experiment two tested the prediction equation against the AUS-MEAT GR tissue depth accreditation framework which stipulates predictions from a device must assign the correct fat score, with a tolerance of ±2 mm of the score boundary, and 90% accuracy. For a device to be accredited three measurements captured within the same device, as well as measurements across three different devices, must meet the AUS-MEAT error thresholds. Three MiS devices scanned lamb carcases (n = 312) across three slaughter days. All three MiS devices met the AUS-MEAT accreditation thresholds, accurately predicting GR tissue depth 96.1–98.4% of the time. Between the different devices, the measurement accuracy was 99.4–100%, and within the same device, the measurement accuracy was 99.7–100%. Based on these results MiS achieved AUS-MEAT device accreditation as an objective technology to predict GR tissue depth.

Relationship between surgical difficulty and postoperative complications of hand-assisted laparoscopic living donor nephrectomy and establishment of prediction model

BackgroundFew studies have systematically explored the factors influencing the difficulty of hand-assisted laparoscopic living donor nephrectomy. To investigate the relationship between the difficulty of hand-assisted laparoscopic living donor nephrectomy and postoperative complications of the donor as well as the recipient, and then build a model for predicting the difficulty of surgery.MethodsIn this study, 60 patients who underwent hand-assisted laparoscopic living donor nephrectomy by the same surgeon from September 2022 to March 2024 were included as the modeling group. 20 patients operated on by another surgeon served as the external validation group. The subjective score (1–3 points) of surgical difficulty was used as the quantitative index of surgical difficulty. Pearson and Spearman correlation tests were used to explore the correlation between preoperative data and surgical difficulty scores of kidney donors, and finally built a prediction model through multiple linear regression analysis.ResultsWith the increase in the difficulty of operation, both donors and recipients’ complications were increased. Linear regression analysis showed that only the number of renal arteries, visceral fat thickness and MAP score were independent risk factors for the difficulty of hand-assisted laparoscopic living donor nephrectomy. The prediction equation is as follows: Difficulty score = 0.584*Number of renal arteries + 0.731*MAP score + 0.110*visceral fat thickness.ConclusionsDonors with higher surgical difficulty are more likely to have serious complications after surgery as well as the recipient. We also established a reliable prediction model for the difficulty of hand-assisted laparoscopic donor nephrectomy.

Numerical Simulation of Vertical Cyclic Responses of a Bucket in Over-Consolidated Clay

Multi-bucket foundations have become an alternative for large offshore wind turbines, with the expansion of offshore wind energy into deeper waters. The vertical cyclic loading–displacement responses of the individual bucket of the tripod foundation are relevant to the deflection of multi-bucket foundations and crucial for serviceability design. Finite element analyses are used to investigate the responses of a bucket subjected to symmetric vertical cyclic loading in over-consolidated clay. The Undrained Cyclic Accumulation Model (UDCAM) is adopted to characterize the stress–strain properties of clay, the parameters of which are calibrated through monotonic and cyclic direct simple shear tests. The performance of the finite element (FE) model combined with UDCAM in simulating vertical displacement amplitudes is evaluated by comparison with existing centrifuge tests. Moreover, the impact of the bucket’s aspect ratio on vertical displacement amplitude is investigated through a parametric study. A predictive equation is proposed to estimate the vertical displacement amplitudes of bucket foundations with various aspect ratios, based on the cyclic displacement amplitude of a bucket with an aspect ratio of unity.

A novel scoring system based on magnetic resonance imaging for the prediction of the difficulty of ultrasound-guided high-intensity focused ultrasound ablation for uterine fibroids

Objective To develop a novel scoring system based on magnetic resonance imaging (MRI) for predicting the difficulty of ultrasound-guided high-intensity focused ultrasound (USgHIFU) ablation for uterine fibroids. Materials and methods A total of 637 patients with uterine fibroids were enrolled. Sonication time, non-perfused volume ratio (NPVR), and ultrasound energy delivered for ablating 1 mm3 of fibroid tissue volume (E/V) were each classified as three levels and assigned scores from 0 to 2, respectively. Treatment difficulty level was then assessed by adding up the scores of sonication time, NPVR and E/V for each patient. The patients with score lower than 3 were categorized into low difficulty group, with score equal to or greater than 3 were categorized into high difficulty group. The potential predictors for treatment difficulty were compared between the two groups. Multifactorial logistic regression analysis model was created by analyzing the variables. The difficulty score system was developed using the beta coefficients of the logistic model. Results Signal intensity on T2WI, fibroid location index, largest diameter of fibroids, abdominal wall thickness, homogeneity of the signal of fibroids, and uterine position were independent influencing factors for the difficulty of USgHIFU for uterine fibroids. A prediction equation was obtained: difficulty score = 17 × uterine position (anteverted =0, retroverted =1)+71 × signal intensity (hypointense = 0, isointense/hyperintense = 1) +8 × enhancement (homogenous = 0, heterogeneous = 1)+25×(largest diameter of fibroids-20) +35 × (fibroid location index −0.2) +1×(abdominal wall thickness −5). Conclusions This scoring system established based on MRI findings can be used to reliably predict the difficulty level of USgHIFU treatment of uterine fibroids.

Determination and prediction of the net energy content of wheat bran for pregnant sow

ABSTRACT Two experiments were conducted to determine net energy (NE) values of wheat bran ingredients and develop a prediction equation for NE of wheat bran. In each experiment, 12 multiparous pregnant sows were allocated to two 3 × 6 Youden squares with three consecutive periods and six diets in each square. The study consisted of six diets, including a corn-soybean meal basal diet and five diets formulated with 29.2% wheat bran. Each period lasted for 10 d, with 5 d allocated for adaptation and followed by 5 d for heat production measurement. Sows were provided feed at 604 kJ/kg BW0.75·d−1. On day 10, sows underwent fasting to measure fasting heat production. Results indicated that the inclusion of wheat bran in the diets significantly reduced digestibility of energy and nutrients in (p < 0.05). The average net energy (NE) content of wheat bran was determined to be 8.8 MJ/kg DM. A regress equation NE = 7.968 + 0.28 × CP + 0.607 × EE − 0.782 × ash − 0.05 × hemicellulose (R2 = 0.98, p < 0.01) was found to accurately predit the NE value when feeding pregnant sows with wheat bran-based diets. In conclusion, the net energy content of wheat bran fed to pregnant sows ranged from 7.24 to 10.67 MJ/kg DM and can be effectively estimated using proximate analysis methods.

Prediction of Boiling Heat Transfer Coefficient for Micro-Fin Using Mini-Channel

The prediction of the heat transfer coefficient commonly relies on the development of new empirical prediction equations when operating conditions and refrigerants change from the existing equations. Creating new prediction equations is expensive and time-consuming; therefore, recent attention has been given to machine learning approaches. However, machine learning requires a large amount of data, and insufficient data can result in inadequate accuracy and applicability. This study showed that using mini-channel data as highly relevant data for the micro-fin heat transfer coefficient yields high prediction accuracy, even when the experimental dataset of interest is limited. In the proposed method, we added dimensionless numbers assumed to significantly influence heat transfer coefficients calculated from experimental data to the training dataset. This allowed efficient learning of the characteristics of thin liquid films present in mini-channels and micro-fins. By combining distinctive physical mechanisms related to heat transfer coefficients with DNN/GPR/Fine-tuning, the proposed method can predict 96.7% of the data points within ±30% deviation. In addition, it has been confirmed that the dryout quality and post-dryout heat transfer coefficients were predicted with high accuracy. Additionally, we utilized visualization techniques to investigate the contents of the black-box machine learning models.

Characterization of damage to steel bridge girders subjected to over-height vehicle collision

Bridge structures that span roads and highways are often at the risk of over-height vehicle collision. Such collisions can cause structural damage to bridge girders, adversely affecting the bridge's safety and functionality, while posing an immediate hazard to traveling motorists. To ensure the structural integrity of the bridge girders, it is imperative to evaluate them in response to the loading demand induced by over-height collisions. However, current specifications and codes do not have the details necessary to predict the impact forces that steel bridge girders can experience during such collisions. To address this gap, a comprehensive investigation was conducted in this study to (i) determine impact force characteristics, (ii) evaluate various impact-induced structural response measures, and (iii) identify design parameters in need of attention to minimize the extent of impact-induced damage to steel girders. For this purpose, nonlinear finite-element models were developed and validated with experimental tests and field surveys. The structural performance of the steel bridge girders was then investigated, in terms of damage patterns, girder displacements and rotations, shear forces and bending moments, and impact forces, during a range of over-height collision scenarios. Based on the obtained results, predictive equations were developed to estimate the mean impact force. This was to facilitate the impact-resistant design of steel girders commonly used in bridge structures.

A Cost-Effective Approach to Creating Large Silicone Rubber Molds Using Advanced Rigid Polyurethane Foam.

In practical applications, polyurethane (PU) foam must be rigid to meet the demands of various industries and provide comfort and protection in everyday life. PU foam components are extensively used in structural foam, thermal insulation, decorative panels, packaging, imitation wood, and floral foam, as well as in models and prototypes. Conventional technology for producing PU foam parts often leads to defects such as deformation, short shots, entrapped air, warpage, flash, micro-bubbles, weld lines, and voids. Therefore, the development of rigid PU foam parts has become a crucial research focus in the industry. This study proposes an innovative manufacturing process for producing rigid PU foam parts using silicone rubber molds (SRMs). The deformation of the silicone rubber mold can be predicted based on its wall thickness, following a trend equation with a correlation coefficient of 0.9951. The volume of the PU foam part can also be predicted by the weight of the PU foaming agent, as indicated by a trend equation with a correlation coefficient of 0.9824. The optimal weight ratio of the foaming agent to water, yielding the highest surface hardness, was found to be 5:1. The surface hardness of the PU foam part can also be predicted based on the weight of the water used, according to a proposed prediction equation with a correlation coefficient of 0.7517. The average surface hardness of the fabricated PU foam part has a Shore O hardness value of approximately 75. Foam parts made with 1.5 g of water added to 15 g of a foaming agent have the fewest internal pores, resulting in the densest interior. PU foam parts exhibit excellent mechanical properties when 3 g of water is added to the PU foaming agent, as evidenced by their surface hardness and compressive strength. Using rigid PU foam parts as a backing material in the proposed method can reduce rapid tool production costs by about 62%. Finally, an innovative manufacturing process for creating large SRMs using rigid PU foam parts as backing material is demonstrated.

Prognostic Equations and Accuracy of a Total Score of Functional Independence Measure at Discharge for Different Diseases in a Convalescent Rehabilitation Ward.

Prognosisand goal setting from admission in the convalescent rehabilitation ward, supported by a multidisciplinary team, enhance rehabilitation and discharge support. Predicting functional independence measure (FIM) outcomes can further optimize these processes. This study aimed to develop prognostic equations for the motor FIMat discharge for stroke, hip fracture (HF), vertebral compression fractures (VCFs), and total knee arthroplasty (TKA), which are common diseases in patients admitted to convalescent rehabilitation wards, using multiple regression analysis, and to clarify the difference in the accuracy of the predicted motor FIM according to the disease. This study included 965 patients admitted to our hospital. The objective variable consists of the motor FIM at discharge, and the explanatory variables were age, sex, days from onset to admission, total admission motor FIM, and total admission cognitive FIM. A stepwise multiple regression analysis was performed. The analysis of the difference in the accuracy of predicted motor FIM by disease used the absolute value of the residuals. The total motor FIM and cognitive FIM at admission were extracted for all four diseases included in this study. The absolute value of the residuals appeared to be more accurate for TKA, HF, stroke, and VCF in that order. Although differences in the accuracy of the prediction equation were observed by disease, this prediction equation can be used as an approach to review the details of rehabilitation and discharge and can be tailored to each case.

Measured and predicted resting metabolic rate in patients with inflammatory bowel disease

ObjectiveThe present study aimed to compare measured and estimated resting metabolic rate (RMR) predicted by selected equations in patients with nonactive inflammatory bowel disease (IBD) on an outpatient university clinic regimen. Research Methods & ProceduresSeventy-two adult (≥20 years) IBD patients (45 with Crohn's disease-CD) had RMR measured (mRMR) by indirect calorimetry and also estimated by predictive equations (Cunningham, Henry, Anjos et al., and Marra et al.). Body composition was assessed by DXA. Absolute Bias (estimated - mRMR) and % Bias (Bias/mRMR) were calculated. Agreement was assessed as the limit of agreement (LoA) in the Bland & Altman approach. ResultsThere was no difference in age, body composition and mRMR between individuals with CD (5414.2 ± 1023.7 kJ/day) and ulcerative colitis (5443.9 ± 1008.9 kJ/day). Among the equations, only the Anjos et al.'s population-specific equation (-52.1 [642.0] kJ/day, P = 0.493; LoA: -1311; 1206 kJ/d) accurately estimated RMR. The equations of Marra et al. produced the highest % Bias (24.1 ± 14.8%). The Bland & Altman plots showed that the range of the LoA was relatively similar for all equations. In the simple regression analysis, the model with FFM resulted in a higher coefficient of determination (R2 = 0.51 for DC 0.74 for UC) compared to the model that included BM (R2 = 0.35 for DC and 0.65 for UC). ConclusionsAmong the equations analyzed, only Anjos et al.'s accurately estimated RMR in outpatients with nonactive IBD. However, caution is advised when applying it at the individual level, due to the wide observed LoA.

Critical quality indicators of high-performance polyetherimide (ULTEM) over the MEX 3D printing key generic control parameters: Prospects for personalized equipment in the defense industry

Additive Manufacturing (AM) can provide customized parts that conventional techniques fail to deliver. One important parameter in AM is the quality of the parts, as a result of the material extrusion 3D printing (3D-P) procedure. This can be very important in defense-related applications, where optimum performance needs to be guaranteed. The quality of the Polyetherimide 3D-P specimens was examined by considering six control parameters, namely, infill percentage, layer height, deposition angle, travel speed, nozzle, and bed temperature. The quality indicators were the root mean square (Rq) and average (Ra) roughness, porosity, and the actual to nominal dimensional deviation. The examination was performed with optical profilometry, optical microscopy, and micro-computed tomography scanning. The Taguchi design of experiments was applied, with twenty-five runs, five levels for each control parameter, on five replicas. Two additional confirmation runs were conducted, to ensure reliability. Prediction equations were constructed to express the quality indicators in terms of the control parameters. Three modeling approaches were applied to the experimental data, to compare their efficiency, i.e., Linear Regression Model (LRM), Reduced Quadratic Regression Model, and Quadratic Regression Model (QRM). QRM was the most accurate one, still the differences were not high even considering the simpler LRM model.

The Causes of Platelet Aggregation in Version 6.4 Trima Accel Automated Blood Collection System and the Comparison of Two Intervention Measures

To explore the causes of platelet aggregation in version 6.4 Trima Accel automated blood collection system and the effect of 2 intervention measures. The data on platelet aggregation (n=61) and non-aggregation (n=323) of 61 donors in 2020 were collected and the causes of aggregation were analyzed. Then the 72 donors with platelet aggregation in 2021 were randomized into intervention group A (increasing the anticoagulant-to-blood ratio) and intervention group B (wrapping the donor's arm with an electric blanket to keep warm and improve the blood flow speed). The collection time, average blood flow speed, number of machine alarms, anticoagulant usage, deaggregation and citrate reaction of the two groups were compared. Platelet aggregation was negatively correlated with the average blood flow speed (r =-0.394) and positively correlated with the collection time (r =0.458). The equations for predicting aggregation and non-aggregation were constructed based on Bayesian and Fisher discriminant analysis, and the predicted accuracy was 77.1%. The comparison of the effects of two intervention measures showed that the average blood flow speed in group B was higher than that in group A; the collection time, number of machine alarms, anticoagulant usage and proportion of citrate reaction in blood donors in group B were all lower than those in Group A, all these differences were significant (P < 0.05). In the entire cohort in 2021, 90.28% of the products were immediately deaggregated after collection, and 9.72% of the products were deaggregated within 4 hours. There was no statistically significant difference in deaggregation between the two intervention groups (P >0.05). During apheresis platelet collection, the predictive equations for aggregation and non-aggregation can be used to predict the occurrence probability of aggregation, and the intervention can be made in advance. Both intervention measures are effective in reducing platelet aggregation, however, measure B has the advantages of improving the speed of blood collection, shortening the collection time, reducing the alarm frequency and the anticoagulant usage, and reducing the incidence of citrate reaction in blood donors.

To compare horizontal strabismus deviation as assessed from photographs with that in the strabismus clinic using the prism bar.

To compare the deviation in cases of horizontal strabismus as assessed from photographs with the measurements as obtained in the strabismus clinic. After obtaining informed consent, we recruited subjects with manifest horizontal strabismus. We took a frontal flash photograph from a distance of 50 cm using smart-phone-based cameras with the flash light vertically aligned with the lens. After projecting the photograph on a laptop and using a vernier caliper, we measured the horizontal corneal diameter of the non-strabismic eye and the decentration of reflex in the strabismic eye taking limbus as the reference point. We converted these values to degrees by using a conversion factor of 7.5°/mm and further to prism diopters (PD) by the standard mathematical formula 100*tanθ. We included 74 subjects aged between 5 and 40 years with manifest horizontal deviation from 20 to 85 PD. We found a statistically significant correlation of 82.6% (P value < 0.001) between the clinic and photographic measurements. Agreement analysis suggested that the photographic measurements measured on average 7 PD less (95% confidence interval: 4.6 to 9.2) than clinical measurements along all values of misalignment, although the difference between the two methods decreased as the quantum of deviation increased. Linear regression revealed an r2 of 68% and provided a predictive equation to derive clinic equivalent measurements from photographic estimates. We believe our simple method provides robust evidence that a photographic estimation can provide the basic information of the size of the deviation to plan possible surgeries, especially in situations of a tele-consultation. This is an easy approach to both understand and master and should form the armamentarium of most orthopticians and strabismologists.

Prediction model of grinding roughness for worm with complicated space tooth surface

Optimum grinding parameters are crucial for achieving high-quality grinding of helical tooth surfaces for worm drives. The complex coupling motion relationship and formation mechanism between the worm blank and the grinding head make it difficult to predict the effect of grinding parameters on tooth surface roughness. Therefore, we developed a prediction model for the worm tooth surface roughness under conjugate grinding and applied it to the Roller Enveloping Worm Reducer (REWR). The forming process of the theoretical worm tooth surface during the grinding process was modeled by using the conjugate meshing principle. Considering the plowing and cutting effects of grinding particles on the tooth surface at different design parameters, feed speeds, and rotational speeds of the grinding head, the roughness prediction equation was established based on the theory of the maximum depth of valley bottom. Through the coupling analysis of the theoretical tooth surface model and the roughness prediction model, the prediction of the worm tooth surface roughness is realized. Based on the validated model, the influences of grinding parameters on the worm tooth surface roughness of REWR were quantitatively discussed. This study is of great significance for optimizing process parameters in the grinding of complex spatial surface transmissions.

A Prediction Model for Assessing the Efficacy of Thermal Ablation in Treating Benign Thyroid Nodules ≥ 2 cm: A Multi-Center Retrospective Study

ObjectivesTo develop and validate a prediction model utilizing clinical and ultrasound (US) data for preoperative assessment of efficacy following US-guided thermal ablation (TA) in patients with benign thyroid nodules (BTNs) ≥ 2 cm. Materials and methodsWe retrospectively assessed 962 patients with 1011 BTNs who underwent TA at four tertiary centers between May 2018 and July 2022. Ablation efficacy was categorized into therapeutic success (volume reduction rate [VRR] > 50%) and non-therapeutic success (VRR ≤ 50%). We identified independent factors influencing the ablation efficacy of BTNs ≥ 2 cm in the training set using multivariate logistic regression. On this basis, a prediction model was established. The performance of model was further evaluated by discrimination (area under the curve [AUC]) in the validation set. ResultsOf the 1011 nodules included, 952 (94.2%) achieved therapeutic success at the 12-month follow-up after TA. Independent factors influencing VRR > 50% included sex, nodular composition, calcification, volume, and largest diameter (all p < 0.05). The prediction equation was established as follows: p = 1/1 + Exp∑[8.113 −2.720 × (if predominantly solid) −2.790 × (if solid) −1.275 × (if 10 mL < volume ≤ 30mL) −1.743 × (if volume > 30 mL) −1.268 × (if with calcification) −2.859 × (if largest diameter > 3 cm) +1.143 × (if female)]. This model showed great discrimination, with AUC of 0.908 (95% confidence interval [CI]: 0.868–0.947) and 0.850 (95% CI: 0.748–0.952) in the training and validation sets, respectively. ConclusionsA clinical prediction model was successfully developed to preoperatively predict the therapeutic success of BTNs larger than 2 cm in size following US-guided TA. This model aids physicians in evaluating treatment efficacy and devising personalized prognostic plans.

Correlation between tooth decay and insulin resistance in normal weight males prompts a role for myo-inositol as a regenerative factor in dentistry and oral surgery: a feasibility study.

In an era of precision and stratified medicine, homogeneity in population-based cohorts, stringent causative entry, and pattern analysis of datasets are key elements to investigate medical treatments. Adhering to these principles, we collected in vivo and in vitro data pointing to an insulin-sensitizing/insulin-mimetic effect of myo-inositol (MYO) relevant to cell regeneration in dentistry and oral surgery. Confirmation of this possibility was obtained by in silico analysis of the relation between in vivo and in vitro results (the so-called bed-to-benchside reverse translational approach). Fourteen subjects over the 266 screened were young adult, normal weight, euglycemic, sedentary males having normal appetite, free diet, with a regular three-times-a-day eating schedule, standard dental hygiene, and negligible malocclusion/enamel defects. Occlusal caries were detected by fluorescence videoscanning, whereas body composition and energy balance were estimated with plicometry, predictive equations, and handgrip. Statistically significant correlations (Pearson r coefficient) were found between the number of occlusal caries and anthropometric indexes predicting insulin resistance (IR) in relation to the abdominal/visceral fat mass, fat-free mass, muscular strength, and energy expenditure adjusted to the fat and muscle stores. This indicated a role for IR in affecting dentin reparative processes. Consistently, in vitro administration of MYO to HUVEC and Swiss NIH3T3 cells in concentrations corresponding to those administered in vivo to reduce IR resulted in statistically significant cell replication (ANOVA/Turkey tests), suggesting that MYO has the potential to counteract inhibitory effects of IR on dental vascular and stromal cells turnover. Finally, in in silico experiments, quantitative evaluation (WOE and information value) of a bioinformatic Clinical Outcome Pathway confirmed that in vitro trophic effects of MYO could be transferred in vivo with high predictability, providing robust credence of its efficacy for oral health. Our reverse bed-to-benchside data indicate that MYO might antagonize the detrimental effects of IR on tooth decay. This provides feasibility for clinical studies on MYO as a regenerative factor in dentistry and oral surgery, including dysmetabolic/aging conditions, bone reconstruction in oral destructive/necrotic disorders, dental implants, and for empowering the efficacy of a number of tissue engineering methodologies in dentistry and oral surgery.

Development and use of semi-empirical spectral ground motion models for GPP-induced micro-earthquakes in Southern Germany

This study provides a comprehensive exploration of ground motions associated with micro-earthquakes induced by geothermal power plants (GPP) in Southern Germany and proposes corresponding ground motion prediction equations (GMPE). Initiating with a statistical analysis of recorded seismic data from the GPP in Insheim, the study is extended to the greater Munich area. For the latter, the scarce recorded data are merged with physics-based simulation data. The recorded data in Insheim, Poing, Unterhaching and the simulated data in Munich are compared to existing GMPEs for GPP-induced events, highlighting the need of new region-specific prediction equations. The proposed GMPEs are expressed in terms of peak quantities, spectral accelerations and velocities, separating the horizontal and vertical direction. The regression curves exhibit a good alignment with both recorded and simulated data, within an acceptable range. Notably, the results reveal higher spectral quantities at shorter periods (<0.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<0.1$$\\end{document} s), underscoring the importance of this characteristic in seismic assessment. The article shows an exemplary application for a low-rise residential building, located at a hypocentral distance of 3 km. While the building meets serviceability standards for an MW\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_W$$\\end{document} up to 2.5, the verification fails at MW=3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_W=3$$\\end{document}, emphasizing the need for robust risk assessment. These findings contribute to the understanding of ground motions of GPP-induced events, offering practical implications for serviceability verifications and aiding informed decision-making in geothermal energy projects.Graphical abstract

Utilising fatty acid prediction equations and mid‐infrared spectroscopy to estimate the seasonality changes in bovine milk from pasture‐based systems

The aim of this study was to utilise mid‐infrared spectroscopy with fatty acid (FA) predictions to characterise the seasonality‐associated changes to the FA profile of milk from Irish spring‐calving pasture‐based dairy cow production systems. A total of 546 454 individual milk records were collected from the Years 2015 to 2020, from 264 003 cows in 2400 commercial dairy farms. Significant changes occur to the predicted FA profile of milk across the year, such as increases in unsaturated FAs including C18:1 n‐9 until April. This can be associated with the increasing proportion of fresh pasture in the cows' diet, and a decrease between September and October as the cows begin to transition indoors for the winter period. The opposite trend was noted for saturated FAs and in particular C16:0, where proportions decreased until April and increased between September and November. Milk produced between April and September also coincided with the lowest spreadability index, indicating a more beneficial milk FA profile for more spreadable butter texture during this period. This work provides a valuable reference resource at a national level of the FA profile of milk from pasture‐based spring‐calving dairy systems.

Development of a new equation and validation of earlier resting energy expenditure predicting equations in adults living in Tehran.

Predictive equations have been considered as a practical approach for estimating resting energy expenditure (REE) across multiple populations, but their accuracy for each community remains to be determined. Thus, the purposes of this study were to determine the validity of REE predictive equations and to develop a new REE predictive equation in adults living in Tehran. The study included 284 subjects (158 females) aged 18-60 years old from two cross-sectional studies conducted in Tehrani populations. Anthropometric measurements were assessed using standard protocols. REE was measured using indirect calorimetry (IC) and was estimated using preexisting equations. A new equation was also developed based on the REE from IC and variables such as age, sex, height, and weight. Measured REE was compared to new equation and preexisting predictive equations via correlation, linear regression, and Bland-Altman tests. The new equation and the equations by Mifflin-St. Jeor, Livingston, Frankenfield, Nichols, Müller, and Ganpule demonstrated the best predictive value at a group level (mean percentage error=-2.2 to 2.4 %). At an individual level, the new equation and the equations by Mara, Frankenfield, Roza, Nikooyeh, and Harris & Benedict showed the greatest accuracies compared to measured REE (accuracy prediction=50-53%). This study highlights the importance of considering race when predicting REE. It also demonstrates that the newly developed equation is more appropriate in a clinical setting at group but not individual level. Thus, further research is needed to examine the new equation in an independent sample.