New Balance (United States)

HISTORY: 16 yr old female x-country runner presents early fall with right foot pain that developed after rapidly building mileage from 0 to 30 miles/week over 2 weeks. Pain felt at forefoot and worse with impact, complicated by an infected ingrown toenail to the right great toe, with mother noting a change in gait. Athlete reached menarche the week of consult and is working on increasing dietary intake. She had a prior metatarsal bone stress injury one year prior. PHYSICAL EXAMINATION: Orthostatic vitals: Lying - BP 120/75, HR 64. Standing - BP 115/70, HR 88. Weight: 18th percentile for age, BMI: 11th percentile for age (BMI 17.57 kg/m2); BMI 0.3-2.11% from 12-15yo. Growth velocity: 3.45 cm/year. Right foot: Onychocryptosis without active infection. Tenderness at 2nd metatarsal. Single leg hop: pain. Remainder of exam unremarkable. DIFFERENTIAL DIAGNOSIS: Overall: 1.Relative energy deficiency in sport (REDs) 2.Constitutional delay of growth and puberty Foot pain: 1.Metatarsal bone stress injury 2.Freiberg infraction Secondary concerns: 1.Inappropriate escalation of running volume 2.Running gait biomechanics TEST AND RESULTS: 1.MRI right foot: •2nd metatarsal stress fracture 2.Bone age: •Chronologic age: 16 yr, 1 mo. 1 SD: 7 mo. •Bone age: 13.5 yr, in keeping with 'delayed’ bone age of below 2 SD of chronologic age. 3.DXA: •Z-scores: Spine: -0.5; Total Hip: -0.7; Femoral Neck: -1.0; Total Body % fat: -0.7. •Improved Z-scores compared to DXA 2 years prior. 4.Lab RESULTS:•CBC: WBC 7.35, Hgb 14.1, Ptl 243 •CMP: WNL •Iron studies: Iron 140, Transferrin 240, TIBC 336, Ferritin 37.2 •25-OH vit D: 40.0 •Endocrine:oThyroid: TSH 2.220 mcunit/mL, total T3 105 ng/dL oGonadal: Estradiol 48 pg/mL, FSH 5.30 IU/L, LH 5.63 IU/L FINAL WORKING DIAGNOSIS: 1.Constitutional delay; normal BMD when adjusted for bone age 2.REDs 3.2nd metatarsal stress fracture 4.Altered running gait secondary to onychocryptosis 5.Inappropriate escalation of running volume TREATMENT AND OUTCOMES: 1.Immobilization in walking boot x3w, transition to supportive footwear. 2.PT guided progression: low impact, then return to run program. 3.Nutrition optimization with RD. 4.Discussed maximal weekly mileage, slower ramp up. 5.Ongoing surveillance of menstrual cycles, appropriate growth and development. Supported by: Research funding: Wu Tsai Human Performance Alliance

Advantages of using energy cost (EC) over oxygen cost (OC) to describe running economy (RE) include greater validity when substrate utilization is altered and greater sensitivity when interventions produce small differences. Single attributes of advanced footwear technology (AFT) that improve RE have not been systematically explained and any attribute is likely to contribute small effects. PURPOSE: The aim of this study was to compare the effects of single AFT shoe attributes on RE using both EC and OC quantification methods. METHODS: Experienced male runners (n = 12) completed 8 x 5-minute blocks of treadmill running at ~70% of estimated critical speed (RER = 0.87 ± 0.04) in four bespoke, mass adjusted AFT shoes. A prototype control (P) was compared to three other prototypes differing in resilience (R), compliance (C) or stack height (S; with additional small differences in geometry and plate). Shoes were tested in randomized, mirrored order with a 1:1 work-to-rest ratio. RE was measured by open circuit spirometry using 20-s averages of gross VO2 and VCO2 over the final 2-minutes of steady-state running in each block. The mass-specific EC (kcal/kg/km; Brockway) or OC (ml/kg/km) per unit distance were each used to compare within-condition AFT prototype pairings. RESULTS: Main effects of shoe type on RE were found (RM ANOVA) and Dunnett’s post-hoc test revealed that lower AFT shoe compliance (P vs C) significantly worsened RE when measured as EC (10/12; 1.57%; p = 0.009) and OC (10/12; 1.21%; p = 0.004). The effects of lower AFT resilience (P vs R) were consistent but smaller and nonsignificant in both EC (10/12; 0.72%; p = 0.277) and OC (9/12; 0.69%; p = 0.265). Stack height (P vs S) resulted in nonsignificant and less consistent effects on RE for both EC (7/12; 0.60%; p = 0.470) and OC (9/12; 0.59%; p = 0.416). There was no effect of AFT shoe attributes on RER (p = 0.296). CONCLUSIONS: Changes in single AFT attributes of resilience and compliance produced small (0.69 to 1.57%), but consistent directional changes in RE on the individual level (75 to 83% of pairs, i.e., 9/12 to 10/12). RE differences greater than ~1% were detected (p < 0.05) equally using EC and OC under conditions when RER did not differ.

Abstract Buleleng Regency is the largest grape production center in Bali, Indonesia. In addition to planting local grapes, farmers have recently started cultivating many introduced grape varieties as raw material for wine production. In tropical areas, grapes can be harvested three times each year, unlike in subtropical areas where grapes only bear fruit once a year, so pests and other insects are often found throughout the year. This research aims to determine the diversity of insects, both pests and predatory insects, on four introduced grape varieties: Colombard, Cabernet blanc, Solaris and Chenin blanc. The research was carried out in April-June 2024 at the Hatten wines vineyard, Buleleng district, Bali. This research was conducted using a survey method with yellow sticky traps and direct observation. The traps were placed diagonally across all plots of green grape varieties. A total of 40 traps were installed. Observations were conducted (from beginning of flowering until flowering 50% cap off) once a day for 15 days (or before fruit emergence). The results of the study showed that the types of pests found were included in the Order Thysanoptera, Diptera, Coleoptera, Lepidoptera, Acari, and Neuroptera. The diversity index showed a low value and the dominance index value was moderate. Direct observations also revealed several pest insects that were not caught in the yellow sticky traps but were present in the field, such as army worms and grasshoppers. Chrysoperla carnea is a natural enemy that acts as a predator. The adult C. carnea consumes nectar or pollen and is not predatory, but its larval stage is predatory. In the larval stage, C. carnea is capable of attacking pest insects from the order Thrips, Aphids, Mites, moth larvae, and some larvae of beetles and fruit flies. C. carnea is polyphagous and therefore does not have specific prey.

superficies se ha diseñado un equipo para la determinación experimental del coeficiente de convección sobre una placa plana. Constructivamente hablando el equipo está formado por una lámina calefactora colocada sobre la cara superior de una placa de madera. Al conectar la lámina calefactora a la red, se consigue una temperatura superficial superior a la de la cara inferior de la placa de madera, a la del ambiente y a la de las superficies que le rodean. De esta manera se logra que exista un intercambio de calor por conducción a través de la placa, por convección con el aire y por radiación con las superficies circundantes. Los objetivos de la práctica son varios. Por un lado, se busca que el alumnado aplique el balance de energía e identifique los diferentes mecanismos de transmisión de calor que intervienen. Por otro lado, a partir de la medida de diversas temperaturas durante la práctica, el alumnado debe calcular el coeficiente de convección que tiene lugar en esas condiciones. Además, el alumnado debe resolver el intercambio de calor por convección como si fuese un problema de clase, usando las correlaciones de convección existentes en la bibliografía relativa a transferencia de calor. Por último se le pide que calcule la diferencia entre ambos coeficientes de convección de manera porcentual. Una ventaja añadida del equipo es que puede ser igualmente utilizado para la realización de prácticas relativas al mecanismo de radiación.

When lean people adopt carbohydrate-restricted diets (CRDs), they may develop a lipid profile consisting of elevated LDL-cholesterol (LDL-C) and HDL-cholesterol (HDL-C) with low triglycerides (TGs). The magnitude of this lipid profile correlates with BMI such that those with lower BMI exhibit larger increases in both LDL-C and HDL-C. The inverse association between BMI and LDL-C and HDL-C change on CRD contributed to the discovery of a subset of individuals—termed Lean Mass Hyper-Responders (LMHR)—who, despite normal pre-diet LDL-C, as compared to non-LMHR (mean levels of 148 and 145 mg/dL, respectively), exhibited a pronounced hyperlipidemic response to a CRD, with mean LDL-C and HDL-C levels increasing to 320 and 99 mg/dL, respectively, in the context of mean TG of 47 mg/dL. In some LMHR, LDL-C levels may be in excess of 500 mg/dL, again, with relatively normal pre-diet LDL-C and absent of genetic findings indicative of familial hypercholesterolemia in those who have been tested. The Lipid Energy Model (LEM) attempts to explain this metabolic phenomenon by positing that, with carbohydrate restriction in lean persons, the increased dependence on fat as a metabolic substrate drives increased hepatic secretion and peripheral uptake of TG contained within very low-density lipoproteins (VLDL) by lipoprotein lipase, resulting in marked elevations of LDL-C and HDL-C, and low TG. Herein, we review the core features of the LEM. We review several existing lines of evidence supporting the model and suggest ways to test the model’s predictions.

ObjectiveTo determine the effects of diets varying in carbohydrate to fat ratio on total energy expenditure.DesignRandomized trial.SettingMulticenter collaboration at US two sites, August 2014 to May 2017.Participants164 adults aged 18-65 years with a body mass index of 25 or more.InterventionsAfter 12% (within 2%) weight loss on a run-in diet, participants were randomly assigned to one of three test diets according to carbohydrate content (high, 60%, n=54; moderate, 40%, n=53; or low, 20%, n=57) for 20 weeks. Test diets were controlled for protein and were energy adjusted to maintain weight loss within 2 kg. To test for effect modification predicted by the carbohydrate-insulin model, the sample was divided into thirds of pre-weight loss insulin secretion (insulin concentration 30 minutes after oral glucose).Main outcome measuresThe primary outcome was total energy expenditure, measured with doubly labeled water, by intention-to-treat analysis. Per protocol analysis included participants who maintained target weight loss, potentially providing a more precise effect estimate. Secondary outcomes were resting energy expenditure, measures of physical activity, and levels of the metabolic hormones leptin and ghrelin.ResultsTotal energy expenditure differed by diet in the intention-to-treat analysis (n=162, P=0.002), with a linear trend of 52 kcal/d (95% confidence interval 23 to 82) for every 10% decrease in the contribution of carbohydrate to total energy intake (1 kcal=4.18 kJ=0.00418 MJ). Change in total energy expenditure was 91 kcal/d (95% confidence interval −29 to 210) greater in participants assigned to the moderate carbohydrate diet and 209 kcal/d (91 to 326) greater in those assigned to the low carbohydrate diet compared with the high carbohydrate diet. In the per protocol analysis (n=120, P<0.001), the respective differences were 131 kcal/d (−6 to 267) and 278 kcal/d (144 to 411). Among participants in the highest third of pre-weight loss insulin secretion, the difference between the low and high carbohydrate diet was 308 kcal/d in the intention-to-treat analysis and 478 kcal/d in the per protocol analysis (P<0.004). Ghrelin was significantly lower in participants assigned to the low carbohydrate diet compared with those assigned to the high carbohydrate diet (both analyses). Leptin was also significantly lower in participants assigned to the low carbohydrate diet (per protocol).ConclusionsConsistent with the carbohydrate-insulin model, lowering dietary carbohydrate increased energy expenditure during weight loss maintenance. This metabolic effect may improve the success of obesity treatment, especially among those with high insulin secretion.Trial registrationClinicalTrials.gov NCT02068885.

Despite intensive research, the causes of the obesity epidemic remain incompletely understood and conventional calorie-restricted diets continue to lack long-term efficacy. According to the carbohydrate-insulin model (CIM) of obesity, recent increases in the consumption of processed, high–glycemic-load carbohydrates produce hormonal changes that promote calorie deposition in adipose tissue, exacerbate hunger, and lower energy expenditure. Basic and genetic research provides mechanistic evidence in support of the CIM. In animals, dietary composition has been clearly demonstrated to affect metabolism and body composition, independently of calorie intake, consistent with CIM predictions. Meta-analyses of behavioral trials report greater weight loss with reduced-glycemic load vs low-fat diets, though these studies characteristically suffer from poor long-term compliance. Feeding studies have lacked the rigor and duration to test the CIM, but the longest such studies tend to show metabolic advantages for low-glycemic load vs low-fat diets. Beyond the type and amount of carbohydrate consumed, the CIM provides a conceptual framework for understanding how many dietary and nondietary exposures might alter hormones, metabolism, and adipocyte biology in ways that could predispose to obesity. Pending definitive studies, the principles of a low-glycemic load diet offer a practical alternative to the conventional focus on dietary fat and calorie restriction.

Few topics are of greater concern for public health, including the delivery of healthcare and the development of policy, than obesity and its myriad complications. Dramatic increases in the prevalence of obesity in high-income countries over the past 50 years (Fig. 1), and the increasing problem of obesity in low- and middle-income countries, are undisputed. The lifetime costs of a patient with obesity (compared with a patient with normal weight) underscore the societal impact of the problem (Fig. 2). However, causes of obesity and best strategies to reverse trends in prevalence of the disease are topics of much debate. The role of nutrition and genetics, impact of physical activity, neurohormonal drivers and mediators of obesity, and mechanisms that underpin the development of obesity-related complications all remain central to ongoing research. In this context, we believe that it is …