Effect of chronic ankle instability on lower extremity kinematics,dynamic postural stability,and muscle activity during unilateral jump-landing tasks: A systematic review and meta-analysis

ObjectiveTo determine if individuals with chronic ankle instability (CAI) demonstrate altered landing kinematics, muscle activity, and impaired dynamic postural stability during a unilateral jump-landing task.Methods21 studies were included from PubMed, MEDLINE, Embase and CINAHL searched on September 26, 2021. Mean differences in joint angles and muscle activity between CAI and controls were analysed as continuous variables and pooled using a random-effects model to obtain standardised mean differences and 95% confidence intervals. Dynamic postural stability measured using time to stabilisation (TTS) was assessed qualitatively.ResultsWe found greater plantarflexion (pooled SMD = 0.33, 95%CI [0.02,0.65]), reduced knee flexion (pooled SMD = −0.67, 95%CI [−0.97, −0.37]), and reduced hip flexion (pooled SMD = −0.52, 95%CI [−0.96, −0.07]) in CAI after landing. Regarding muscle activity, we observed reduced peroneus longus muscle activation (pooled SMD = −0.77, 95% CI [−1.17, −0.36]) in CAI prior to landing.ConclusionOur study provides preliminary evidence of altered landing kinematics in the sagittal plane and reduced peroneus muscle activity in CAI during a dynamic jump-landing task. These results may have clinical implications in the development of more effective and targeted rehabilitation programmes for patients with CAI.     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.     

The influence of smoking on foot and ankle surgery: a review of the literature

The effect of tobacco smoking on foot and ankle procedures is likely to be more pronounced when compared to other orthopaedic surgery. This is due to the peripheral nature of the vasculature involved. This paper reviews the current clinical evidence on the effects of smoking foot and ankle surgery. In the trauma setting, the evidence suggests that wound complications and non-unions are significantly higher in the smoking population. In the elective setting there is a significantly increased risk of non-union in ankle and hindfoot arthrodeses in smokers. In the setting of diabetes, ulceration rate in smokers is higher and there may be a higher risk of amputation.     

Dynamic functional connectivity of the EEG in relation to outcome of postanoxic coma

ObjectiveEarly EEG contains reliable information for outcome prediction of comatose patients after cardiac arrest. We introduce dynamic functional connectivity measures and estimate additional predictive values.MethodsWe performed a prospective multicenter cohort study on continuous EEG for outcome prediction of comatose patients after cardiac arrest. We calculated Link Rates (LR) and Link Durations (LD) in the α, δ, and θ band, based on similarity of instantaneous frequencies in five-minute EEG epochs, hourly, during 3 days after cardiac arrest. We studied associations of LR and LD with good (Cerebral Performance Category (CPC) 1–2) or poor outcome (CPC 3–5) with univariate analyses. With random forest classification, we established EEG-based predictive models. We used receiver operating characteristics to estimate additional values of dynamic connectivity measures for outcome prediction.ResultsOf 683 patients, 369 (54%) had poor outcome. Patients with poor outcome had significantly lower LR and longer LD, with largest differences 12 h after cardiac arrest (LR_θ 1.87 vs. 1.95 Hz and LD_α 91 vs. 82 ms). Adding these measures to a model with classical EEG features increased sensitivity for reliable prediction of poor outcome from 34% to 38% at 12 h after cardiac arrest.ConclusionPoor outcome is associated with lower dynamics of connectivity after cardiac arrest.SignificanceDynamic functional connectivity analysis may improve EEG based outcome prediction.     

Therapeutic effectiveness of a single exercise session combined with Walk Aide functional electrical stimulation in post-stroke patients:a crossover design study

A growing body of evidence has suggested that the imbalance of epigenetic markers and oxidative stress appears to be involved in the pathophysiology and progression of stroke. Thus, strategies that modulate these biomarkers might be considered targets for neuroprotection and novel therapeutic opportunities for these patients. Physical exercise has been reported to induce changes in these epigenetic markers and improve clinical outcomes in different populations. However, little is reported on this in post-stroke patients. The purpose of this study was to investigate the effect of a single exercise session with Walk Aide functional electrical stimulation(FES) on cognitive performance, clinical functional parameters, oxidative stress and epigenetic modulation in post-stroke individuals. In this crossover design study, 12 post-stroke individuals aged 54–72 years of either sexes were included and subjected to a single session of exercise(45 minutes) without Walk Aide functional electrical stimulation(EXE alone group), followed by another single session of exercise(45 minutes) with Walk Aide functional electrical stimulation(EXE + FES group). The clinical functional outcome measures, cognitive performance and blood collections for biomarker measurements were assessed pre-and post-intervention. After intervention, higher Berg Balance Scale scores were obtained in the EXE + FES group than in the EXE alone group. There was no significant difference in the Timed Up and Go test results post-intervention between EXE alone and EXE + FES groups. After intervention, a better cognitive performance was found in both groups compared with before the intervention. After intervention, the Timed Up and Go test scores were higher in the EXE + FES group than in the EXE alone group. In addition, the intervention induced lower levels of lipid peroxidation. After intervention, carbonyl level was lower, superoxide dismutase activity and superoxide dismutase/catalase activity ratio were higher in the EXE + FES group, compared with the EXE group alone. In each group, both histone deacetylase(HDAC2) and histone acetyltransferase activities were increased after intervention compared with before the intervention. These findings suggest that a single exercise session with Walk Aide FES is more effective on balance ability and cognitive performance compared with conventional exercise alone in post-stroke patients. This is likely to be related to the regulation of oxidative stress markers. The present study was approved by the Research Ethics Committee of the Methodist University Center-IPA(approval No. 2.423.376) on December 7, 2017 and registered in the Brazilian Registry of Clinical Trials—Re BEC(RBR-9 phj2 q) on February 11, 2019.     

术中同期韧带修复治疗踝关节骨折伴三角韧带断裂疗效及预后影响因素探讨

目的探讨术中同期韧带修复治疗踝关节骨折伴三角韧带断裂疗效及预后影响因素。方法回顾性分析溧阳市人民医院2016年1月至2018年12月收治踝关节骨折伴三角韧带断裂患者共183例临床资料,其中未行同期韧带修复者共48例设为A组,术中行同期韧带修复者135例设为B组,而B组根据修复方式分为浅层修复(54例),深层修复(48例)及完全修复(33例);比较不同组术中出血量、住院时间、美国足踝外科学会(AOFAS)评分、视觉模拟评分法(VAS)评分、术后并发症发生率及矢状面足跟着地角度,采用Logistic回归模型进行预后影响因素分析。结果B组住院时间、VAS评分、术后并发症发生率及矢状面足跟着地角度均少于A组(P<0.05);B组AOFAS评分高于A组(P<0.05);B组冠状面和水平面足跟着地角度则高于A组(P<0.05);深层组和完全组AOFAS评分少于浅层组(P<0.05);深层组和完全组VAS评分显著高于浅层组(P<0.05);完全组术后并发症发生率高于浅层组(P<0.05);Logistic回归模型单因素分析结果显示,年龄、合并其他损伤、受伤至手术时间、深层及浅层修复均是患者临床预后影响因素(P<0.05);而多因素分析结果则显示,浅层修复是患者临床预后独立保护因素(P<0.05)。结论踝关节骨折伴三角韧带断裂患者术中同期行韧带修复可有效提高临床疗效,改善关节生物力学指标;其中行浅层修复三角韧带更有助于增加临床获益。     

Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study

ObjectiveChildhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs.MethodsTwelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1–30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks.ResultsDelta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs.ConclusionUsing spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring.SignificanceIdentifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.