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Background Chylomicronemia syndrome with severe hypertriglyceridemia may be the result of genetic abnormalities, aggravating factors or a combination of both. Clinical case The patient is a 17-year-old overweight female (BMI of 90th percentile) with a history of type 1 diabetes mellitus (T1D) for eleven years, PCOS, vitamin D deficiency and hypertriglyceridemia. Her father has a history of hypertriglyceridemia that improves with dietary modifications. Her mother and younger sister (who also has T1D) have normal serum lipid levels. Although the patient has a history of poor compliance (HbA1c ranging between 9-11%), glucose control had been improving (HbA1c most recently between 8.1-8.6%). Her serum triglyceride level had been slightly abnormal for the past few years (200-300 mg/dL; normal range: < 90 mg/dL) and had increased to 413 mg/dL, despite the improvement in glucose control. She subsequently started taking omega-3, 1000 mg daily. At that time, she also began treatment with combined oral contraceptives (COCs) for PCOS. The serum triglyceride level three months later increased to 1,322 mg/dL. She consistently denied alcohol or recreational drug use. The omega-3 dose was increased to 4 gm daily. Atorvastatin 10 mg daily was also started and COCs were discontinued. Three weeks later, a repeat triglyceride level was found to be 2,813 mg/dL. Fenofibrate 160 mg daily was added. Atorvastatin was discontinued and she began a low-fat diet. After further questioning, patient admitted to heavy alcohol intake during the weekends. She reported specifically drinking grain alcohols to avoid carbohydrates. Three weeks after cessation of alcohol, her triglyceride level declined to 302 mg/dL. Clinical lesson: This patient had multiple risk factors for chylomicronemia syndrome including poorly controlled T1D, overweight and estrogen containing contraceptive therapy. Although alcohol abuse was a problem at the time hypertriglyceridemia worsened, the history of excessive alcohol consumption was not given until many other treatments had been initiated. This case highlights the importance of screening for alcohol abuse in patients with hypertriglyceridemia who do not respond to standard therapies, especially in adolescent patients who may be reticent about discussing alcohol abuse with their physicians. Presentation: Monday, June 13, 2022 12:30 p.m. - 2:30 p.m.

Background: There is limited scientific literature regarding gender non-conforming (GNC) youth in pediatric practice. GNC patients of all ages have an increased risk for psychiatric co-morbidities and suicidal risk. An increasing number of GNC youth are seeking therapeutic options to develop physical characteristics to match their gender identity. The study aim is to describe the prevalence of psychiatric co-morbidities and sexual orientation among GNC pediatric patients in a clinic-based setting in Miami, Florida. A secondary aim is to compare the grade of dysphoria before and after therapeutic interventions among GNC youth. Methods: This is a retrospective chart review on records from 2014–2019 among transgender adolescents attending a pediatric endocrinology clinic in Miami. Data on demographics and clinical characteristics were obtained from electronic medical records. We performed descriptive statistical analysis using SPSS version and reported frequencies and percentages. Results: A total of 158 patients were included in this analysis. There were 107 (67.7%) affirmed males (female to male), 47 (29.7%) affirmed females (male to female), and 4 (2.5%) considered themselves non-binary. Median age at onset of gender dysphoria symptoms and beginning of social affirmation was earlier in affirmed females (7.21 and 12.36 years vs. 9.65 and 13.50 years). Among affirmed males, sexual orientation was self-reported as 38% straight, 47% bisexual, 12% homosexual and 1% asexual vs. among affirmed females, whose sexual orientation was reported as 54.3% straight, 37.1% bisexual and 8.6% homosexual. Prevalence of psychiatric co-morbidities in our study population was 78.5%. Depression was the most frequent diagnosis (66.5%), followed by anxiety (33.5%), attention deficit hyperactivity disorder (10.1%), bipolar disorder (1.9%), bulimia (1.3%), anorexia nervosa (0.6%) and post traumatic stress disorder (0.6%). Psychiatric co-morbidities were more common among affirmed males (84.1% vs. 66%). History of suicidal ideation was more common among affirmed males (70.1%) than affirmed females (49%). Self-injuring (cutting) was more common among affirmed males (56.1%) than in affirmed females (25.5%). Mean age at hormonal treatment onset was similar in both groups (15.75 years in affirmed males vs. 15.58 years in affirmed females). The degree of gender dysphoria before and after starting hormonal treatment, reported on a scale of 0 (no dysphoria) to 10 (highest possible dysphoria), declined for both genders (8.08/10 and 3.99/10) and affirmed females (7.87/10 and 2.96/10). Conclusion: The prevalence of psychiatric co-morbidities, suicidal ideation, and self-injuring behavior is high among GNC youth, but in this population, significantly worse among affirmed males. Both groups had significant improvement in the degree of dysphoria after beginning hormonal treatment.

Background: Hyperinsulinism is the most common cause of persistent hypoglycemia. It results from different genetic defects, the most common being recessive and dominant mutations in the ABCC8/KCNJ11 genes. The majority of recessive mutations have a poor response to Diazoxide, while dominant mutations are highly variable in both clinical presentation and response to treatment. Prompt recognition and management is critical to avoid irreversible brain damage. Clinical case: A 38-week gestation male, born via emergent c-section due to decreased fetal movement, presented with neonatal hypoglycemia. Pregnancy was uncomplicated and mother had a normal OGTT. Patient had a history of suspected sepsis, seizures, pulmonary hypertension and respiratory distress requiring intubation. Blood glucose was undetectable at birth and required multiple dextrose 10% boluses. A critical sample with a glucose of 47 mg/dL showed an elevated insulin at 30.3 m IU/mL with undetectable ketone levels. Lactic acid, ammonia, cortisol, GH, plasma amino acids, acylcarnitine profile and uric organic acids where all normal for a hypoglycemic state. He required intravenous glucose infusion with GIR up to 17 mg/kg/min to maintain normoglycemia. A brain MRI at 11 days of life showed findings of white matter injury. Subsequent genetic testing identified a heterozygous c.4051G>A (p.Val1351Met) variant in ABCC8, classified as “of uncertain significance”. However, an entry in the ClinVar database (RCV000714711.1) exists from a research lab and was classified as likely pathogenic. Analysis of parental samples showed that the mother was heterozygous for the same genetic variant. She did not have a history of hypoglycemia. Patient was started on diazoxide (8 mg/kg/day) and chlorothiazide with resolution of hypoglycemia. At a follow up visit at 5 months of age, there was no history of hypoglycemia, and no need for adjustments of the diazoxide dose by weight (dose at that time of 7.4 mg/kg/day). Conclusion: The ABCC8 reported here is a dominant mutation causing hyperinsulinemic hypoglycemia responsive to diazoxide with a milder phenotype later in infancy. Longitudinal follow up of the case is warranted to understand the long term progress in patients with this particular mutation. Reference: Adam MP, Ardinger HH, Pagon RA, Wallace SE, et al. None. 1993. Familial hyperinsulinism.

Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM). We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 × 5 km pixel resolution) were obtained from ISRIC – World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ∼ 1 and ∼ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ∼ 39 % of SOC variability from repeated 5-fold cross-validation. We report a combined SOC stock of 77.8 ± 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 ± 16.5 Pg) and croplands (13 ± 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 ± 42.2 and 76.8 ± 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates.

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data.

People naturally understand emotions, thus permitting a machine to do the same could open new paths for human-computer interaction. Facial expressions can be very useful for emotion recognition techniques, as these are the biggest transmitters of non-verbal cues capable of being correlated with emotions. Several techniques are based on Convolutional Neural Networks (CNNs) to extract information in a machine learning process. However, simple CNNs are not always sufficient to locate points of interest on the face that can be correlated with emotions. In this work, we intend to expand the capacity of emotion recognition techniques by proposing the usage of Facial Action Units (AUs) recognition techniques to recognize emotions. This recognition will be based on the Facial Action Coding System (FACS) and computed by a machine learning system. In particular, our method expands over EmotiRAM, an approach for multi-cue emotion recognition, in which we improve over their facial encoding module.