The function and therapeutic potential of transfer RNA-derived small RNAs in cardiovascular diseases: A review.

Transfer RNA-derived small RNAs (tsRNAs) are a class of small non-coding RNA (sncRNA) molecules derived from tRNA, including tRNA derived fragments (tRFs) and tRNA halfs (tiRNAs). tsRNAs can affect cell functions by participating in gene expression regulation, translation regulation, intercellular signal transduction, and immune response. They have been shown to play an important role in various human diseases, including cardiovascular diseases (CVDs). Targeted regulation of tsRNAs expression can affect the progression of CVDs. The tsRNAs induced by pathological conditions can be detected when released into the extracellular, giving them enormous potential as disease biomarkers. Here, we review the biogenesis, degradation process and related functional mechanisms of tsRNAs, and discuss the research progress and application prospects of tsRNAs in different CVDs, to provide a new perspective on the treatment of CVDs.

Molecular insights into regulatory RNAs in the cellular machinery.

It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning.

Severe obstructive sleep apnea in children with syndromic craniosynostosis: analysis of pulse transit time.

STUDY OBJECTIVES: We examined the association between pulse transit time (PTT) and obstructive sleep apnea (OSA) in children with syndromic craniosynostosis (SCS), where OSA is a common problem and may cause cardiorespiratory disturbance. METHODS: Retrospective study of children (age <18 years) with SCS and moderate-to-severe OSA (i.e., obstructive apnea-hypopnea index [oAHI] ≥ 5), or no OSA (oAHI < 1) who underwent overnight polysomnography (PSG). Children without SCS and normal PSG were included as controls. Reference intervals (RIs) for PTT were computed by non-parametric bootstrap analysis. Based on RIs of controls, the sensitivity and specificity of PTT to detect OSA were determined. In a linear mixed-model the explanatory variables assessed were sex, age, sleep stage, and time after obstructive events. RESULTS: In all 68 included children (19 SCS with OSA, 30 SCS without OSA, 19 controls), obstructive events occurred throughout all sleep stages, most prominently during rapid eye movement sleep (REM) and non-REM sleep stages N1 and N2, with evident PTT changes. Greatest reductions were observed 4 - 8 s after an event (p < 0.05). In SCS with OSA, PTT RIs were lower during all sleep stages compared to SCS without OSA. The highest sensitivity was observed during N1 (55.5%), and the highest specificity during REM (76.5%). Lowest PTT values were identified during N1. CONCLUSIONS: Obstructive events occur throughout all sleep stages with transient reductions in PTT. However, PTT as a variable for OSA detection is limited by its sensitivity and specificity.

Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures.

The quantification of comfort in binding parts, essential human-machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.

Determination of Gait Events and Temporal Gait Parameters for Persons with a Knee-Ankle-Foot Orthosis.

Gait event detection is essential for controlling an orthosis and assessing the patient's gait. In this study, patients wearing an electromechanical (EM) knee-ankle-foot orthosis (KAFO) with a single IMU embedded in the thigh were subjected to gait event detection. The algorithm detected four essential gait events (initial contact (IC), toe off (TO), opposite initial contact (OIC), and opposite toe off (OTO)) and determined important temporal gait parameters such as stance/swing time, symmetry, and single/double limb support. These gait events were evaluated through gait experiments using four force plates on healthy adults and a hemiplegic patient who wore a one-way clutch KAFO and a pneumatic cylinder KAFO. Results showed that the smallest error in gait event detection was found at IC, and the largest error rate was observed at opposite toe off (OTO) with an error rate of -2.8 ± 1.5% in the patient group. Errors in OTO detection resulted in the largest error in determining the single limb support of the patient with an error of 5.0 ± 1.5%. The present study would be beneficial for the real-time continuous monitoring of gait events and temporal gait parameters for persons with an EM KAFO.

Quantitative detection and follow-up of intracranial hypertension in craniosynostosis: an optical coherence tomography study.

BACKGROUND/PURPOSE: To evaluate in craniosynostosis: 1) the diagnostic accuracy of fundoscopy and optical coherence tomography (OCT) to detect intracranial hypertension (ICH); 2) the time course of retinal thickness after treatment of ICH; and 3) the relation between high hyperopia (HH) and fundoscopy/OCT scan findings. METHODS: Syndromic, multisuture, unicoronal, unilambdoid and sagittal synostosis patients visiting our national center were included in this longitudinal cohort study and formed a consecutive series. Retinal layers on OCT, OCT fundus image and fundoscopy were evaluated. ICH was scored according to presence of abnormal intracranial pressures, hydrocephalus, progressive cerebellar tonsillar herniation or fingerprinting and growth arrest. Diagnostic accuracy of OCT, fundoscopy and fundus image, the time course of retinal thickness after ICH and interference of HH were analyzed using linear mixed models. RESULTS: 577 OCT scans in 307 patients were included. ICH was found in 7.2%. Combining total retinal thickness (TRT), OCT fundus image and fundoscopy resulted in a sensitivity of 76% and 81% specificity to detect signs of ICH. TRT was increased in patients who have had signs of ICH versus patients who never had signs of ICH (ß+44.9 µm in patients who have had ICH, 95%CI 9.0-80.8,P=0.01). TRT decreased to normal in the years after surgery (ß -3.6 µm/year, 95%CI -7.2 - -0.05, P=0.047). There were greater odds of having increased TRT in patients with HH (OR 2.9, 95%CI 1.1-7.6,P=0.03). CONCLUSIONS: The correlation between TRT, OCT fundus image, fundoscopy and particularly for the combination of these parameters with ICP surrogate markers is fair. Increased TRT in the presence of a clinical suspicion of ICH warrants further screening.

TinyFallNet: A Lightweight Pre-Impact Fall Detection Model.

Falls represent a significant health concern for the elderly. While studies on deep learning-based preimpact fall detection have been conducted to mitigate fall-related injuries, additional efforts are needed for embedding in microcomputer units (MCUs). In this study, ConvLSTM, the state-of-the-art model, was benchmarked, and we attempted to lightweight it by leveraging features from image-classification models VGGNet and ResNet while maintaining performance for wearable airbags. The models were developed and evaluated using data from young subjects in the KFall public dataset based on an inertial measurement unit (IMU), leading to the proposal of TinyFallNet based on ResNet. Despite exhibiting higher accuracy (97.37% < 98.00%) than the benchmarked ConvLSTM, the proposed model requires lower memory (1.58 MB > 0.70 MB). Additionally, data on the elderly from the fall data of the FARSEEING dataset and activities of daily living (ADLs) data of the KFall dataset were analyzed for algorithm validation. This study demonstrated the applicability of image-classification models to preimpact fall detection using IMU and showed that additional tuning for lightweighting is possible due to the different data types. This research is expected to contribute to the lightweighting of deep learning models based on IMU and the development of applications based on IMU data.

Quantifying Amide-Aromatic Interactions at Molecular and Atomic Levels: Experimentally Determined Enthalpic and Entropic Contributions to Interactions of Amide sp2O, N, C and sp3C Unified Atoms with Naphthalene sp2C Atoms in Water.

In addition to amide hydrogen bonds and the hydrophobic effect, interactions involving π-bonded sp2 atoms of amides, aromatics, and other groups occur in protein self-assembly processes including folding, oligomerization, and condensate formation. These interactions also occur in aqueous solutions of amide and aromatic compounds, where they can be quantified. Previous analysis of thermodynamic coefficients quantifying net-favorable interactions of amide compounds with other amides and aromatics revealed that interactions of amide sp2O with amide sp2N unified atoms (presumably C═O···H-N hydrogen bonds) and amide/aromatic sp2C (lone pair π, n-π*) are particularly favorable. Sp3C-sp3C (hydrophobic), sp3C-sp2C (hydrophobic, CH-π), sp2C-sp2C (hydrophobic, π-π), and sp3C-sp2N interactions are favorable, sp2C-sp2N interactions are neutral, while sp2O-sp2O and sp2N-sp2N self-interactions and sp2O-sp3C interactions are unfavorable. Here, from determinations of favorable effects of 14 amides on naphthalene solubility at 10, 25, and 45 °C, we dissect amide-aromatic interaction free energies into enthalpic and entropic contributions and find these vary systematically with amide composition. Analysis of these results yields enthalpic and entropic contributions to intrinsic strengths of interactions of amide sp2O, sp2N, sp2C, and sp3C unified atoms with aromatic sp2C atoms. For each interaction, enthalpic and entropic contributions have the same sign and are much larger in magnitude than the interaction free energy itself. The amide sp2O-aromatic sp2C interaction is enthalpy-driven and entropically unfavorable, consistent with direct chemical interaction (e.g., lone pair-π), while amide sp3C- and sp2C-aromatic sp2C interactions are entropy-driven and enthalpically unfavorable, consistent with hydrophobic effects. These findings are relevant for interactions involving π-bonded sp2 atoms in protein processes.

Propensity-matched analysis of robotic versus sternotomy approaches for mitral valve replacement.

To compare early and medium-term outcomes between robotic and sternotomy approaches for mitral valve replacement (MVR). Clinical data of 1393 cases who underwent MVR between January 2014 and January 2023 were collected and stratified into robotic MVR (n = 186) and conventional sternotomy MVR (n = 1207) groups. The baseline data of the two groups of patients were corrected by the propensity score matching (PSM) method. After matching, the baseline characteristics were not significant different between the two groups (standardized mean difference < 10%). Moreover, the rates of operative mortality (P = 0.663), permanent stroke (P = 0.914), renal failure (P = 0.758), pneumonia (P = 0.722), and reoperation (P = 0.509) were not significantly different. Operation, CPB and cross-clamp time were shorter in the sternotomy group. On the other hand, ICU stay time, post-operative LOS, intraoperative transfusion, and intraoperative blood loss were shorter or less in the robot group. Operation, CPB, and cross-clamp time in robot group were all remarkably improved with experience. Finally, all-cause mortality (P = 0.633), redo mitral valve surgery (P = 0.739), and valve-related complications (P = 0.866) in 5 years of follow-up were not different between the two groups. Robotic MVR is safe, feasible, and reproducible for carefully selected patients with good operative outcomes and medium-term clinical outcomes.

Quantifying Amide-Aromatic Interactions at Molecular and Atomic Levels: Experimentally-determined Enthalpic and Entropic Contributions to Interactions of Amide sp 2 O, N, C and sp 3 C Unified Atoms with Naphthalene sp 2 C Atoms in Water.

In addition to amide hydrogen bonds and the hydrophobic effect, interactions involving π-bonded sp 2 atoms of amides, aromatics and other groups occur in protein self-assembly processes including folding, oligomerization and condensate formation. These interactions also occur in aqueous solutions of amide and aromatic compounds, where they can be quantified. Previous analysis of thermodynamic coefficients quantifying net-favorable interactions of amide compounds with other amides and aromatics revealed that interactions of amide sp 2 O with amide sp 2 N unified atoms (presumably C=O···H-N hydrogen bonds) and amide/aromatic sp 2 C (lone pair-π, n-π * ) are particularly favorable. Sp 3 C-sp 3 C (hydrophobic), sp 3 C-sp 2 C (hydrophobic, CH-π), sp 2 C-sp 2 C (hydrophobic, π-π) and sp 3 C-sp 2 N interactions are favorable, sp 2 C-sp 2 N interactions are neutral, while sp 2 O-sp 2 O and sp 2 N-sp 2 N self-interactions and sp 2 O-sp 3 C interactions are unfavorable. Here, from determinations of favorable effects of fourteen amides on naphthalene solubility at 10, 25 and 45 °C, we dissect amide-aromatic interaction free energies into enthalpic and entropic contributions and find these vary systematically with amide composition. Analysis of these results yields enthalpic and entropic contributions to intrinsic strengths of interactions of amide sp 2 O, sp 2 N, sp 2 C and sp 3 C unified atoms with aromatic sp 2 C atoms. For each interaction, enthalpic and entropic contributions have the same sign and are much larger in magnitude than the interaction free energy itself. The amide sp 2 O-aromatic sp 2 C interaction is enthalpy-driven and entropically unfavorable, consistent with direct chemical interaction (e.g. lone pair-π) while amide sp 3 C- and sp 2 C-aromatic sp 2 C interactions are entropy-driven and enthalpically unfavorable, consistent with hydrophobic effects. These findings are relevant for interactions involving π-bonded sp 2 atoms in protein processes.

Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges.

The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.

Quantitative flow ratio-guided versus angiography-guided operation for valve disease accompanying coronary heart disease.

Background: Valve replacement combined with coronary artery bypass graft (CABG) operation (VR + CABG) is usually associated with higher mortality and complication rates. Currently, angiography remains the most commonly used approach to guide CABG. The aim of this study is to investigate whether a quantitative flow ratio (QFR)-guided strategy can improve the clinical outcomes of VR + CABG. Methods: Patients (n = 536) treated by VR + CABG between January 2018 and December 2021 were retrospectively assessed. In 116 patients, all lesions were revascularized entirely based on QFR (the QFR-guided group), whereas in 420 patients, all lesions were revascularized entirely based on angiography (the angiography-guided group). To minimize selection bias between the 2 groups, propensity score matching was performed at a ratio of 1:2. The primary endpoint of the study was the rate of major adverse cardiac and cerebrovascular events (MACCE) at 1-year, which was defined as a composite of cardiac mortality, myocardial infarction (MI), any repeat revascularization, and stroke. Results: No statistically significant differences were observed in the baseline clinical characteristics between the QFR-guided and angiography-guided groups after propensity score matching. The mean age of all patients was 66.2 years [standard deviation (SD) = 8.3], 370 (69%) were men, the mean body-mass index of the population was 24.8 kg/m2 (SD = 4.5), 129 (24%) had diabetes, and 229 (43%) had angina symptoms. When compared with the angiography-guided group, the QFR-guided group had a significantly shorter operative time (323 ± 60 min vs. 343 ± 71 min, P = 0.010), extra corporal circulation time (137 ± 38 min vs. 155 ± 62 min, P = 0.004), clamp time (73 ± 19 min vs. 81 ± 18 min, P < 0.001), and less intraoperative bleeding volume (640 ± 148 ml vs. 682 ± 166 ml, P = 0.022). Compared with the angiography-guided group, the 1-year MACCE was significantly lower in the QFR-guided group (6.9% vs. 14.7%, P = 0.036, hazard ratio = 0.455, 95% confidence interval: 0.211-0.982). Conclusion: Our results raise the hypothesis that among patients who undergo VR + CABG, QFR-guided strategy is associated with optimized surgical procedure and a superior clinical outcome, as evidenced by a lower rate of MACCE at 1-year compared with conventional angiography-guided strategy.

Quantification of Comfort for the Development of Binding Parts in a Standing Rehabilitation Robot.

Human-machine interfaces (HMI) refer to the physical interaction between a user and rehabilitation robots. A persisting excessive load leads to soft tissue damage, such as pressure ulcers. Therefore, it is necessary to define a comfortable binding part for a rehabilitation robot with the subject in a standing posture. The purpose of this study was to quantify the comfort at the binding parts of the standing rehabilitation robot. In Experiment 1, cuff pressures of 10-40 kPa were applied to the thigh, shank, and knee of standing subjects, and the interface pressure and pain scale were obtained. In Experiment 2, cuff pressures of 10-20 kPa were applied to the thigh, and the tissue oxygen saturation and the skin temperature were measured. Questionnaire responses regarding comfort during compression were obtained from the subjects using the visual analog scale and the Likert scale. The greatest pain was perceived in the thigh. The musculoskeletal configuration affected the pressure distribution. The interface pressure distribution by the binding part showed higher pressure at the intermuscular septum. Tissue oxygen saturation (StO2) increased to 111.9 ± 6.7% when a cuff pressure of 10 kPa was applied and decreased to 92.2 ± 16.9% for a cuff pressure of 20 kPa. A skin temperature variation greater than 0.2 °C occurred in the compressed leg. These findings would help evaluate and improve the comfort of rehabilitation robots.

Spinal infection caused by Coxiella burnetii.

BACKGROUND: Spinal infection caused by Coxiella burnetii is rare and difficult to diagnose. Here we reported a case of spinal infection from Coxiella burnetii detected by the metagenomic next-generation sequencing (mNGS). CASE PRESENTATION: A 66-year-old male farmer with no medical history reported severe sharp low back pain, numbness and lower limb weakness for three years. Magnetic resonance imaging (MRI) revealed bone destruction and spinal cord compression within L1 and L2. mNGS testing showed that the inspected specimen collected from spinal lesion was detected positively for Coxiella burnetii. After receiving the combined treatment of antibiotic therapy and surgical intervention, the patient recovered well, and the sagittal MRI showed that vertebral edema signals disappeared and the graft of bone fused 16 months after surgery. CONCLUSION: The mNGS may be benefit for early diagnosis and intervention of non-specific spinal infection, and future studies should validate its effectiveness for clinical use in spinal infections. Additionally, antibiotic therapy combined with surgical intervention plays an important role on the treatment of spinal infection caused by Coxiella burnetii.

Boosting of tau protein aggregation by CD40 and CD48 gene expression in Alzheimer's disease.

Neurodegenerative diseases result from the interplay of abnormal gene expression and various pathological factors. Therefore, a disease-specific integrative genetic approach is required to understand the complexities and causes of target diseases. Recent studies have identified the correlation between genes encoding several transmembrane proteins, such as the cluster of differentiation (CD) and Alzheimer's disease (AD) pathogenesis. In this study, CD48 and CD40 gene expression in AD, a neurodegenerative disease, was analyzed to infer this link. Total RNA sequencing was performed using an Alzheimer's disease mouse model brain and blood, and gene expression was determined using a genome-wide association study (GWAS). We observed a marked elevation of CD48 and CD40 genes in Alzheimer's disease. Indeed, the upregulation of both CD48 and CD40 genes was significantly increased in the severe Alzheimer's disease group. With the elevation of CD48 and CD40 genes in Alzheimer's disease, associations of protein levels were also markedly increased in tissues. In addition, overexpression of CD48 and CD40 genes triggered tau aggregation, and co-expression of these genes accelerated aggregation. The nuclear factor kappa B (NF-ĸB) signaling pathway was enriched by CD48 and CD40 gene expression: it was also associated with tau pathology. Our data suggested that the CD48 and CD40 genes are novel AD-related genes, and this approach may be useful as a diagnostic or therapeutic target for the disease.

Cryptic mutations of PLC family members in brain disorders: recent discoveries and a deep-learning-based approach.

Phospholipase C (PLC) is an essential isozyme involved in the phosphoinositide signalling pathway, which maintains cellular homeostasis. Gain- and loss-of-function mutations in PLC affect enzymatic activity and are therefore associated with several disorders. Alternative splicing variants of PLC can interfere with complex signalling networks associated with oncogenic transformation and other diseases, including brain disorders. Cells and tissues with various mutations in PLC contribute different phosphoinositide signalling pathways and disease progression, however, identifying cryptic mutations in PLC remains challenging. Herein, we review both the mechanisms underlying PLC regulation of the phosphoinositide signalling pathway and the genetic variation of PLC in several brain disorders. In addition, we discuss the present challenges associated with the potential of deep-learning-based analysis for the identification of PLC mutations in brain disorders.

sEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee.

sEMG-based gesture recognition is useful for human-computer interactions, especially for technology supporting rehabilitation training and the control of electric prostheses. However, high variability in the sEMG signals of untrained users degrades the performance of gesture recognition algorithms. In this study, the hand posture recognition algorithm and radar plot-based visual feedback training were developed using multichannel sEMG sensors. Ten healthy adults and one bilateral forearm amputee participated by repeating twelve hand postures ten times. The visual feedback training was performed for two days and five days in healthy adults and a forearm amputee, respectively. Artificial neural network classifiers were trained with two types of feature vectors: a single feature vector and a combination of feature vectors. The classification accuracy of the forearm amputee increased significantly after three days of hand posture training. These results indicate that the visual feedback training efficiently improved the performance of sEMG-based hand posture recognition by reducing variability in the sEMG signal. Furthermore, a bilateral forearm amputee was able to participate in the rehabilitation training by using a radar plot, and the radar plot-based visual feedback training would help the amputees to control various electric prostheses.

Finite-time flocking with collision-avoiding problem of a modified Cucker-Smale model.

To achieve collision-avoiding flocking in finite time, a modified Cucker-Smale model with general inter-driving force is proposed. First, it is proved that the system can achieve conditional collision-avoiding flocking in finite time by imposing appropriate restrictions on the initial states. Moreover, a special case of the inter-driving force is demonstrated. Last, the correctness of the results is verified through numerical simulations.

Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites.

Workers at construction sites are prone to fall-from-height (FFH) accidents. The severity of injury can be represented by the acceleration peak value. In the study, a risk prediction against FFH was made using IMU sensor data for accident prevention at construction sites. Fifteen general working movements (NF: non-fall), five low-hazard-fall movements, (LF), and five high-hazard-FFH movements (HF) were performed by twenty male subjects and a dummy. An IMU sensor was attached to the T7 position of the subject to measure the three-axis acceleration and angular velocity. The peak acceleration value, calculated from the IMU data, was 4 g or less in general work movements and 9 g or more in FFHs. Regression analysis was performed by applying various deep learning models, including 1D-CNN, 2D-CNN, LSTM, and Conv-LSTM, to the risk prediction, and then comparing them in terms of their mean absolute error (MAE) and mean squared error (MSE). The FFH risk level was estimated based on the predicted peak acceleration. The Conv-LSTM model trained by MAE showed the smallest error (MAE: 1.36 g), and the classification with the predicted peak acceleration showed the best accuracy (97.6%). This study successfully predicted the FFH risk levels and could be helpful to reduce fatal injuries at construction sites.

The use of OCT to detect signs of intracranial hypertension in patients with sagittal suture synostosis: Reference values and correlations.

PURPOSE: To obtain pediatric normative reference values and determine whether optical coherence tomography (OCT) corresponds better with clinical signs of intracranial hypertension (ICH) compared to the traditional screening method fundoscopy in a large cohort of one type of single suture craniosynostosis. METHODS: Control subjects without optic nerve diseases and isolated sagittal synostosis patients aged 3-10 years who underwent fundoscopy and OCT were included in this prospective cohort study. Normative reference values were obtained through bootstrap analysis. Main outcome was the association between peripapillary total retinal thickness (TRT) and total retinal volume (TRV) and appearance on fundoscopy. Signs and symptoms suggestive of ICH, including skull growth arrest, fingerprinting, and headache, were scored. RESULTS: Sixty-four healthy controls and 93 isolated sagittal synostosis patients were included. Normative cut-off values for mean TRT are < 256 µm and > 504 µm and for mean TRV < 0.21 mm3 and > 0.39 mm3. TRT was increased in 16 (17%) and TRV in 15 (16%) of 93 patients, compared to only 4 patients with papilledema on fundoscopy (4%). Both parameters were associated with papilledema on fundoscopy (OR = 16.7, p = 0.02, and OR = 18.2, p = 0.01). Skull growth arrest was significantly associated with abnormal OCT parameters (OR = 13.65, p < 0.01). CONCLUSIONS: The established cut-off points can be applied to screen for ICH in pediatrics. The present study detected abnormalities with OCT more frequent than with fundoscopy, which were associated with skull growth arrest. Therefore, a combination of OCT, fundoscopy, and skull growth arrest can improve clinical decision-making in craniosynostosis.