Chilean Market Protein Shakes Composition

Understanding the nutritional content of protein supplements is crucial for optimal nutritional planning among athletes and other people. Distribution of macronutrients and aminograms in the main products available in the national Chilean market remains unknown. A descriptive cross-sectional study was conducted to identify the main protein supplements available in the Chilean market. Information on macronutrients and aminograms from the nutritional labels of each product was extracted. The analysis considered the content per portion and per 100 g. Cluster analysis models and graphical representations were explored. Eighty protein shakes were assessed in the Santiago de Chile market. The median protein dosage was 32 g (range from 25 to 52), and the median energy value stood at 390 kcal (range from 312 to 514). The median protein content per 100 g of product was found to be 75 g (range from 42.5 to 97.2). The combined median concentration of amino acids was 4749.75 mg. Among these, the essential amino acid L-Tryptophan exhibited the lowest concentration at 1591.50 mg, while the conditional amino acid L-Glutamine had the highest median concentration at 17,336 mg. There was a significant prevalence of animal-derived products, placing specific emphasis on protein supplements that feature elevated levels of the amino acids L-Glutamine and L-Leucine.

Artificial intelligence in diagnosing upper limb musculoskeletal disorders: a systematic review and meta-analysis of diagnostic tests

Purpose

The integration of artificial intelligence (AI) in radiology has revolutionized diagnostics, optimizing precision and decision-making. Specifically in musculoskeletal imaging, AI tools can improve accuracy for upper extremity pathologies. This study aimed to assess the diagnostic performance of AI models in detecting musculoskeletal pathologies of the upper extremity using different imaging modalities.

Methods

A meta-analysis was conducted, involving searches on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The quality of the studies was assessed using the QUADAS-2 tool. Diagnostic accuracy measures including sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR, NLR), area under the curve (AUC), and summary receiver operating characteristic were pooled using a random-effects model. Heterogeneity and subgroup analyses were also included. All statistical analyses and plots were performed using the R software package.

Results

Thirteen models from ten articles were analyzed. The sensitivity and specificity of the AI models to detect musculoskeletal conditions in the upper extremity were 0.926 (95% CI: 0.900; 0.945) and 0.908 (95% CI: 0.810; 0.958). The PLR, NLR, lnDOR, and the AUC estimates were found to be 19.18 (95% CI: 8.90; 29.34), 0.11 (95% CI: 0.18; 0.46), 4.62 (95% CI: 4.02; 5.22) with a (P < 0.001), and 95%, respectively.

Conclusion

The AI models exhibited strong univariate and bivariate performance in detecting both positive and negative cases within the analyzed dataset of musculoskeletal pathologies in the upper extremity.

Nonsurgical Management of Shoulder Pain in Rotator Cuff Tears: Ultrasound-Guided Biceps Tenotomy Combined With Corticosteroid Injection

Traditionally, the management of rotator cuff tears in elderly individuals has involved surgical intervention, specifically biceps tenotomy. However, surgical procedures come with inherent medical risks and significant financial costs. As an alternative, ultrasound-guided biceps tenotomy combined with corticosteroid has emerged as a highly effective, well-tolerated, and cost-efficient option. This article aims to describe the procedural technique of nonsurgical management using ultrasound-guided biceps tenotomy combined with corticosteroid injection.

High-accuracy detection of supraspinatus fatty infiltration in shoulder MRI using convolutional neural network algorithms

Background

The supraspinatus muscle fatty infiltration (SMFI) is a crucial MRI shoulder finding to determine the patient's prognosis. Clinicians have used the Goutallier classification to diagnose it. Deep learning algorithms have been demonstrated to have higher accuracy than traditional methods.

Aim

To train convolutional neural network models to categorize the SMFI as a binary diagnosis based on Goutallier's classification using shoulder MRIs.

Methods

A retrospective study was performed. MRI and medical records from patients with SMFI diagnosis from January 1st, 2019, to September 20th, 2020, were selected. 900 T2-weighted, Y-view shoulder MRIs were evaluated. The supraspinatus fossa was automatically cropped using segmentation masks. A balancing technique was implemented. Five binary classification classes were developed into two as follows, A: 0, 1 v/s 3, 4; B: 0, 1 v/s 2, 3, 4; C: 0, 1 v/s 2; D: 0, 1, 2, v/s 3, 4; E: 2 v/s 3, 4. The VGG-19, ResNet-50, and Inception-v3 architectures were trained as backbone classifiers. An average of three 10-fold cross-validation processes were developed to evaluate model performance. AU-ROC, sensitivity, and specificity with 95% confidence intervals were used.

Results

Overall, 606 shoulders MRIs were analyzed. The Goutallier distribution was presented as follows: 0 = 403; 1 = 114; 2 = 51; 3 = 24; 4 = 14. Case A, VGG-19 model demonstrated an AU-ROC of 0.991 ± 0.003 (accuracy, 0.973 ± 0.006; sensitivity, 0.947 ± 0.039; specificity, 0.975 ± 0.006). B, VGG-19, 0.961 ± 0.013 (0.925 ± 0.010; 0.847 ± 0.041; 0.939 ± 0.011). C, VGG-19, 0.935 ± 0.022 (0.900 ± 0.015; 0.750 ± 0.078; 0.914 ± 0.014). D, VGG-19, 0.977 ± 0.007 (0.942 ± 0.012; 0.925 ± 0.056; 0.942 ± 0.013). E, VGG-19, 0.861 ± 0.050 (0.779 ± 0.054; 0.706 ± 0.088; 0.831 ± 0.061).

Conclusion

Convolutional neural network models demonstrated high accuracy in MRIs SMFI diagnosis.

A New Test for the Advanced Diagnosis of Lateral Elbow Tendinopathy with Concomitant Intrasubstance Tear: Failure to Resist Extension Effort (the Free Test)

Background

Lateral elbow tendinopathy (LET) is one of the most common causes of musculoskeletal pain. The diagnosis is based on the clinical history and different physical maneuvers. Ultrasound (US) is a complementary diagnostic method to detect degenerative tendon changes and intrasubstance tears (IST). To date, there is no available physical maneuver to identify an IST in patients with LET.

Aim

To evaluate the diagnostic accuracy of an index test to detect an IST confirmed by ultrasound in patients with LET.

Methods

A diagnostic retrospective study was performed. Patients who presented medical records with LET were recruited. Two orthopaedic surgeons developed the physical maneuver. The index test was considered positive when the position failed to resist the wrist extension maximum effort. Clinical findings were associated with confirmation of IST by US. Data were calculated using diagnostic accuracy, sensitivity, and specificity with 95% confidence intervals.

Results

Thirty-nine patients (39 elbows) were analyzed, 25 (64%) women and 14 (36%) men, with an average age of 47.7 years. The index test's sensitivity was 0.86 (95% CI, 0.67-0.96). Accuracy was 0.79 (95% CI, 0.64-0.91), and the specificity was 0.64 (95% CI, 0.31-0.89).

Conclusion

The index test presented very good sensitivity and good accuracy in patients with LET with US diagnostic confirmation of IST.

Level of Evidence

Diagnostic study, Level III.

The inverse relationship between fatness and bone mineral content is mediated by the adolescent appendicular skeletal muscle mass index: The Cogni-Action Project

Background

Excess adipose tissue negatively influences bone health during childhood, affecting future bone fragility diseases such as osteoporosis. However, little is known about how adolescent appendicular skeletal muscle mass index (ASMI) may mediate the relation between fatness and bone mineral content (BMC).

Methods

The sample comprised 1,296 adolescents (50% girls) aged 10-14. A principal component analysis was performed to obtain a factor made up of four fatness indicators (a) neck circumference, (b) kilograms of fat, (c) visceral fat area, and (d) waist-to-height ratio. BMC, kilograms of fat, visceral fat area, and appendicular skeletal muscle mass were obtained by a multi-frequency bioelectrical impedance analyzer. ASMI was calculated as the appendicular skeletal muscle mass divided by height squared (kg/m2). A mediation analysis was performed adjusting by age, sex, maturation, socioeconomic status, physical activity, and adolescents' body weight. We also explore differences by sex and nutritional status.

Results

The fatness factor explained 71.5% of the proportion variance. Fatness was inversely associated with the ASMI and BMC, while the ASMI was positively related to BMC. Overall, the inverse relationship between fatness and BMC was partially mediated by the adolescents' ASMI (29.7%, indirect effect: B= -0.048, 95%CI -0.077 to -0.022), being higher in girls than in boys (32.9 vs. 29.2%). Besides, the mediation effect was higher in adolescents with normal body weight than with overweight-obese (37.6 vs 23.9%, respectively).

Conclusions

This finding highlighted the relevance of promoting healthy habits to reduce fatness and improve muscle mass in adolescents. Moreover, this highlights the central role of ASMI mediating the inverse association between fatness and BMC in female and male adolescents. Public health strategies should promote bone health in childhood, reducing the incidence of early osteopenia and osteoporosis.

Lateral elbow tendinopathy and artificial intelligence: Binary and multilabel findings detection using machine learning algorithms

Background

Ultrasound (US) is a valuable technique to detect degenerative findings and intrasubstance tears in lateral elbow tendinopathy (LET). Machine learning methods allow supporting this radiological diagnosis.

Aim

To assess multilabel classification models using machine learning models to detect degenerative findings and intrasubstance tears in US images with LET diagnosis.

Materials and methods

A retrospective study was performed. US images and medical records from patients with LET diagnosis from January 1st, 2017, to December 30th, 2018, were selected. Datasets were built for training and testing models. For image analysis, features extraction, texture characteristics, intensity distribution, pixel-pixel co-occurrence patterns, and scales granularity were implemented. Six different supervised learning models were implemented for binary and multilabel classification. All models were trained to classify four tendon findings (hypoechogenicity, neovascularity, enthesopathy, and intrasubstance tear). Accuracy indicators and their confidence intervals (CI) were obtained for all models following a K-fold-repeated-cross-validation method. To measure multilabel prediction, multilabel accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) with 95% CI were used.

Results

A total of 30,007 US images (4,324 exams, 2,917 patients) were included in the analysis. The RF model presented the highest mean values in the area under the curve (AUC), sensitivity, and also specificity by each degenerative finding in the binary classification. The AUC and sensitivity showed the best performance in intrasubstance tear with 0.991 [95% CI, 099, 0.99], and 0.775 [95% CI, 0.77, 0.77], respectively. Instead, specificity showed upper values in hypoechogenicity with 0.821 [95% CI, 0.82, −0.82]. In the multilabel classifier, RF also presented the highest performance. The accuracy was 0.772 [95% CI, 0.771, 0.773], a great macro of 0.948 [95% CI, 0.94, 0.94], and a micro of 0.962 [95% CI, 0.96, 0.96] AUC scores were detected. Diagnostic accuracy, sensitivity, and specificity with 95% CI were calculated.

Conclusion

Machine learning algorithms based on US images with LET presented high diagnosis accuracy. Mainly the random forest model shows the best performance in binary and multilabel classifiers, particularly for intrasubstance tears.

Effects of skim milk and isotonic drink consumption before exercise on fluid homeostasis and time-trial performance in cyclists: a randomized cross-over study

BACKGROUND

Hydration status affects endurance performance. Pre-exercise hydration recommendations target the consumption of high carbohydrate and sodium beverages. Milk, due to its carbohydrate and sodium content, may be considered an effective pre-exercise hydration beverage.

PURPOSE

In a randomized cross-over trial, we compared the effects of an isotonic sport drink (SPD) with skim milk (SM) consumption before a race, on fluid homeostasis and time-trial performance in road cyclists.

METHODS

Male road cyclists (n = 9; age, 26.8 ± 4.78 years) with 10.8 ± 8.56 years of experience in national competitions, consumed either SPD or SM in doses of 350 mL at 3 h and 350 mL at 1.5 h before a 18.6 km time-trial race. Measurements of body mass, urine specific gravity (USG), urine color and time-trial were compared between drinks (group; g) before and after the race (time; t).

RESULTS

The two-way ANOVA showed no differences between SPD and SM in body mass (t, p < 0.0001; g, p = 0.89; t × g, p = 0.54), USG (t, p = 0.01; g, p = 0.63; t × g, p = 0.29) and urine color (t, p = 0.01; g, p = 0.54; t × g, p = 0.28) before or after race. Furthermore, no differences on water consumption during the race (p = 0.55) or time-trial performance (p = 0.84) were observed between trials.

CONCLUSION

Current results may help athletes with different beverages preferences to increase their options of hydration strategies.

Electrical Behavior of Copper Mine Tailings During EKR with Modified Electric Fields

Electro-kinetic remediation (EKR) with sinusoidal electric field obtained simultaneously with DC/AC voltage reduce the polarization of the EKR with DC voltage. The DC voltage value defines the presence of a periodic polarity reversal of the cell and the electrical charge for electro-kinetic transport. In this case, the AC frequency favors the breaking of polarization conditions resulting from the EKR with DC voltage. However, with high frequencies a negative effect occurs where the tailings behave as a filter circuit, discriminating frequencies of an electric signal. The goal of this work is to analyse the electrical behaviour of tailings in EKR experiments. The conditions selected were: DC/AC voltages: 10/15 and 20/25 V (peak values), and AC voltage frequencies 50-2000 Hz. When the AC frequency reaches 2000 Hz, the copper removal tends to zero, indicating that the tailing behaves as a high-pass filter in which the DC voltage was filtered out.

Effects of Beer, Non-Alcoholic Beer and Water Consumption before Exercise on Fluid and Electrolyte Homeostasis in Athletes

Fluid and electrolyte status have a significant impact on physical performance and health. Pre-exercise recommendations cite the possibility of consuming beverages with high amounts of sodium. In this sense, non-alcoholic beer can be considered an effective pre-exercise hydration beverage. This double-blind, randomized study aimed to compare the effect of beer, non-alcoholic beer and water consumption before exercise on fluid and electrolyte homeostasis. Seven male soccer players performed 45 min of treadmill running at 65% of the maximal heart rate, 45 min after ingesting 0.7 L of water (W), beer (AB) or non-alcoholic beer (NAB). Body mass, plasma Na⁺ and K⁺ concentrations and urine specific gravity (USG) were assessed before fluid consumption and after exercise. After exercise, body mass decreased (p < 0.05) in W (−1.1%), AB (−1.0%) and NAB (−1.0%). In the last minutes of exercise, plasma Na⁺ was reduced (p < 0.05) in W (−3.9%) and AB (−3.7%), plasma K⁺ was increased (p < 0.05) in AB (8.5%), and USG was reduced in W (−0.9%) and NAB (−1.0%). Collectively, these results suggest that non-alcoholic beer before exercise could help maintain electrolyte homeostasis during exercise. Alcoholic beer intake reduced plasma Na⁺ and increased plasma K⁺ during exercise, which may negatively affect health and physical performance, and finally, the consumption of water before exercise could induce decreases of Na⁺ in plasma during exercise.

[PREVALENCE OF DEHYDRATION BEFORE TRAINING IN PROFESIONAL CHILEAN SOCCER PLAYERS]

INTRODUCTION

there is a lack of studies concerning hydration status before training in professional soccer player.

PURPOSE

to describe hydration status before regular training practices in professional soccer players.

METHODS

a total of 156 male soccer players (age 25.4 ± 5.2 y) from six professional Chilean clubs were included. No hydration or food intake recommendations were made before experiment, with the aim to assess hydration status under athlete's regular "real" conditions. Body mass, height and urine specific gravity (USG) measurements were performed before training practices.

RESULTS

98% of athletes showed dehydration (between moderate and severe) before regular training practices.

DISCUSSION

dehydration is the most prevalent hydration status in professional Chilean soccer players before training, which may negatively affect athlete's performance and may increase their risk of heat-related injuries.