Progressive ataxia, ophthalmoparesis, and hypogonadotropic hypogonadism in a family with a novel variant in the KIFBP gene.

A novel homozygous variant in KIFBP was identified in a consanguineous family with four sibs affected by Goldberg-Sphrintzen Syndrome (GOSHS). We report for the first time, early-adulthood-onset progressive ataxia, opthalmoparesis, and hypogonadotropic hypogonadism in GOSHS.

Electrical stimulator with mechanomyography-based real-time monitoring, muscle fatigue detection, and safety shut-off: a pilot study.

Functional electrical stimulation (FES) has been used to produce force-related activities on the paralyzed muscle among spinal cord injury (SCI) individuals. Early muscle fatigue is an issue in all FES applications. If not properly monitored, overstimulation can occur, which can lead to muscle damage. A real-time mechanomyography (MMG)-based FES system was implemented on the quadriceps muscles of three individuals with SCI to generate an isometric force on both legs. Three threshold drop levels of MMG-root mean square (MMG-RMS) feature (thr50, thr60, and thr70; representing 50%, 60%, and 70% drop from initial MMG-RMS values, respectively) were used to terminate the stimulation session. The mean stimulation time increased when the MMG-RMS drop threshold increased (thr50: 22.7 s, thr60: 25.7 s, and thr70: 27.3 s), indicating longer sessions when lower performance drop was allowed. Moreover, at thr70, the torque dropped below 50% from the initial value in 14 trials, more than at thr50 and thr60. This is a clear indication of muscle fatigue detection using the MMG-RMS value. The stimulation time at thr70 was significantly longer (p = 0.013) than that at thr50. The results demonstrated that a real-time MMG-based FES monitoring system has the potential to prevent the onset of critical muscle fatigue in individuals with SCI in prolonged FES sessions.

Low genetic diversity indicating the threatened status of Rhizophora apiculata (Rhizophoraceae) in Malaysia: declined evolution meets habitat destruction.

Worldwide, many mangrove species are experiencing significant population declines, including Rhizophora apiculata, which is one of the most widespread and economically important species in tropical Asia. In Malaysia, there has been an alarming decline in R. apiculata populations driven primarily by anthropogenic activities. However, the lack of genetic and demographic information on this species has hampered local efforts to conserve it. To address these gaps, we generated novel genetic information for R. apiculata, based on 1,120 samples collected from 39 natural populations in Peninsular Malaysia. We investigated its genetic diversity and genetic structure with 19 transcriptome and three nuclear microsatellite markers. Our analyses revealed a low genetic diversity (mean He: 0.352) with significant genetic differentiation (FST: 0.315) among populations of R. apiculata. Approximately two-third of the populations showed significant excess of homozygotes, indicating persistent inbreeding which might be due to the decrease in population size or fragmentation. From the cluster analyses, the populations investigated were divided into two distinct clusters, comprising the west and east coasts of Peninsular Malaysia. The western cluster was further divided into two sub-clusters with one of the sub-clusters showing strong admixture pattern that harbours high levels of genetic diversity, thus deserving high priority for conservation.

Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury.

Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity. Graphical abstract ᅟ.