Pathological hyperphosphorylation of the microtubule-associated protein tau is characteristic of Alzheimer's disease (AD) and the associated tauopathies. The reciprocal relationship between phosphorylation and O-GlcNAc modification of tau and reductions in O-GlcNAc levels on tau in AD brain offers motivation for the generation of potent and selective inhibitors that can effectively enhance O-GlcNAc in vertebrate brain. We describe the rational design and synthesis of such an inhibitor (thiamet-G, K(i) = 21 nM; 1) of human O-GlcNAcase. Thiamet-G decreased phosphorylation of tau in PC-12 cells at pathologically relevant sites including Thr231 and Ser396. Thiamet-G also efficiently reduced phosphorylation of tau at Thr231, Ser396 and Ser422 in both rat cortex and hippocampus, which reveals the rapid and dynamic relationship between O-GlcNAc and phosphorylation of tau in vivo. We anticipate that thiamet-G will find wide use in probing the functional role of O-GlcNAc in vertebrate brain, and it may also offer a route to blocking pathological hyperphosphorylation of tau in AD.

O-GlcNAc is an abundant post-translational modification of serine and threonine residues of nucleocytoplasmic proteins. This modification, found only within higher eukaryotes, is a dynamic modification that is often reciprocal to phosphorylation. In a manner analogous to phosphatases, a glycoside hydrolase termed O-GlcNAcase cleaves O-GlcNAc from modified proteins. Enzymes with high sequence similarity to human O-GlcNAcase are also found in human pathogens and symbionts. We report the three-dimensional structure of O-GlcNAcase from the human gut symbiont Bacteroides thetaiotaomicron both in its native form and in complex with a mimic of the reaction intermediate. Mutagenesis and kinetics studies show that the bacterial enzyme, very similarly to its human counterpart, operates via an unusual 'substrate-assisted' catalytic mechanism, which will inform the rational design of enzyme inhibitors.

O-GlcNAcase catalyzes the cleavage of beta-O-linked 2-acetamido-2-deoxy-beta-d-glucopyranoside (O-GlcNAc) from serine and threonine residues of post-translationally modified proteins. Two potent inhibitors of this enzyme are O-(2-acetamido-2-deoxy-d-glucopyranosylidene)amino-N-phenylcarbamate (PUGNAc) and 1,2-dideoxy-2'-methyl-alpha-d-glucopyranoso[2,1-d]-Delta2'-thiazoline (NAG-thiazoline). Derivatives of these inhibitors differ in their selectivity for human O-GlcNAcase over the functionally related human lysosomal beta-hexosamindases, with PUGNAc derivatives showing modest selectivities and NAG-thiazoline derivatives showing high selectivities. The molecular basis for this difference in selectivities is addressed as is how well these inhibitors mimic the O-GlcNAcase-stabilized transition state (TS). Using a series of substrates, ground state (GS) inhibitors, and transition state mimics having analogous structural variations, we describe linear free energy relationships of log(KM/kcat) versus log(KI) for PUGNAc and NAG-thiazoline. These relationships suggest that PUGNAc is a poor transition state analogue, while NAG-thiazoline is revealed as a transition state mimic. Comparative X-ray crystallographic analyses of enzyme-inhibitor complexes reveal subtle molecular differences accounting for the differences in selectivities between these two inhibitors and illustrate key molecular interactions. Computational modeling of species along the reaction coordinate, as well as PUGNAc and NAG-thiazoline, provide insight into the features of NAG-thiazoline that resemble the transition state and reveal where PUGNAc fails to capture significant binding energy. These studies also point to late transition state poise for the O-GlcNAcase catalyzed reaction with significant nucleophilic participation and little involvement of the leaving group. The potency of NAG-thiazoline, its transition state mimicry, and its lack of traditional transition state-like design features suggest that potent rationally designed glycosidase inhibitors can be developed that exploit variation in transition state poise.

Cricket fast bowling and possibly baseball pitching workloads require scrutiny not just for acute injuries but also for injury prevention in the subsequent month.

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Objectives: To examine whether bowling workload is a risk factor for overuse injury to Australian junior cricket fast bowlers and to evaluate the appropriateness of current bowling workload guidelines. Methods: Forty four male fast bowlers (mean (standard deviation) age 14.7 (1.4) years) were monitored prospectively over the [2002][2003] season. Bowlers completed a daily diary to record bowling workloads and self reported injuries, which were validated by a physiotherapist. Bowling workload prior to the first injury (for those bowlers who were injured) was compared to workload across the whole season for uninjured bowlers. Results: Eleven (25%) bowlers reported an overuse-type injury, with seven of these sustaining a back injury. Injured bowlers had been bowling significantly more frequently than uninjured bowlers (median number of days since the previous bowling day: 3.2 v 3.9 days, Mann-Whitney U = 105.0, p = 0.038). Compared with bowlers with an average of >3.5 rest days between bowling, bowlers with an average of ,3.5 rest days were at a significantly increased risk of injury (risk ratio (RR) = 3.1, 95% confidence interval (CI) 1.1 to 8.9). There were also trends towards an increased risk of injury for those who bowled an average of >2.5 days per week (RR = 2.5, 95% CI 0.9 to 7.4) or >50 deliveries per day (RR = 2.0, 95% CI 0.7 to 5.4). Conclusions: This study has identified high bowling workload as a risk factor for overuse injury to junior fast bowlers. Continued research is required to provide scientific evidence for bowling workload guidelines that are age-specific for junior fast bowlers.

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T he accurate determination of a child's developmental status is required for proper treatment of various growth disorders (1) and scoliosis (2). Other parameters, such as height, weight, secondary sexual characteristics, chronologic age, and dental age, correlate with developmental status, but skeletal age has been considered the most reliable method (3-5). The standard of care for this assessment calls for radiologists to identify the reference standard in an atlas of hand radiographs that most closely resembles an anteroposterior or posteroanterior radiograph of the participant's left hand. The most common atlas used as a reference standard is the Radiographic Atlas of Skeletal Development of the Hand and Wrist, published in 1959 (6).As part of the process of implementing an artificial intelligence (AI) algorithm in clinical practice, it is critical to properly determine its effects. However, different study designs may yield different findings about the same assistive technologies. For example, the same commercially available computer-aided detection system for detecting pulmonary nodules on chest CT scans produced different findings in studies completed within a year of each other (7-9). Findings on potential computer-aided diagnosis Background: Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice.Purpose: To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods:In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists' signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center-and radiologist-level effects was used to compare the two experimental groups.Results: Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001). Conclusion:Use of an artificial intelligence algorithm ...