Wearable multisource quantitative gait analysis of Parkinson's diseases

As the motor symptoms of Parkinson's disease (PD) are complex and influenced by many factors, it is challenging to quantify gait abnormalities adequately using a single type of signal. Therefore, a wearable multisource gait monitoring system is developed to perform a quantitative analysis of gait abnormalities for improving the effectiveness of the clinical diagnosis. To detect multisource gait data for an accurate evaluation of gait abnormalities, force sensitive sensors, piezoelectric sensors, and inertial measurement units are integrated into the devised device. The modulation circuits and wireless framework are designed to simultaneously collect plantar pressure, dynamic deformation, and postural angle of the foot and then wirelessly transmit these collected data. With the designed system, multisource gait data from PD patients and healthy controls are collected. Multisource features for quantifying gait abnormalities are extracted and evaluated by a significance test of difference and correlation analysis. The results show that the features extracted from every single type of data are able to quantify the health status of the subjects (p < 0.001, ρ > 0.50). More importantly, the validity of multisource gait data is verified. The results demonstrate that the gait feature fusing multisource data achieves a maximum correlation coefficient of 0.831, a maximum Area Under Curve of 0.9206, and a maximum feature-based classification accuracy of 88.3%. The system proposed in this study can be applied to the gait analysis and objective evaluation of PD.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

Prognostic relevance of gait-related cognitive functions for dementia conversion in amnestic mild cognitive impairment

BACKGROUND

Increasing research suggests that gait abnormalities can be a risk factor for Alzheimer's Disease (AD). Notably, there is growing evidence highlighting this risk factor in individuals with amnestic Mild Cognitive Impairment (aMCI), however further studies are needed. The aim of this study is to analyze cognitive tests results and brain-related measures over time in aMCI and examine how the presence of gait abnormalities (neurological or orthopedic) or normal gait affects these trends. Additionally, we sought to assess the significance of gait and gait-related measures as prognostic indicators for the progression from aMCI to AD dementia, comparing those who converted to AD with those who remained with a stable aMCI diagnosis during the follow-up.

METHODS

Four hundred two individuals with aMCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were included. Robust linear mixed-effects models were used to study the impact of gait abnormalities on a comprehensive neuropsychological battery over 36 months while controlling for relevant medical variables at baseline. The impact of gait on brain measures was also investigated. Lastly, the Cox proportional-hazards model was used to explore the prognostic relevance of abnormal gait and neuropsychological associated tests.

RESULTS

While controlling for relevant covariates, we found that gait abnormalities led to a greater decline over time in attention (DSST) and global cognition (MMSE). Intriguingly, psychomotor speed (TMT-A) and divided attention (TMT-B) declined uniquely in the abnormal gait group. Conversely, specific AD global cognition tests (ADAS-13) and auditory-verbal memory (RAVLT immediate recall) declined over time independently of gait profile. All the other cognitive tests were not significantly affected by time or by gait profile. In addition, we found that ventricles size increased faster in the abnormal gait group compared to the normal gait group. In terms of prognosis, abnormal gait (HR = 1.7), MMSE (HR = 1.09), and DSST (HR = 1.03) covariates showed a higher impact on AD dementia conversion.

CONCLUSIONS

The importance of the link between gait and related cognitive functions in terms of diagnosis, prognosis, and rehabilitation in aMCI is critical. We showed that in aMCI gait abnormalities lead to executive functions/attention deterioration and conversion to AD dementia.

Are there common walking gait characteristics in patients diagnosed with late-onset Pompe disease?

Late-onset Pompe disease (LOPD) is a rare disease, defined as a progressive accumulation of lysosomal glycogen resulting in muscle weakness and respiratory problems. Anecdotally, individuals often have difficulties walking, yet, there is no three-dimensional data supporting these claims. We aimed to assess walking patterns in individuals with LOPD and compare with healthy individuals. Kinematic, kinetic and spatiotemporal data were compared during walking at a self-selected speed between individuals with LOPD (n = 12) and healthy controls (n = 12). Gait profile scores and movement analysis profiles were also determined to indicate gait quality. In comparison with healthy individuals, the LOPD group demonstrated greater thoracic sway (96%), hip adduction angles (56%) and pelvic range of motion (77%) and reduced hip extensor moments (36%). Greater group variance for the LOPD group were also observed. Individuals with LOPD had a slower (15%) walking speed and reduced cadence (7%). Gait profile scores were 37% greater in the LOPD group compared to the healthy group. Proximal muscular weakness associated with LOPD disease is likely to have resulted in a myopathic gait pattern, slower selected walking speeds and deviations in gait patterns. Although individuals with LOPD presented with some common characteristics, greater variability in gait patterns is likely to be a result of wide variability in phenotype spectrum observed with LOPD. This is the first study to examine walking in individuals with LOPD using instrumented gait analysis and provides an understanding of LOPD on walking function which can help orientate physiotherapy treatment for individuals with LOPD.

Management and outcome of benign acute childhood myositis in pediatric emergency department

BACKGROUND

Benign acute childhood myositis (BACM) is a self-limited syndrome associated with viral infections characterized by symmetric lower extremity pain typically affecting school-aged children. Evolution in rhabdomyolysis and kidney damage is rarely reported. Despite this, the acute presentation commonly concerns both parents and health care providers, often leading to unnecessary workup. The aim of the study was to determine the features and outcome of a large series of children with BACM identifying a management pathway for pediatricians in Emergency Department (ED).

METHODS

We conducted a retrospective study of patients with BACM managed in 2 Italian pediatric ED during a period of 8 and a half years. Demographic data, clinical, and laboratory results were extracted from electronic medical records. Recurrence, complications, treatments, and outcomes were also recorded. Descriptive statistics were produced for first-episode patients and for those with recurrence of myositis. A comparison between groups was performed.

RESULTS

One hundred and thirteen patients with BACM were identified. Ninety-two children (65 males) had a single episode, while ten (nine males) had recurrence. The mean age at presentation was 6.0 years (range 2-13,2). All patients had normal neurological examination and no one developed myoglobinuria, or renal failure. At first evaluation median CK level was 1413 UI/l (normal values < 150 U/L). Median CK of "recurrent" patients was higher than "non-recurrent" (2330 vs 1150 U/L, p = 0.009). Viral studies were positive in 51/74 cases, with high prevalence of Influenza viruses. Ninety-six patients (85%) were hospitalized with a median of 4 days. No patients had any residual muscular impairment.

CONCLUSIONS

BACM has an excellent prognosis. Severe pathological conditions can be excluded with a complete history and clinical examination and simple blood and urine tests, avoiding unnecessary diagnostic investigations. Most patients may be discharged home from ED recommending hydration, rest, analgesics and careful follow-up.

Whole-exome sequencing identifies a novel mutation in spermine synthase gene (SMS) associated with Snyder-Robinson Syndrome

BACKGROUND

Loss of function mutations in the spermine synthase gene (SMS) have been reported to cause a rare X-linked intellectual disability known as Snyder-Robinson Syndrome (SRS). Besides intellectual disability, SRS is also characterized by reduced bone density, osteoporosis and facial dysmorphism. SRS phenotypes evolve with age from childhood to adulthood.

METHODS

Whole exome sequencing was performed to know the causative gene/pathogenic variant. Later we confirmed the pathogenic variant through Sanger sequencing. Furthermore, we also performed the mutational analysis through HOPE SERVER and SWISS-MODEL. Also, radiographs were also obtained for affected individual to confirm the disease features.

RESULTS

In this article, we report the first Pakistani family consisting of three patients with SRS and a novel missense pathogenic variant in the SMS gene (c.905 C > T p.(Ser302Leu)). In addition to the typical phenotypes, one patient presented with early-onset seizures. Clinical features, genetic and in-silico analysis linked the affected patients of the family with Snyder-Robinson and suggest that this novel mutation affects the spermine synthase activity.

CONCLUSION

A novel missense variant in the SMS, c.905C > T p. (Ser302Leu), causing Snyder- Robinson Syndrome (SRS) is reported in three members of Pakistani Family.