Central nervous system modulates the neuromechanical delay in a broad range for the control of muscle force

Force is generated by muscle units according to the neural activation sent by motor neurons. The motor unit is therefore the interface between the neural coding of movement and the musculotendinous system. Here we propose a method to accurately measure the latency between an estimate of the neural drive to muscle and force. Furthermore, we systematically investigate this latency, which we refer to as the neuromechanical delay (NMD), as a function of the rate of force generation. In two experimental sessions, eight men performed isometric finger abduction and ankle dorsiflexion sinusoidal contractions at three frequencies and peak-to-peak amplitudes {0.5, 1, and 1.5 Hz; 1, 5, and 10 of maximal force [%maximal voluntary contraction (MVC)]}, with a mean force of 10% MVC. The discharge timings of motor units of the first dorsal interosseous (FDI) and tibialis anterior (TA) muscle were identified by high-density surface EMG decomposition. The neural drive was estimated as the cumulative discharge timings of the identified motor units. The neural drive predicted 80 ± 0.4% of the force fluctuations and consistently anticipated force by 194.6 ± 55 ms (average across conditions and muscles). The NMD decreased nonlinearly with the rate of force generation ( R² = 0.82 ± 0.07; exponential fitting) with a broad range of values (from 70 to 385 ms) and was 66 ± 0.01 ms shorter for the FDI than TA ( P < 0.001). In conclusion, we provided a method to estimate the delay between the neural control and force generation, and we showed that this delay is muscle-dependent and is modulated within a wide range by the central nervous system. NEW & NOTEWORTHY The motor unit is a neuromechanical interface that converts neural signals into mechanical force with a delay determined by neural and peripheral properties. Classically, this delay has been assessed from the muscle resting level or during electrically elicited contractions. In the present study, we introduce the neuromechanical delay as the latency between the neural drive to muscle and force during variable-force contractions, and we show that it is broadly modulated by the central nervous system.

Prostaglandin D2 regulates joint inflammation and destruction in murine collagen-induced arthritis

OBJECTIVE

Prostaglandin D2 (PGD2) may exert proinflammatory or antiinflammatory effects in different biologic systems. Although this prostanoid and the enzymes responsible for its synthesis are up-regulated by interleukin-1β (IL-1β) in human chondrocytes in vitro, the role of PGD2 in arthritis remains unclear. This study was undertaken to investigate the role of PGD2 in the inflammatory response and in joint destruction during the development of collagen-induced arthritis (CIA) in mice.

METHODS

PGD2 and cytokine levels in mice with CIA were determined by enzyme-linked immunosorbent assay. Expression of hematopoietic PGD synthase (h-PGDS), lipocalin-type PGD synthase (l-PGDS), and DP1 and DP2 receptors was analyzed by immunohistochemical methods. PGE2 levels were determined by radioimmunoassay.

RESULTS

The arthritic process up-regulated the expression of h-PGDS, l-PGDS, DP1, and DP2 in articular tissue. PGD2 was produced in the joint during the early phase of arthritis, and serum PGD2 levels increased progressively throughout the arthritic process, reaching a maximum during the late stages of CIA. Treatment of arthritic mice with the DP1 antagonist MK0524 soon after the onset of disease increased the incidence and severity of CIA as well as the local levels of IL-1β, CXCL-1, and PGE2, whereas IL-10 levels were reduced. The administration of the DP2 antagonist CAY10595 did not modify the severity of arthritis. The injection of PGD2 into the paw, as well as the administration of the DP1 agonist BW245C, significantly lowered the incidence of CIA, the inflammatory response, and joint damage.

CONCLUSION

Our findings indicate that PGD2 is produced in articular tissue during the development of CIA and plays an antiinflammatory role, acting through the DP1 receptor.

Analyzing EEG signals to detect unexpected obstacles during walking

Background

When an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI). In this work, the possibility of detecting a sudden obstacle appearance analyzing electroencephalographic (EEG) signals is assessed. For this purpose, different features of EEG signals are evaluated during the appearance of sudden obstacles while a subject is walking on a treadmill. The future goal is to use this procedure to detect any obstacle appearance during walking when the user is wearing a lower limb exoskeleton in order to generate an emergency stop command for the exoskeleton. This would enhance the user-exoskeleton interaction, improving the safety mechanisms of current exoskeletons.

Methods

In order to detect the change in the brain activity when an obstacle suddenly appears, different features of EEG signals are evaluated using the recordings of five healthy subjects. Since the change in the brain activity occurs in the time domain, the features evaluated are: common spatial patterns, average power, slope, and the coefficients of a polynomial fit. A Linear Discriminant Analysis-based classifier is used to differentiate between two conditions: the appearance or not of an obstacle. The evaluation of the performance to detect the obstacles is made in terms of accuracy, true positive (TP) and false positive (FP) rates.

Results

From the offline analysis, the best performance is achieved when the slope or the polynomial coefficients are used as features, with average detection accuracy rates of 74.0 and 79.5 %, respectively. These results are consistent with the pseudo-online results, where a complete EEG recording is segmented into windows of 500 ms and overlapped 400 ms, and a decision about the obstacle appearance is made for each window. The results of the best subject were 11 out of 14 obstacles detected with a rate of 9.09 FPs/min, and 10 out of 14 obstacles detected with a rate of 6.34 FPs/min using slope and polynomial coefficients features, respectively.

Conclusions

An EEG-based BCI can be developed to detect the appearance of unexpected obstacles. The average accuracy achieved is 79.5 % of success rate with a low number of false detections. Thus, the online performance of the BCI would be suitable for commanding in a safely way a lower limb exoskeleton during walking.

[Non-invasive mechanical ventilation in the pre- and intraoperative period and difficult airway]

Non-invasive mechanical ventilation is a method of ventilatory assistance aimed at increasing alveolar ventilation, thus achieving, in selected subjects, the avoidance of endotracheal intubation and invasive mechanical ventilation, with the consequent improvement in survival. There has been a systematic review and study of the technical, clinical experiences, and recommendations concerning the application of non-invasive mechanical ventilation in the pre- and intraoperative period. The use of prophylactic non-invasive mechanical ventilation before surgery that involves significant alterations in the ventilatory function may decrease the incidence of postoperative respiratory complications. Its intraoperative use will mainly depend on the type of surgery, type of anaesthetic technique, and the clinical status of the patient. Its use allows greater anaesthetic depth without deterioration of oxygenation and ventilation of patients.

Predictors of vasovagal symptoms or syncope during outpatient diagnostic hysteroscopy: A prospective observational study

OBJECTIVE

To analyze factors conditioning the apparition of vasovagal prodromic symptoms or vagal syncope during or immediately after a diagnostic hysteroscopy performed in an outpatient setting.

STUDY DESIGN

Prospective observational study including 1255 patients who received an outpatient diagnostic hysteroscopy in the Hysteroscopy Unit of Quirón Dexeus University Hospital, Barcelona, Spain since April 2019 until December 2021. Data such as fasting, smoking, analgesic or anxiolytic treatment intake, parity and menopausal status were collected. Patients with pathologies or treatments that could modify pain tolerance were excluded.

RESULTS

Mean age was 42.7 ± 9.03 years. An 84.3 % of patients were premenopausal, while 15.7 % were smokers and 59 % of them had smoked 30 min before the procedure. Thirty-three point three percent of patients were fasting and 56.5 % had taken analgesic or anxiolytic pre-medication. A total of 79 patients (6.3 %) developed symptoms of vasovagal reaction. Pain was reported as 7.56 ± 1.97 in the Visual Analogic Scale (VAS) in patients with vagal symptoms versus 4.95 ± 2.66 in patients without vagal symptoms (p < 0.001). In the multivariate logistic model only a higher VAS score and a previous history of vasovagal symptoms during medical procedures were associated to a higher probability of developing vagal symptoms after adjusted by covariates [Odds Ratio (OR): 1.67 (95 % confidence interval (CI):1.45;1.93), OR:3.44 (95 % CI: 2.04;5.79) respectively].

CONCLUSIONS

When performing an outpatient hysteroscopy, patients should be asked about their previous history of vagal symptoms during medical procedures. Painful procedures should be immediately stopped in order to prevent discomfort, avoid the apparition of vagal prodromes or syncope and improve satisfaction with the procedure.