A High-Sugar Diet Consumption,Metabolism and Health Impacts with a Focus on the Development of Substance Use Disorder: A Narrative Review

Carbohydrates are important macronutrients in human and rodent diet patterns that play a key role in crucial metabolic pathways and provide the necessary energy for proper body functioning. Sugar homeostasis and intake require complex hormonal and nervous control to proper body energy balance. Added sugar in processed food results in metabolic, cardiovascular, and nervous disorders. Epidemiological reports have shown enhanced consumption of sweet products in children and adults, especially in reproductive age and in pregnant women, which can lead to the susceptibility of offspring’s health to diseases in early life or in adulthood and proneness to mental disorders. In this review, we discuss the impacts of high-sugar diet (HSD) or sugar intake during the perinatal and/or postnatal periods on neural and behavioural disturbances as well as on the development of substance use disorder (SUD). Since several emotional behavioural disturbances are recognized as predictors of SUD, we also present how HSD enhances impulsive behaviour, stress, anxiety and depression. Apart from the influence of HSD on these mood disturbances, added sugar can render food addiction. Both food and addictive substances change the sensitivity of the brain rewarding neurotransmission signalling. The results of the collected studies could be important in assessing sugar intake, especially via maternal dietary patterns, from the clinical perspective of SUD prevention or pre-existing emotional disorders. Methodology: This narrative review focuses on the roles of a high-sugar diet (HSD) and added sugar in foods and on the impacts of glucose and fructose on the development of substance use disorder (SUD) and on the behavioural predictors of drugs abuse. The literature was reviewed by two authors independently according to the topic of the review. We searched the PubMed and Scopus databases and Multidisciplinary Digital Publishing Institute open access scientific journals using the following keyword search strategy depending on the theme of the chapter: “high-sugar diet” OR “high-carbohydrate diet” OR “sugar” OR “glucose” OR “fructose” OR “added sugar” AND keywords. We excluded inaccessible or pay-walled articles, abstracts, conference papers, editorials, letters, commentary, and short notes. Reviews, experimental studies, and epidemiological data, published since 1990s, were searched and collected depending on the chapter structure. After the search, all duplicates are thrown out and full texts were read, and findings were rescreened. After the selection process, appropriate papers were included to present in this review.     

Correction to: Measuring treatment effect on psoriatic arthritis-related domains: insights from the SPIRIT-H2H study at weeks 24 and 52

Clinical Rheumatology -     

Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification

Background and purpose — Classification of ankle fractures is crucial for guiding treatment but advanced classifications such as the AO Foundation/Orthopedic Trauma Association (AO/OTA) are often too complex for human observers to learn and use. We have therefore investigated whether an automated algorithm that uses deep learning can learn to classify radiographs according to the new AO/OTA 2018 standards.Method — We trained a neural network based on the ResNet architecture on 4,941 radiographic ankle examinations. All images were classified according to the AO/OTA 2018 classification. A senior orthopedic surgeon (MG) then re-evaluated all images with fractures. We evaluated the network against a test set of 400 patients reviewed by 2 expert observers (MG, AS) independently.Results — In the training dataset, about half of the examinations contained fractures. The majority of the fractures were malleolar, of which the type B injuries represented almost 60% of the cases. Average area under the area under the receiver operating characteristic curve (AUC) was 0.90 (95% CI 0.82–0.94) for correctly classifying AO/OTA class where the most common major fractures, the malleolar type B fractures, reached an AUC of 0.93 (CI 0.90–0.95). The poorest performing type was malleolar A fractures, which included avulsions of the fibular tip.Interpretation — We found that a neural network could attain the required performance to aid with a detailed ankle fracture classification. This approach could be scaled up to other body parts. As the type of fracture is an important part of orthopedic decision-making, this is an important step toward computer-assisted decision-making.

Ankle fractures are recognized among the most common fractures, with peak incidence between 15 and 29 years (67 per 100,000 person-years) and elderly women ≥ 60 years (174 per 100,000 person-years) (Westerman and Porter 2007, Thur et al. 2012). Efforts to classify ankle fractures in clinically relevant entities have a long history, ending in 3 classic systems, i.e., the Lauge-Hansen (Hansen 1942), Danis–Weber, and the AO/OTA classifications (Association Committee for Coding and Classification 1996; Budny and Young 2008), where the Danis–Weber with its A, B, and C classes is probably the most used in everyday practice.The most recent update for the AO/OTA classification system was published in 2018 (Meinberg et al. 2018). The AO/OTA system contains classifications for the entire body. The ankle is divided into (1) malleolar, (2) distal tibia, and (3) fibular fractures. For malleolar fractures, the subcategories correspond to the Danis–Weber ABC classification (Hughes et al. 1979) with the addition of a suffix of 2 digits (range 1–3), e.g., the common intra-syndesmotic B-injury without widening of the mortise corresponds to the B1.1 class. The numbers correspond roughly to the severity of each fracture.The complexity of this classification makes it difficult to learn and apply, limiting inter-observer reliability and reproducibility (Fonseca et al. 2017). This has hindered its use in an everyday clinical setting, suggesting the need for better aid during the classification.During recent years, the resurgence of neural networks, a form of artificial intelligence (AI), has proven highly successful for image classification. In some medical image classification applications neural networks attain (Olczak et al. 2017, Kim and MacKinnon 2018, Gan et al. 2019), and surpass, human expert performance (Esteva et al. 2017, Lee et al. 2017, Chung et al. 2018, Urakawa et al. 2019). Machine learning and neural networks are also becoming more commonplace research tools in orthopedics. They hold great potential, as the diagnostic underpinning and intervention decision relies heavily on medical imaging (Cabitza et al. 2018). The strength of these learning algorithms is their ability to review a vast number of examinations and examples, and the speed and consistency with which they can review each examination and at the same time remember thousands of categories without issue.We therefore hypothesized that a neural network can learn to classify ankle fractures according to the AO/OTA 2018 classification from radiographs.     

Application of transmission electron tomography for modeling the renal corpuscle

Structural alteration to the microanatomical organization of the glomerular filtration barrier results in proteinuria. Conventional transmission electron microscopy is an important diagnostic tool to assess the degree of ultrastructural damage of the corpusclar filtration unit. However, this approach lacks the ability to collect accurate stereological insights in a relative large tissue volume. Transmission electron tomography offers the ability to gather three-dimensional information with relative ease. Therefore, this contribution aims to highlight what electron tomography can bring to the pathologist in this challenging area of diagnostic practice.     

Oral administration of BDNF and/or GDNF normalizes serum BDNF level in the olfactory bulbectomized rats: A proof of concept study

BackgroundNeurotrophins, especially brain-derived neurotrophic factor (BDNF) have gained significant therapeutic interest particularly in neurologic and psychiatric disorders and they have been found in human breast milk of mothers who suffered from adverse outcomes in pregnancy. This study tested the hypothesis that oral administration of BDNF/GDNF (glial cell line-derived neurotrophic factor) can exert a biological effect in a rat model of severe neuropathology induced by olfactory bulbectomy (OBX), which exhibits dysregulation of BDNF signaling and impaired blood-brain barrier.MethodsAdult male albino Sprague-Dawley rats underwent the OBX surgery and separate groups of OBX and sham-operated controls received one oral dose of vehicle, BDNF (0.005 mg/kg), GDNF (0.03 mg/kg) or their combination. One week after neurotrophin dosing the rats were sacrificed and BDNF level was assessed by ELISA in the blood serum and cerebrospinal fluid.ResultsA significant decrease of serum BDNF level was found in the OBX model. This alteration was normalized by all types of treatment BDNF, GDNF, or their combination. No influence of sham surgery or treatment was observed in the control rats. BDNF levels in cerebrospinal fluid were below detection limit.ConclusionThis study indicates that oral administration of neurotrophins is able to exert a biological effect in the OBX model. There is a number of potential mechanisms, which remain to be elucidated.     

Max Lewandowsky (1876–1918)

Journal of Neurology -     

Veal Calves Produce Less Antibodies against C. Perfringens Alpha Toxin Compared to Beef Calves

Enterotoxaemia is a disease with a high associated mortality rate, affecting beef and veal calves worldwide, caused by C. perfringens alpha toxin and perfringolysin. A longitudinal study was conducted to determine the dynamics of antibodies against these toxins in 528 calves on 4 beef and 15 veal farms. The second study aimed to determine the effect of solid feed intake on the production of antibodies against alpha toxin and perfringolysin. The control group only received milk replacer, whereas in the test group solid feed was provided. Maternal antibodies for alpha toxin were present in 45% of the veal calves and 66% of the beef calves. In beef calves a fluent transition from maternal to active immunity was observed for alpha toxin, whereas almost no veal calves developed active immunity. Perfringolysin antibodies significantly declined both in veal and beef calves. In the second study all calves were seropositive for alpha toxin throughout the experiment and solid feed intake did not alter the dynamics of alpha and perfringolysin antibodies. In conclusion, the present study showed that veal calves on a traditional milk replacer diet had significantly lower alpha toxin antibodies compared to beef calves in the risk period for enterotoxaemia, whereas no differences were noticed for perfringolysin.