Crotonylation of NAE1 Modulates Cardiac Hypertrophy via Gelsolin Neddylation.

BACKGROUND: Cardiac hypertrophy and its associated remodeling are among the leading causes of heart failure. Lysine crotonylation is a recently discovered posttranslational modification whose role in cardiac hypertrophy remains largely unknown. NAE1 (NEDD8 [neural precursor cell expressed developmentally downregulated protein 8]-activating enzyme E1 regulatory subunit) is mainly involved in the neddylation modification of protein targets. However, the function of crotonylated NAE1 has not been defined. This study aims to elucidate the effects and mechanisms of NAE1 crotonylation on cardiac hypertrophy. METHODS: Crotonylation levels were detected in both human and mouse subjects with cardiac hypertrophy through immunoprecipitation and Western blot assays. Tandem mass tag (TMT)-labeled quantitative lysine crotonylome analysis was performed to identify the crotonylated proteins in a mouse cardiac hypertrophic model induced by transverse aortic constriction. We generated NAE1 knock-in mice carrying a crotonylation-defective K238R (lysine to arginine mutation at site 238) mutation (NAE1 K238R) and NAE1 knock-in mice expressing a crotonylation-mimicking K238Q (lysine to glutamine mutation at site 238) mutation (NAE1 K238Q) to assess the functional role of crotonylation of NAE1 at K238 in pathological cardiac hypertrophy. Furthermore, we combined coimmunoprecipitation, mass spectrometry, and dot blot analysis that was followed by multiple molecular biological methodologies to identify the target GSN (gelsolin) and corresponding molecular events contributing to the function of NAE1 K238 (lysine residue at site 238) crotonylation. RESULTS: The crotonylation level of NAE1 was increased in mice and patients with cardiac hypertrophy. Quantitative crotonylomics analysis revealed that K238 was the main crotonylation site of NAE1. Loss of K238 crotonylation in NAE1 K238R knock-in mice attenuated cardiac hypertrophy and restored the heart function, while hypercrotonylation mimic in NAE1 K238Q knock-in mice significantly enhanced transverse aortic constriction-induced pathological hypertrophic response, leading to impaired cardiac structure and function. The recombinant adenoviral vector carrying NAE1 K238R mutant attenuated, while the K238Q mutant aggravated Ang II (angiotensin II)-induced hypertrophy. Mechanistically, we identified GSN as a direct target of NAE1. K238 crotonylation of NAE1 promoted GSN neddylation and, thus, enhanced its protein stability and expression. NAE1 crotonylation-dependent increase of GSN promoted actin-severing activity, which resulted in adverse cytoskeletal remodeling and progression of pathological hypertrophy. CONCLUSIONS: Our findings provide new insights into the previously unrecognized role of crotonylation on nonhistone proteins during cardiac hypertrophy. We found that K238 crotonylation of NAE1 plays an essential role in mediating cardiac hypertrophy through GSN neddylation, which provides potential novel therapeutic targets for pathological hypertrophy and cardiac remodeling.

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.

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.

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.

Prediction of Alzheimer's disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening.

Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover Alzheimer's disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of phospholipase c gamma-1 (PLCγ1) gene, using genome-wide association study (GWAS) and a deep learning-based exon splicing prediction tool. GWAS revealed that the identified single-nucleotide variations were mainly distributed in the H3K27ac-enriched region of PLCγ1 gene body during brain development in an AD mouse model. A deep learning analysis, trained with human genome sequences, predicted 14 splicing sites in human PLCγ1 gene, and one of these completely matched with an SNV in exon 27 of PLCγ1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLCγ1 gene is associated with abnormal splicing during messenger RNA maturation. Taken together, our findings suggest that this approach, which combines in silico and deep learning-based analyses, has potential for identifying the clinical utility of critical SNVs in AD prediction.

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.

Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment.

Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impairments, identifying specific joint disorders, and a combined model for both tasks. Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature extraction, and models were fine-tuned using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB). The IMU-based system achieved over 91% accuracy in classifying the three types of gait. In contrast, the walkway system achieved less than 77% accuracy in classifying the three types of gait, primarily due to high misclassification rates between knee and ankle joint impairments. The IMU-based system shows promise for accurate gait assessment in patients with joint impairments, suggesting future research for clinical application improvements in rehabilitation and patient management.

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.

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.

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.

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.

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.

Molecular landscape of long noncoding RNAs in brain disorders.

According to current paradigms, various risk factors, such as genetic mutations, oxidative stress, neural network dysfunction, and abnormal protein degradation, contribute to the progression of brain disorders. Through the cooperation of gene transcripts in biological processes, the study of noncoding RNAs can lead to insights into the cause and treatment of brain disorders. Recently, long noncoding RNAs (lncRNAs) which are longer than 200 nucleotides in length have been suggested as key factors in various brain disorders. Accumulating evidence suggests the potential of lncRNAs as diagnostic or prognostic biomarkers and therapeutic targets. High-throughput screening-based sequencing has been instrumental in identification of lncRNAs that demand new approaches to understanding the progression of brain disorders. In this review, we discuss the recent progress in the study of lncRNAs, and addresses the pathogenesis of brain disorders that involve lncRNAs and describes the associations of lncRNAs with neurodegenerative disorders such as Alzheimer disease (AD), Parkinson disease (PD), and neurodevelopmental disorders. We also discuss potential targets of lncRNAs and their promise as novel therapeutics and biomarkers in brain disorders.

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.

Accuracy of Detecting Obstructive Sleep Apnea Using Ambulatory Sleep Studies in Patients With Syndromic Craniosynostosis.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) is seen in up to two-third of the patients with syndromic craniosynostosis. Gold standard to diagnose OSA is the hospital-based polysomnography, although alternatively ambulatory home sleep apnea devices are available. Aim of this study was to assess (1) accuracy of ambulatory sleep studies, (2) clinical decision making following sleep studies, and (3) course of OSA during long-term follow-up. MATERIALS AND METHODS: A retrospective cohort study was performed in children with syndromic craniosynostosis, of whom polysomnographies and home sleep apnea device recordings were collected. Measurements of apnea-hypopnea index, respiratory event index, total sleep/recording time, heart rate, oxygen saturation, and oxygen desaturation index were derived from the sleep studies. Primary clinical care subsequent to the sleep studies was determined using electronic patient files. RESULTS: In total, 123 patients were included, with 149 polysomnographies and 108 ambulatory studies. Performing an ambulatory study was associated with increased age at time of measurement (OR=1.1, 95% CI=1.02 to 1.17, P =0.01). No significant difference was found between the 2 types of sleep studies regarding sleep study parameters. Subsequent to sleep studies, patients with no-mild OSA had expectant care whereas patients with moderate-severe OSA underwent OSA-related treatment. OSA was most prevalent up to the age of 5 years, but also noticeable after the age of 10 years in patients with the Crouzon syndrome. CONCLUSIONS: Ambulatory sleep studies are reliable for diagnosing OSA in older children and can be used to determine clinical decision-making. Hence, we recommend implementing ambulatory sleep studies in a protocolized management.

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.

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.

The death risk factors of patients undergoing re-exploration for bleeding or tamponade after isolated off-pump coronary artery bypass grafting: a case-control study.

BACKGROUND: The purpose of the study is to identify off-pump patients who are at higher risk of mortality after re-exploration for bleeding or tamponade. METHODS: We analyzed the data of 3256 consecutive patients undergoing isolated off-pump coronary artery bypass grafting (OPCABG) in our heart center from 2013 through 2020. Fifty-eight patients underwent re-exploration after OPCABG. The 58 patients were divided into death group and survival group according to their discharge status. Propensity score matching (PSM) was performed to analysis the risk factors of death. 15 pairs of cases of two groups were matched well. RESULTS: The mortality rate of patients underwent re-exploration after OPCABG for bleeding or tamponade was 27.59% (16/58). In the raw data, we found the patients in death group had higher body mass index (BMI) (P = 0.030), higher cardiac troponin T (cTnT) (P = 0.028) and higher incidence of heart failure before OPCABG (P = 0.003). After PSM, the levels of lactic acid before and after re-exploration (P = 0.028 and P < 0.001) were higher in death group. And the levels of creatinine (P = 0.002) and cTnT (P = 0.017) were higher in the death group after re-exploration. The death group had longer reoperation time (P = 0.010). In addition, the perioperative utilization rate of intra-aortic ballon pump (IABP) (P = 0.027), continuous renal replacement therapy (CRRT) (P < 0.001) and platelet transfusion (P = 0.017) were higher than survival group. CONCLUSIONS: The mortality rate of patients undergoing re-exploration for bleeding or tamponade after isolated OPCABG is high. More attention should be paid to patients with above risk factors and appropriate measures should be taken in time.

Adult Saethre-Chotzen Syndrome: A Unique Abnormal Breathing Pattern.

ABSTRACT: A 35-year-old male with Saethre-Chotzen syndrome presented with severe complaints. Neuroimaging showed a Chiari-I malformation, mild ventriculomegaly, a syrinx of the wide central canal, and various cerebral vascular anomalies including a large occipital emissary vein on the right. Ultrasound of this vein confirmed blocking of the outflow-track when turning his head to the right, which also provoked the headaches and bruit. Polysomnography revealed severe positional sleep apnea with a mixed breathing pattern, the central components consisted of periodic breathing with, at times, crescendo-decrescendo reminiscent of a Cheyne-Stokes versus Biot breathing pattern, pointing to possible brain stem/pontine problems. Continuous positive airway pressure was initiated, and the patient was instructed to avoid sleeping in the right lateral position. One year later, nearly all his complaints have resolved. A questionnaire was sent to all adult Saethre-Chotzen patients in our craniofacial unit, none reported any of the severe symptoms as described by our index case.

Collismycin C reduces HMGB1-mediated septic responses and improves survival rate in septic mice.

We examined the effects of a 2,2'-bipyridine containing natural product, collismycin C on high mobility group box 1 (HMGB1, septic mediator)-mediated septic responses and survival rate in a mouse sepsis model. Collismycin C inhibited the HMGB1 release and downregulated HMGB1-mediated inflammatory responses in human endothelial cells. Collismycin C also inhibited HMGB1-induced hyperpermeability and leukocyte migration in mice. In addition, collismycin C treatment reduced CLP-induced HMGB1 release and sepsis-related mortality and pulmonary damage in vivo. Our results indicate that collismycin C is a potential therapeutic agent for the treatment of severe vascular inflammatory diseases by inhibiting HMGB1 signaling pathway.