SNAr Regioselectivity Predictions: Machine Learning Triggering DFT Reaction Modeling through Statistical Threshold.

Fast and accurate prospective predictions of regioselectivity can significantly reduce the time and resources spent on unproductive transformations in the pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition state theory (TST) and machine learning (ML) methods has been widely used to predict reaction outcomes such as selectivity. However, TST reaction modeling and ML methods are either time-consuming or data-dependent. Herein, we introduce a prototype seamlessly bridging ML and TST modeling by triggering resource-intensive but much less domain-sensitive DFT calculations only on less confident ML predictions. The proposed workflow was trained and tested on both the Pfizer internal dataset and the USPTO public dataset to predict regioselectivity for SNAr reactions. Our method is accurate and fast, which achieves 96.3 and 94.7% accuracy in predicting the correct major product on Pfizer and USPTO datasets, respectively, in a fraction of conventional TST computing time.

The Single-Component Flavin Reductase/Flavin-Dependent Halogenase AetF is a Versatile Catalyst for Selective Bromination and Iodination of Arenes and Olefins.

Flavin-dependent halogenases (FDHs) natively catalyze selective halogenation of electron rich aromatic and enolate groups. Nearly all FDHs reported to date require a separate flavin reductase to supply them with FADH2 , which complicates biocatalysis applications. In this study, we establish that the single component flavin reductase/flavin dependent halogenase AetF catalyzes halogenation of a diverse set of substrates using a commercially available glucose dehydrogenase to drive its halogenase activity. High site selectivity, activity on relatively unactivated substrates, and high enantioselectivity for atroposelective bromination and bromolactonization was demonstrated. Site-selective iodination and enantioselective cycloiodoetherification was also possible using AetF. The substrate and reaction scope of AetF suggest that it has the potential to greatly improve the utility of biocatalytic halogenation.

Association between physical activity level and cardiovascular risk factors in adolescents living with type 1 diabetes mellitus: a cross-sectional study.

BACKGROUND: Type 1 diabetes mellitus (T1DM) is associated with an increased risk for cardiovascular disease (CVD) related morbidity and premature mortality. Regular physical activity plays an important role in the primary and secondary prevention of CVD, improving overall health and wellbeing. Previous observational studies have examined the associations between self-reported physical activity and CVD risk factors in largely adult Caucasian populations. However, limited work has evaluated the relationship between objectively measured physical activity and CVD risk factors in other ethnicities, particularly Chinese youth living with T1DM. METHODS: This cross-sectional study assessed CVD risk factors, physical activity, and aerobic fitness (and their associations) in Chinese youth living with T1DM (n = 48) and peers (n = 19) without T1DM. Primary outcomes included blood pressure, lipid profiles, and physical activity (accelerometry). Statistical differences between groups were determined with chi-square, independent-samples t-tests, or analysis of covariance. The associations between aerobic fitness, daily physical activity variables, and CVD risk factors were assessed with univariate and multivariate linear regression analyses. RESULTS: Results were summarized using means and standard deviation (SD) for normally distributed variables and medians and 25-75th quartile for non-normally distributed variables. In comparison to peers without diabetes, youth living with T1DM showed higher levels of total cholesterol (3.14 ± 0.67 vs. 4.03 ± 0.81 mmol·L-1, p = 0.001), low-density lipoprotein cholesterol (1.74 ± 0.38 vs. 2.31 ± 0.72 mmol·L-1, p = 0.005), and triglycerides (0.60 ± 0.40 vs. 0.89 ± 0.31 mmol·L-1 p = 0.012), and lower maximal oxygen power (44.43 ± 8.29 vs. 35.48 ± 8.72 mL·kg-1·min-1, p = 0.003), total physical activity counts (451.01 ± 133.52 vs. 346.87 ± 101.97 counts·min-1, p = 0.004), metabolic equivalents (METs) (2.41 ± 0.60 vs. 2.09 ± 0.41 METs, p = 0.033), moderate-to-vigorous intensity physical activity [MVPA: 89.57 (61.00-124.14) vs (53.19 (35.68-63.16) min, p = 0.001], and the percentage of time spent in MVPA [11.91 (7.74-16.22) vs 8.56 (6.18-10.12) %, p = 0.038]. The level of high-density lipoprotein cholesterol was positively associated with METs (ß = 0.29, p = 0.030, model R2 = 0.168), and the level of triglycerides was negatively associated with physical activity counts (ß = - 0.001, p = 0.018, model R2 = 0.205) and METs (ß = - 0.359, p = 0.015, model R2 = 0.208), and positively associated with time spent in sedentary behaviour (ß = 0.002, p = 0.041, model R2 = 0.156) in persons living with T1DM. CONCLUSIONS: Chinese youth with T1DM, despite their young age and short duration of diabetes, present early signs of CVD risk, as well as low physical activity levels and cardiorespiratory fitness compared to apparently healthy peers without diabetes. Regular physical activity is associated with a beneficial cardiovascular profile in T1DM, including improvements in lipid profile. Thus, physical activity participation should be widely promoted in youth living with T1DM.

Predicting Infrared Spectra with Message Passing Neural Networks.

Infrared (IR) spectroscopy remains an important tool for chemical characterization and identification. Chemprop-IR has been developed as a software package for the prediction of IR spectra through the use of machine learning. This work serves the dual purpose of providing a trained general-purpose model for the prediction of IR spectra with ease and providing the Chemprop-IR software framework for the training of new models. In Chemprop-IR, molecules are encoded using a directed message passing neural network, allowing for molecule latent representations to be learned and optimized for the task of spectral predictions. Model training incorporates spectra metrics and normalization techniques that offer better performance with spectral predictions than standard practice in regression models. The model makes use of pretraining using quantum chemistry calculations and ensembling of multiple submodels to improve generalizability and performance. The spectral predictions that result are of high quality, showing capability to capture the extreme diversity of spectral forms over chemical space and represent complex peak structures.

Anti-depression effects of ketogenic diet are mediated via the restoration of microglial activation and neuronal excitability in the lateral habenula.

Depression is a severe neuropsychiatric disorder, of which the underlying pathological mechanisms remain unclear. The ketogenic diet (KD) has been reported to exhibit preventative effects on depressive-like behaviors in rodents. However, the therapeutic effects of KD on depressive-like behaviors have not been illustrated thus far. Here, we found that KD treatment dramatically ameliorated depressive-like behaviors in both repeated social defeat stress (R-SDS) and lipopolysaccharide (LPS) models, indicating the potential therapeutic effects of KD on depression. Our electrophysiological studies further showed that neuronal excitability was increased in the lateral habenula (LHb) of mice exposed to R-SDS or LPS, which can be reversed in the presence of KD treatment. Moreover, R-SDS and LPS were also found to induce robust microglial inflammatory activation in the LHb. Importantly, these phenotypes were rescued in mice fed with KD. In addition, we found that the protein level of innate immune receptor Trem2 in the LHb was significantly decreased in depression models. Specific knockdown of Trem2 in LHb microglia induced depressive-like behaviors, increased neuronal excitability as well as robust microglial inflammatory activation. Altogether, we demonstrated the therapeutic effects of KD on depressive-like behaviors, which are probably mediated via the restoration of microglial inflammatory activation and neuronal excitability. Besides, we also proposed an unrecognized function of Trem2 in the LHb for depression. Our study sheds light on the pathogenesis of depression and thereby offers a potential therapeutic intervention.

The implication of LncRNA MALAT1 in promoting chemo-resistance of laryngeal squamous cell carcinoma cells.

BACKGROUND: This study was aimed to evaluate the involvement of lncRNA MALAT1 in modifying chemo-sensitivity of laryngeal squamous cell carcinoma (LSCC) cell lines. METHODS: Totally 108 pairs of tumor tissues and matched para-tumor normal tissues were gathered from patients who were pathologically confirmed as LSCC. Meanwhile, LSCC cell lines, including TU686, TU177, AMC-HN-8, and LSC-1, were purchased to evaluate their tolerance to cisplatin, 5-fluorouracil, paclitaxel, and vincristine. Additionally, CCK-8 assay, flow cytometry, transwell assay, and wound healing assay were implemented to assess the part of MALAT1 in modulating viability, apoptosis, invasion, and migration of LSCC cell lines. RESULTS: MALAT1 expression was higher in LSCC tissues than in adjacent normal tissues (P < .05), and LSCC patients who carried highly expressed MALAT1 demonstrated poorer 5-year survival than ones with low MALAT1 expression (P < .05). For another, expression of MALAT1 was also unusually elevated within TU686, TU177, AMC-HN-8, and LSC-1 cell lines as relative to NHBEC cell line (P < .05). The TU686 cell line therein excelled in resisting the growth-curbing effects of 5-fluorouracil (IC50 = 20.44 µmol/L), paclitaxel (IC50 = 35.86 µg/L), and vincristine (IC50 = 0.12 µmol/L), when compared with TU177, AMC-HN-8, and LSC-1 cell line (P < .05). Moreover, there seemed great potential for over-expressed MALAT1 to enhance the chemo-resistance of both TU686 and LSC-1 cell lines (P < .05). Not only that, silencing of MALAT1 tended to undermine the proliferative and metastatic power of TU686 and LSC-1 cell lines (P < .05). CONCLUSION: LncRNA MALAT1 counted in triggering tolerance of LSCC against chemo-drugs by boosting metastasis and depressing apoptosis of tumor cells.

Message-passing neural networks for high-throughput polymer screening.

Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data, machine learning approaches can enable rapid high-throughput virtual screening of large libraries of compounds. Graph-based neural network architectures have emerged in recent years as the most successful approach for predictions based on molecular structure and have consistently achieved the best performance on benchmark quantum chemical datasets. However, these models have typically required optimized 3D structural information for the molecule to achieve the highest accuracy. These 3D geometries are costly to compute for high levels of theory, limiting the applicability and practicality of machine learning methods in high-throughput screening applications. In this study, we present a new database of candidate molecules for organic photovoltaic applications, comprising approximately 91 000 unique chemical structures. Compared to existing datasets, this dataset contains substantially larger molecules (up to 200 atoms) as well as extrapolated properties for long polymer chains. We show that message-passing neural networks trained with and without 3D structural information for these molecules achieve similar accuracy, comparable to state-of-the-art methods on existing benchmark datasets. These results therefore emphasize that for larger molecules with practical applications, near-optimal prediction results can be obtained without using optimized 3D geometry as an input. We further show that learned molecular representations can be leveraged to reduce the training data required to transfer predictions to a new density functional theory functional.

Noncovalent Interactions in Organocatalysis and the Prospect of Computational Catalyst Design.

Noncovalent interactions are ubiquitous in organic systems, and can play decisive roles in the outcome of asymmetric organocatalytic reactions. Their prevalence, combined with the often subtle line separating favorable dispersion interactions from unfavorable steric interactions, often complicates the identification of the particular noncovalent interactions responsible for stereoselectivity. Ultimately, the stereoselectivity of most organocatalytic reactions hinges on the balance of both favorable and unfavorable noncovalent interactions in the stereocontrolling transition state (TS). In this Account, we provide an overview of our attempts to understand the role of noncovalent interactions in organocatalyzed reactions and to develop new computational tools for organocatalyst design. Following a brief discussion of noncovalent interactions involving aromatic rings and the associated challenges capturing these effects computationally, we summarize two examples of chiral phosphoric acid catalyzed reactions in which noncovalent interactions play pivotal, although somewhat unexpected, roles. In the first, List's catalytic asymmetric Fischer indole reaction, we show that both π-stacking and CH/π interactions of the substrate with the 3,3'-aryl groups of the catalyst impact the stability of the stereocontrolling TS. However, these noncovalent interactions oppose each other, with π-stacking interactions stabilizing the TS leading to one enantiomer and CH/π interactions preferentially stabilizing the competing TS. Ultimately, the CH/π interactions dominate and, when combined with hydrogen bonding interactions, lead to preferential formation of the observed product. In the second example, a series of phosphoric acid catalyzed asymmetric ring openings of meso-epoxides, we show that noncovalent interactions of the substrates with the 3,3'-aryl groups of the catalyst play only an indirect role in stereoselectivity. Instead, the stereoselectivity of these reactions are driven by the electrostatic stabilization of a fleeting partial positive charge in the SN2-like transition state by the chiral electrostatic environment of the phosphoric acid catalyst. Next, we describe our studies of bipyridine N-oxide and N,N'-dioxide catalyzed alkylation reactions. Based on several examples, we demonstrate that there are many potential arrangements of ligands around a hexacoordinate silicon in the stereocontrolling TS, and one must consider all of these in order to identify the lowest-lying TS structures. We also present a model in which electrostatic interactions between a formyl CH group and a chlorine in these TSs underlie the enantioselectivity of these reactions. Finally, we discuss our efforts to develop computational tools for the screening of potential organocatalyst designs, starting in the context of bipyridine N,N'-dioxide catalyzed alkylation reactions. Our new computational tool kit (AARON) has been used to design highly effective catalysts for the asymmetric propargylation of benzaldehyde, and is currently being used to screen catalysts for other reactions. We conclude with our views on the potential roles of computational chemistry in the future of organocatalyst design.

Stacked homodimers of substituted contorted hexabenzocoronenes and their complexes with C60 fullerene.

Stacking interactions involving substituted contorted hexabenzocoronene (c-HBC) with C60 were studied at the B97-D3M(BJ)/TZVPP//B97-D/TZV(2d,2p) level of theory. First, we showed that substituent effects in benzeneC60 complexes are uncorrelated with those in the benzene sandwich dimer, underscoring the importance of local, direct interactions in substituent effects in stacking interactions. Second, we showed that c-HBC preferentially forms stacked homodimers over complexes with C60; however, if the bowl depth of c-HBC is increased beyond 1.25 Å, the c-HBCC60 complex becomes preferred over the c-HBC homodimer. Ultimately, we showed that the perfluorination of c-HBC leads to sufficient curvature to allow the c-HBCC60 heterodimers to form preferentially over c-HBC homodimers, suggesting the possibility of the development of c-HBC derivatives that assemble into alternating stacks with C60.

Conformational behavior and stacking interactions of contorted polycyclic aromatics.

We present a systematic computational analysis of the conformations and stacking interactions of a set of 18 saddle-shaped, contorted polycyclic aromatic compounds at the B97-D3M(BJ)/TZV(2d,2p)//B97-D/TZV(2d,2p) level of theory. These doubly-concave systems offer a means of tuning the strength of stacking interactions through variations in molecular curvature, and understanding the intermolecular non-covalent interactions exhibited by these systems will aid the design of contorted polycyclic systems with precisely defined packing in the solid state. Computations reveal wide variations in both the nature of the low-lying conformations and the stacking affinities of these systems. In particular, the introduction of both thiophene rings around the periphery of these systems and the incorporation of B and N atoms into the coronene core can greatly enhance their tendency to form strongly stacked dimers. Overall, these data provide a reminder that curvature does not always lead to stronger stacking interactions.

Automated Quantum Mechanical Predictions of Enantioselectivity in a Rhodium-Catalyzed Asymmetric Hydrogenation.

A computational toolkit (AARON: An automated reaction optimizer for new catalysts) is described that automates the density functional theory (DFT) based screening of chiral ligands for transition-metal-catalyzed reactions with well-defined reaction mechanisms but multiple stereocontrolling transition states. This is demonstrated for the Rh-catalyzed asymmetric hydrogenation of (E)-ß-aryl-N-acetyl enamides, for which a new C2 -symmetric phosphorus ligand is designed.

Multi-responsive polypeptidosome: characterization, morphology transformation, and triggered drug delivery.

The biodegradable polymeric nanomedicines that may be integrated with multi-stimuli-sensitivity to achieve triggered or on-demand drug release kinetics are challenging for polymer therapeutics and drug delivery systems. By controlling the structure transformation of one polypeptide-b-PEO copolymer, a novel multi-responsive polypeptide-based vesicle (polypeptidosome) presents the combined sensitivity of multiple physiological and clinic-related stimuli, and both morphology and size of the polypeptidosome are changed during the triggered process. The designer polypeptide has unique structures composed of 1) light-responsive o-nitrobenzyl groups, 2) oxidizable thioether linkers, 3) photo-caged redox thiol groups on parent poly(L-cysteine), and 4) tunable conformation, which enable the polypeptidosome to have a peculiar multi-response. The anticancer drug doxorubicin can be released in a controlled or on-off manner. The combination stimuli of UV irradiation and H2 O2 oxidation induces a large effect and a lower IC50 of 3.80 µg doxorubicin (DOX) equiv/mL compared to 5.28 µg DOX equiv/mL of individual H2 O2 trigger.

Predicting the Risk of Injuries Through Assessments of Asymmetric Lower Limb Functional Performance: A Prospective Study of 415 Youth Taekwondo Athletes.

Background: The impact of interlimb asymmetries on sport injuries is unclear because of inconsistent findings, and there is a lack of research on youth athletes and the sport of taekwondo. Purpose: To examine the effects of functional interlimb asymmetries on noncontact lower limb injuries in youth athletes. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 415 taekwondo athletes (318 boys and 97 girls) aged 6 to 17 years underwent baseline testing to determine interlimb asymmetries through the single-leg countermovement jump (CMJ), hop, and triple hop tests as well as the Star Excursion Balance Test. The athletes were then evaluated for 12 months to observe the occurrence of noncontact lower limb injuries. Results: During the study, 98 athletes (70 boys and 28 girls) sustained at least 1 noncontact lower limb injury. Athletes with higher interlimb asymmetries in single-leg CMJ height showed a significantly increased risk of noncontact lower limb injuries (boys: odds ratio [OR], 1.053 [95% CI, 1.027-1.080], P < .001; girls: OR, 1.070 [95% CI, 1.016-1.128], P = .011). Asymmetry in single-leg CMJ height of ≥15.28% was found to be the cutoff point for predicting noncontact lower limb injuries in boys (OR, 4.652 [95% CI, 2.577-8.398]; P < .001). Conclusion: This study highlights the utility of interlimb asymmetries in unilateral jump performance as a tool for assessing the risk of noncontact lower limb injuries in youth taekwondo athletes of both sexes. A proper evaluation of interlimb asymmetries may improve prevention strategies for youth athletes.

Association between Inter-Limb Asymmetries in Lower-Limb Functional Performance and Sport Injury: A Systematic Review of Prospective Cohort Studies.

BACKGROUND: Inter-limb asymmetry in lower-limb functional performance has been associated with increased risk of sport injury; however, findings are not always consistent. PURPOSE: To conduct a systematic review on whether inter-limb asymmetry in lower-limb functional performance can predict sport injury. METHODS: Four electronic databases (MEDLINE, EMBASE, Web of Science, and SportDiscus) were systematically searched for prospective cohort studies reporting the association between inter-limb asymmetry in lower-limb functional performance and sport injury. RESULTS: A total of 28 prospective cohort studies were included in the analyses. Collectively, the findings were highly inconsistent, and a clear statement on the association between each asymmetry and sport injury was difficult. CONCLUSIONS: Highly inconsistent findings make it difficult to create clear recommendations on the relationship between the inter-limb asymmetry in lower-limb functional performance (power, muscle flexibility, and dynamic balance) and sport injury. The influence of potential factors (selection of tests/parameters, participant characteristics, definition of injury, and ways of calculating asymmetry) should be considered when using previous findings.

Associations between Sleep Characteristics and Cardiovascular Risk Factors in Adolescents Living with Type 1 Diabetes.

Adolescents living with type 1 diabetes (T1D) have an increased risk of developing cardiovascular disease. Sleep patterns have physiological and behavioral impacts on diabetes outcomes. This study aimed to investigate the associations between sleep patterns and CVD risk factors in adolescents living with T1D and their peers living without T1D. This cross-sectional study assessed CVD risk factors and sleep characteristics (and their associations) in adolescents, aged 12-18 years, living with T1D (n = 48) and their peers (n = 19) without T1D. Outcomes included blood pressure, lipid profiles, and sleep characteristics (accelerometry). Statistical differences between groups were determined with chi-square or independent samples t-tests. The associations between sleep characteristics and CVD risk factors were assessed with multivariate linear regression analyses. We found no significant differences between the two groups in terms of sleep duration, efficiency, sleep onset and offset, and frequency of awakenings, and there were associations between sleep efficiency and LDL-C (ß = -0.045, p = 0.018, model R2 = 0.230) and triglycerides (ß = -0.027, p = 0.012, model R2 = 0.222) after adjusting confounders (diabetes status, sex, age, pubertal stage) in all participants. In conclusion, adolescents with T1D and without T1D sleep less than the recommended eight hours per night. The associations between sleep efficiency and LDL-C and triglycerides are independent of sleep duration, regardless of sex, age, and pubertal stage.

Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network.

Nuclear magnetic resonance (NMR) is one of the primary techniques used to elucidate the chemical structure, bonding, stereochemistry, and conformation of organic compounds. The distinct chemical shifts in an NMR spectrum depend upon each atom's local chemical environment and are influenced by both through-bond and through-space interactions with other atoms and functional groups. The in silico prediction of NMR chemical shifts using quantum mechanical (QM) calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations, are expensive. Additionally, the QM predictions themselves may lack sufficient accuracy to identify a correct structure. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict 1H and 13C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model trained using QM calculations. When tested on the CHESHIRE dataset, the proposed model predicts observed 13C chemical shifts with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time. An automated prediction webserver and graphical interface are accessible online at http://nova.chem.colostate.edu/cascade/. We further demonstrate the model in three applications: first, we use the model to decide the correct organic structure from candidates through experimental spectra, including complex stereoisomers; second, we automatically detect and revise incorrect chemical shift assignments in a popular NMR database, the NMRShiftDB; and third, we use NMR chemical shifts as descriptors for determination of the sites of electrophilic aromatic substitution.

Bilateral difference between lower limbs in children practicing laterally dominant vs. non-laterally dominant sports.

Bilateral asymmetry in lower-limb power and dynamic balance has been associated with increased risk of sport injury, whereas there is a lack of research examining this asymmetry for child athletes. Twenty-eight fencers (19 boys and 9 girls, aged 9.71 ± 1.08 years) and 28 Taekwondo athletes (19 boys and 9 girls, aged 9.71 ± 1.08 years) were examined on the single-leg jump and Star Excursion Balance Test (SEBT) performance. A mixed model design ANOVA (2 [Sex: Boys, Girls] × 2 [Sport group: Fencing, Taekwondo] × 2 [Limb: Dominant, Non-dominant Leg]) was conducted to examine for difference for each test. There was a significant main effect of limb on hop and triple hop distance (p < 0.05). A significant limb by sex interaction (p = 0.000) was shown for the single-leg countermovement jump (CMJ) performance, wherein a bilateral difference was only shown in boys. In SEBT, a main effect (p = 0.007) of limb was identified for posterolateral (PL) reach distance. A limb by sex interaction (p = 0.009) was also shown for posteromedial (PM) reach distance, wherein a bilateral difference was only shown in girls. These findings suggest that child athletes in both laterally dominant and non-laterally dominant sports showed inter-limb asymmetry of leg power and dynamic balance. Sex should be an important consideration when evaluating bilateral difference of leg power and dynamic balance for child athletes.

The effect of fatigue on asymmetry between lower limbs in functional performances in elite child taekwondo athletes.

BACKGROUND: Inter-limb asymmetry above a certain threshold in functional performance indicates increased injury risk in sports. Fatigue has been found to increase bilateral asymmetry in lower-limb jumping performance among high-school and adult athletes, whereas this impact has not been examined in child athletes. This study aimed to examine the effect of fatigue on inter-limb asymmetry in functional performances in elite Taekwondo athletes aged between 9 and 11 years. METHODS: Performance of single-leg jumps, Star Excursion Balance Test (SEBT), and muscle (hamstring and gastrocnemius) flexibility were measured for 13 elite male child Taekwondo athletes (aged 9.85 ± 0.80 years) at both the rested and fatigued states to examine the inter-limb asymmetry. A two-way repeated measures ANOVA was conducted to examine for difference and the interaction between limb (dominant, non-dominant leg) and state (rested, fatigued state) for each test. Paired t test or Wilcoxon signed-rank test was used to compare the asymmetry magnitude at the rested vs. fatigued state for each test, and the variation of performance post fatigue in the dominant vs. non-dominant leg when appropriate. RESULTS: The inter-limb asymmetry in triple-hop distance significantly (p = 0.046) increased with fatigue, whereas the asymmetry significantly (p = 0.004) decreased with fatigue in anterior (ANT) reach distance in SEBT. A significant (p = 0.027) limb by state interaction was shown for posterolateral (PL) reach distance in SEBT, wherein a significant (p = 0.005) bilateral difference was only shown at the rested state. The PL reach distance showed a significantly greater decrease (p = 0.028) post fatigue when using the dominant leg for support compared to using the non-dominant leg. CONCLUSIONS: Fatigue significantly impacts inter-limb asymmetry in jump performances and dynamic balance for child athletes, while the variation of inter-limb asymmetry post fatigue may be different across tests. For the purpose of injury prevention, practitioners should consider assessing the inter-limb asymmetry for children at both the rested and fatigued state and be mindful of the fatigue response of each leg in functional tests.

Risk Factors for Non-Contact Lower-Limb Injury: A Retrospective Survey in Pediatric-Age Athletes.

BACKGROUND: Risk factors for non-contact lower-limb injury in pediatric-age athletes and the effects of lateral dominance in sport (laterally vs. non-laterally dominant sports) on injury have not been investigated. PURPOSE: To identify risk factors for non-contact lower-limb injury in pediatric-age athletes. METHODS: Parents and/or legal guardians of 2269 athletes aged between 6-17 years were recruited. Each participant completed an online questionnaire that contained 10 questions about the athlete's training and non-contact lower-limb injury in the preceding 12 months. RESULTS: The multivariate logistic regression model determined that lateral dominance in sport (adjusted OR (laterally vs. non-laterally dominant sports), 1.38; 95% CI, 1.10-1.75; p = 0.006), leg preference (adjusted OR (right vs. left-leg preference), 0.71; 95% CI, 0.53-0.95; p = 0.023), increased age (adjusted OR, 1.21; 95% CI, 1.16-1.26; p = 0.000), training intensity (adjusted OR, 1.77; 95% CI, 1.43-2.19; p = 0.000), and training frequency (adjusted OR, 1.36; 95% CI, 1.25-1.48; p = 0.000) were significantly associated with non-contact lower-limb injury in pediatric-age athletes. Length of training (p = 0.396) and sex (p = 0.310) were not associated with a non-contact lower-limb injury. CONCLUSIONS: Specializing in laterally dominant sports, left-leg preference, increase in age, training intensity, and training frequency indicated an increased risk of non-contact lower-limb injury in pediatric-age athletes. Future research should take into account exposure time and previous injury.

Publisher Correction: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.