The purpose of this study was to investigate the complementary effects of polyphenolic compounds from pine bark extract (PE) as a strong antioxidative substrate on the symptoms of inattention and impulsivity in children with attention-deficit hyperactivity disorder (ADHD). This was a randomized, double-blind, crossover, placebo-controlled study that included two experimental units (4 weeks with PE supplementation and 4 weeks with placebo supplementation) separated by a 2-week washout period. ADHD participants were supplemented with 25 mg or 50 mg PE. We recruited 20 participants (17 boys and 3 girls) with a mean age of 10.0 ± 2.1 years. PE supplementation caused a significant reduction in the inattention and hyperactivity-impulsivity items of SNAP-IV. During the period of PE supplementation, the item of commissions in the Continuous Performance Test III (CPT III) significantly decreased, which was used to evaluate the symptoms of inattention and impulsivity. In addition, the erythrocytic reduced glutathione/oxidized glutathione ratio significantly increased, and the plasma TBARs level significantly decreased after 4 weeks of PE supplementation. However, there was no significant correlation between CPT III (commission) and antioxidative status indictors. PE supplementation may have potential effects of ameliorating inattention and impulsivity, and elevating the antioxidative status in children with ADHD. K E Y W O R D Sattention-deficit hyperactivity disorder, oxidative stress, pine bark extract | INTRODUCTIONAttention-deficit hyperactivity disorder (ADHD) is one of the most prevalent neurobiological disorders in the world and presents a complex, highly inheritable etiology (Polanczyk, Willcutt, Salum, Kieling, & Rohde, 2014). The global prevalence of ADHD in school-age children is 5.3%-7.1%, which varies according to different diagnostic tools, diagnostic criteria, and case samples (Thomas, Sanders, Doust, Beller, & Glasziou, 2015). On the other hand, Huang et al. used a National Health Insurance database and found that the prevalence of ADHD among Taiwanese school children was 7%-12% and was more common among boys (14.9%) than girls (4.5%) (Huang, Chu, Cheng, & Weng, 2014). This difference is partially because boys have explicit behaviors, which are more easily exhibited in early stages. Conversely, girls often show inattention, anxiety, and depression. It was reported that in around 60% of children with ADHD, the problems of inattention and hyperactivity continue to occur in adulthood (Barkley,
Rationale: Anorexia nervosa (AN) is a serious eating disorder associated with a distorted body image. Hypercholesterolemia has been found in patients with AN but the mechanism of hyperlipidemia in AN remains little known. Ascites in patients with AN has been attributed to hypoalbuminemia and liver diseases, but massive ascites without the aforementioned etiologies has never been reported in AN. Patient concerns: An 11-year-old girl was admitted for exclusion of organic underlying diseases due to severe body weight loss (18% within 3 weeks), poor appetite, and hypercholesterolemia (274 mg/dL). She complained of heartburn sensation, nausea, vomiting, constipation, and postprandial dull abdominal pain with fullness. Diagnoses: The patient's condition met with all 3 of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for diagnosing AN. On admission, her total cholesterol level was 337 mg/dL and hypocomplementemia (C3 55.5 mg/dL) was also found. Abdominal sonography and computed tomography scans showed massive ascites. However, neither proteinuria nor hypoalbuminemia was found. Upper gastroduodenal endoscopy showed chronic superficial gastritis and colonoscopy revealed negative findings. Ascites obtained by paracentesis demonstrated a transudate without bacterial infection, tuberculosis, or pancreatitis. Exploratory laparoscopy showed nonpurulent ascites. However, biopsies from the small intestine, mesentery, and liver showed chronic inflammation and fibrosis. Interventions: The intensive nutritional therapy by increasing total energy intake stepwise with a combination of high-energy formula and her favorite foods. Outcomes: Her hypercholesterolemia, hypocomplementemia, and massive ascites resolved after her weight was restored. She developed binge eating with continuous weight gain after discharge. Her weight significantly increased to an obese level (body mass index [BMI] 25.9 kg/m 2 ) after loss to follow-up for 4 years until she returned to our emergency room due to suicide attempt. Conclusion: Diagnostic crossover between subtypes in anorexia nervosa might be a potential risk factor for illness severity and poor prognosis. AN can manifest as massive ascites with normal albumin concentrations that could possibly be due to chronic inflammation of the intestinal serosa, mesentery, and peritoneal surface of the liver.
BackgroundAttention deficit hyperactivity disorder (ADHD) is a well-studied topic in child and adolescent psychiatry. ADHD diagnosis relies on information from an assessment scale used by teachers and parents and psychological assessment by physicians; however, the assessment results can be inconsistent.PurposeTo construct models that automatically distinguish between children with predominantly inattentive-type ADHD (ADHD-I), with combined-type ADHD (ADHD-C), and without ADHD.MethodsClinical records with age 6–17 years-old, for January 2011–September 2020 were collected from local general hospitals in northern Taiwan; the data were based on the SNAP-IV scale, the second and third editions of Conners’ Continuous Performance Test (CPT), and various intelligence tests. This study used an artificial neural network to construct the models. In addition, k-fold cross-validation was applied to ensure the consistency of the machine learning results.ResultsWe collected 328 records using CPT-3 and 239 records using CPT-2. With regard to distinguishing between ADHD-I and ADHD-C, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 88.75 and 85.56% in the training and testing sets, respectively. The replacement of CPT-2 with CPT-3 results in this model yielded an overall accuracy of 90.46% in the training set and 89.44% in the testing set. With regard to distinguishing between ADHD-I, ADHD-C, and the absence of ADHD, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 86.74 and 77.43% in the training and testing sets, respectively.ConclusionThis proposed model distinguished between the ADHD-I and ADHD-C groups with 85–90% accuracy, and it distinguished between the ADHD-I, ADHD-C, and control groups with 77–86% accuracy. The machine learning model helps clinicians identify patients with ADHD in a timely manner.
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