Different Positions of Weightbearing CT Images Can Influence the Hindfoot Alignment Evaluation Using 2-Dimensional Methodology.

BACKGROUND: Three-dimensional weightbearing CT (WBCT) has been widely used to assess foot and ankle alignment. However, most current measurement methods are based on 2-dimensional concepts-distance, long axis, angulation, etc-and are sensitive to changes in orientation of the WBCT images. This study demonstrated how changes in positioning of the image can significantly influence the evaluation of hindfoot alignment. METHODS: WBCT scans of 10 feet without deformities were used. In the horizontal view, the long axis of the foot image was aligned to a neutral position and then rotated 5, 10, 15, and 20 degrees both internally and externally to simulate malposition. The Calcaneal Moment Arm (CMA) was measured by 2 investigators independently in the above positions. An intraclass correlation coefficient (ICC) model was used to assess the intra- and interobserver reliabilities. The correlation between the CMA and the rotation angle of the foot images was analyzed using linear regression. RESULTS: The CMA measurements demonstrated that internal rotation of the image changed the neutral hindfoot into valgus, whereas external rotation led to varus. A 1-degree internal or external rotation of the image correlated with 0.48 (±0.03) mm increase or decrease in the modified WBCT-CMA value (P < .0001, R2 = 0.6406). CONCLUSION: This study demonstrated that although 3D WBCT provides the ability to observe the foot from different perspectives, current alignment evaluation tools are limited to 2 dimensions. Therefore, positioning the WBCT images in a consistent orientation is important to generate correct data.

Transcriptome meta-analysis of Kawasaki disease in humans and mice.

Kawasaki Disease (KD) affects young children less than five years old with severe blood vessel inflammation. Despite being treatable, the causes and mechanisms remain elusive. This study conducted a meta-analysis of RNA sequencing (RNA-seq) data from human and animal models to explore KD's transcriptomic profile and evaluate animal models. We retrieved bulk and single-cell RNA-seq data from Gene Expression Omnibus, with blood and coronary artery samples from KD patients, aorta samples from KD mouse models (Lactobacillus casei cell wall extract-injected mice), and their controls. Upon consistent quality control, we applied Fisher's exact test to assess differential gene expression, followed by an enrichment analysis of overlapping genes. These studies identified 400 differentially expressed genes in blood samples of KD patients compared to controls and 413 genes in coronary artery samples. The data from KD blood and KD coronary artery samples shared only 16 differentially expressed genes. Eighty-one genes overlapped between KD human coronary arteries and KD mouse aortas, and 67 of these 81 genes were regulated in parallel in both humans and mice: 30 genes were up-regulated, and 37 were down-regulated. These included previously identified KD-upregulated genes: CD74, SFRP4, ITGA4, and IKZF1. Gene enrichment analysis revealed significant alterations in the cardiomyopathy pathway. Single-cell RNAseq showed a few significant markers, with known KD markers like S100A9, S100A8, CD74, CD14, IFITM2, and IFITM3, being overexpressed in KD cohorts. Gene profiles obtained from KD human coronary artery are more compatible with data from aorta samples of KD mice than blood samples of KD humans, validating KD animal models for identifying therapeutic targets. Although blood samples can be utilized to discover novel biomarkers, more comprehensive single-cell sequencing is required to detail gene expression in different blood cell populations. This study identifies critical genes from human and mouse tissues to help develop new treatment strategies for KD.

Acute Lung Injury and the NLRP3 Inflammasome.

Acute lung injury (ALI) manifests through harm to the capillary endothelium and alveolar epithelial cells, arising from a multitude of factors, leading to scattered interstitial alterations, pulmonary edema, and subsequent acute hypoxic respiratory insufficiency. Acute lung injury (ALI), along with its more serious counterpart, acute respiratory distress syndrome (ARDS), carry a fatality rate that hovers around 30-40%. Its principal pathological characteristic lies in the unchecked inflammatory reaction. Currently, the main strategies for treating ALI are alleviation of inflammation and prevention of respiratory failure. Concerning the etiology of ALI, NLRP3 Inflammasome is essential to the body's innate immune response. The composition of this inflammasome complex includes NLRP3, the pyroptosis mediator ASC, and pro-caspase-1. Recent research has reported that the inflammatory response centered on NLRP3 inflammasomes plays a key part in inflammation in ALI, and may hence be a prospective candidate for therapeutic intervention. In the review, we present an overview of the ailment characteristics of acute lung injury along with the constitution and operation of the NLRP3 inflammasome within this framework. We also explore therapeutic strategies targeting the NLRP3 inflammasome to combat acute lung injury.

Development of multiplexed orthogonal base editor (MOBE) systems.

Base editors (BEs) enable efficient, programmable installation of point mutations while avoiding the use of double-strand breaks. Simultaneous application of two or more different BEs, such as an adenine BE (which converts A·T base pairs to G·C) and a cytosine BE (which converts C·G base pairs to T·A), is not feasible because guide RNA crosstalk results in non-orthogonal editing, with all BEs modifying all target loci. Here we engineer both adenine BEs and cytosine BEs that can be orthogonally multiplexed by using RNA aptamer-coat protein systems to recruit the DNA-modifying enzymes directly to the guide RNAs. We generate four multiplexed orthogonal BE systems that enable rates of precise co-occurring edits of up to 7.1% in the same DNA strand without enrichment or selection strategies. The addition of a fluorescent enrichment strategy increases co-occurring edit rates up to 24.8% in human cells. These systems are compatible with expanded protospacer adjacent motif and high-fidelity Cas9 variants, function well in multiple cell types, have equivalent or reduced off-target propensities compared with their parental systems and can model disease-relevant point mutation combinations.

Predicting the complexity and mortality of polytrauma patients with machine learning models.

We aim to develop machine learning (ML) models for predicting the complexity and mortality of polytrauma patients using clinical features, including physician diagnoses and physiological data. We conducted a retrospective analysis of a cohort comprising 756 polytrauma patients admitted to the intensive care unit (ICU) at Pizhou People's Hospital Trauma Center, Jiangsu, China between 2020 and 2022. Clinical parameters encompassed demographics, vital signs, laboratory values, clinical scores and physician diagnoses. The two primary outcomes considered were mortality and complexity. We developed ML models to predict polytrauma mortality or complexity using four ML algorithms, including Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost). We assessed the models' performance and compared the optimal ML model against three existing trauma evaluation scores, including Injury Severity Score (ISS), Trauma Index (TI) and Glasgow Coma Scale (GCS). In addition, we identified several important clinical predictors that made contributions to the prognostic models. The XGBoost-based polytrauma mortality prediction model demonstrated a predictive ability with an accuracy of 90% and an F-score of 88%, outperforming SVM, RF and ANN models. In comparison to conventional scoring systems, the XGBoost model had substantial improvements in predicting the mortality of polytrauma patients. External validation yielded strong stability and generalization with an accuracy of up to 91% and an AUC of 82%. To predict polytrauma complexity, the XGBoost model maintained its performance over other models and scoring systems with good calibration and discrimination abilities. Feature importance analysis highlighted several clinical predictors of polytrauma complexity and mortality, such as Intracranial hematoma (ICH). Leveraging ML algorithms in polytrauma care can enhance the prognostic estimation of polytrauma patients. This approach may have potential value in the management of polytrauma patients.

Functional EPAS1/HIF2A missense variant is associated with hematocrit in Andean highlanders.

Hypoxia-inducible factor pathway genes are linked to adaptation in both human and nonhuman highland species. EPAS1, a notable target of hypoxia adaptation, is associated with relatively lower hemoglobin concentration in Tibetans. We provide evidence for an association between an adaptive EPAS1 variant (rs570553380) and the same phenotype of relatively low hematocrit in Andean highlanders. This Andean-specific missense variant is present at a modest frequency in Andeans and absent in other human populations and vertebrate species except the coelacanth. CRISPR-base-edited human cells with this variant exhibit shifts in hypoxia-regulated gene expression, while metabolomic analyses reveal both genotype and phenotype associations and validation in a lowland population. Although this genocopy of relatively lower hematocrit in Andean highlanders parallels well-replicated findings in Tibetans, it likely involves distinct pathway responses based on a protein-coding versus noncoding variants, respectively. These findings illuminate how unique variants at EPAS1 contribute to the same phenotype in Tibetans and a subset of Andean highlanders despite distinct evolutionary trajectories.

Transcriptomic Analysis of the Rat Dorsal Root Ganglion After Fracture.

In fractures, pain signals are transmitted from the dorsal root ganglion (DRG) to the brain, and the DRG generates efferent signals to the injured bone to participate in the injury response. However, little is known about how this process occurs. We analyzed DRG transcriptome at 3, 7, 14, and 28 days after fracture. We identified the key pathways through KEGG and GO enrichment analysis. We then used IPA analysis to obtain upstream regulators and disease pathways. Finally, we compared the sequencing results with those of nerve injury to identify the unique transcriptome changes in DRG after fracture. We found that the first 14 days after fracture were the main repair response period, the 3rd day was the peak of repair activity, the 14th day was dominated by the stimulus response, and on the 28th day, the repair response had reached a plateau. ECM-receptor interaction, protein digestion and absorption, and the PI3K-Akt signaling pathway were most significantly enriched, which may be involved in repair regeneration, injury response, and pain transmission. Compared with the nerve injury model, DRG after fracture produced specific alterations related to bone repair, and the bone density function was the most widely activated bone-related function. Our results obtained some important genes and pathways in DRG after fracture, and we also summarized the main features of transcriptome function at each time point through functional annotation clustering of GO pathway, which gave us a deeper understanding of the role played by DRG in fracture.

Genomic Selection Signals in Andean Highlanders Reveal Adaptive Placental Metabolic Phenotypes That Are Disrupted in Preeclampsia.

BACKGROUND: The chronic hypoxia of high-altitude residence poses challenges for tissue oxygen supply and metabolism. Exposure to high altitude during pregnancy increases the incidence of hypertensive disorders of pregnancy and fetal growth restriction and alters placental metabolism. High-altitude ancestry protects against altitude-associated fetal growth restriction, indicating hypoxia tolerance that is genetic in nature. Yet, not all babies are protected and placental pathologies associated with fetal growth restriction occur in some Andean highlanders. METHODS: We examined placental metabolic function in 79 Andeans (18-45 years; 39 preeclamptic and 40 normotensive) living in La Paz, Bolivia (3600-4100 m) delivered by unlabored Cesarean section. Using a selection-nominated approach, we examined links between putatively adaptive genetic variation and phenotypes related to oxygen delivery or placental metabolism. RESULTS: Mitochondrial oxidative capacity was associated with fetal oxygen delivery in normotensive but not preeclamptic placenta and was also suppressed in term preeclamptic pregnancy. Maternal haplotypes in or within 200 kb of selection-nominated genes were associated with lower placental mitochondrial respiratory capacity (PTPRD [protein tyrosine phosphatase receptor-δ]), lower maternal plasma erythropoietin (CPT2 [carnitine palmitoyl transferase 2], proopiomelanocortin, and DNMT3 [DNA methyltransferase 3]), and lower VEGF (vascular endothelial growth factor) in umbilical venous plasma (TBX5 [T-box transcription factor 5]). A fetal haplotype within 200 kb of CPT2 was associated with increased placental mitochondrial complex II capacity, placental nitrotyrosine, and GLUT4 (glucose transporter type 4) protein expression. CONCLUSIONS: Our findings reveal novel associations between putatively adaptive gene regions and phenotypes linked to oxygen delivery and placental metabolic function in highland Andeans, suggesting that such effects may be of genetic origin. Our findings also demonstrate maladaptive metabolic mechanisms in the context of preeclampsia, including dysregulation of placental oxygen consumption.

Delineating highly transcribed noncoding elements landscape in breast cancer.

Highly transcribed noncoding elements (HTNEs) are critical noncoding elements with high levels of transcriptional capacity in particular cohorts involved in multiple cellular biological processes. Investigation of HTNEs with persistent aberrant expression in abnormal tissues could be of benefit in exploring their roles in disease occurrence and progression. Breast cancer is a highly heterogeneous disease for which early screening and prognosis are exceedingly crucial. In this study, we developed a HTNE identification framework to systematically investigate HTNE landscapes in breast cancer patients and identified over ten thousand HTNEs. The robustness and rationality of our framework were demonstrated via public datasets. We revealed that HTNEs had significant chromatin characteristics of enhancers and long noncoding RNAs (lncRNAs) and were significantly enriched with RNA-binding proteins as well as targeted by miRNAs. Further, HTNE-associated genes were significantly overexpressed and exhibited strong correlations with breast cancer. Ultimately, we explored the subtype-specific transcriptional processes associated with HTNEs and uncovered the HTNE signatures that could classify breast cancer subtypes based on the properties of hormone receptors. Our results highlight that the identified HTNEs as well as their associated genes play crucial roles in breast cancer progression and correlate with subtype-specific transcriptional processes of breast cancer.

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision.

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

Inflammation biomarkers in OSA, chronic obstructive pulmonary disease, and chronic obstructive pulmonary disease/OSA overlap syndrome.

STUDY OBJECTIVES: The coexistence of obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD) in a single individual, also known as overlap syndrome (OVS), is associated with higher cardiovascular risk and mortality than either OSA or COPD alone. However, the underlying mechanisms remain unclear. We hypothesized that patients with OVS have elevated systemic inflammatory biomarkers relative to patients with either disease alone, which could explain greater cardiovascular risk observed in OVS. METHODS: We included 255 participants in the study, 55 with COPD alone, 100 with OSA alone, 50 with OVS, and 50 healthy controls. All participants underwent a home sleep study, spirometry, and a blood draw for high-sensitivity C-reactive protein and total blood count analysis. In a randomly selected subset of 186 participants, inflammatory protein profiling was performed using Bio-Rad Bio-Plex Pro Human Cytokine 27-Plex Assays. Biomarker level differences across groups were identified using a mixed linear model. RESULTS: Levels of interleukin 6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), and granulocyte colony stimulating factor (G-CSF) were higher in participants with OVS and COPD compared with healthy controls and participants with OSA. Furthermore, participants with OVS had higher circulating levels of leukocytes and neutrophils than those with COPD, OSA, and controls. CONCLUSIONS: COPD and OVS are associated with higher systemic inflammation relative to OSA and healthy controls. This work proposes the potential utilization of interleukin 6, granulocyte colony stimulating factor, and high-sensitivity C-reactive protein as screening biomarkers for COPD in patients with OSA. Inflammatory pathways may not fully explain the higher cardiovascular risk observed in OVS, indicating the need for further investigation. CITATION: Sanchez-Azofra A, Gu W, Masso-Silva JA, et al. Inflammation biomarkers in OSA, chronic obstructive pulmonary disease, and chronic obstructive pulmonary disease/OSA overlap syndrome. J Clin Sleep Med. 2023;19(8):1447-1456.

In Silico Quantification of Intersubject Variability on Aerosol Deposition in the Oral Airway.

The extrathoracic oral airway is not only a major mechanical barrier for pharmaceutical aerosols to reach the lung but also a major source of variability in lung deposition. Using computational fluid dynamics, deposition of 1−30 µm particles was predicted in 11 CT-based models of the oral airways of adults. Simulations were performed for mouth breathing during both inspiration and expiration at two steady-state flow rates representative of resting/nebulizer use (18 L/min) and of dry powder inhaler (DPI) use (45 L/min). Consistent with previous in vitro studies, there was a large intersubject variability in oral deposition. For an optimal size distribution of 1−5 µm for pharmaceutical aerosols, our data suggest that >75% of the inhaled aerosol is delivered to the intrathoracic lungs in most subjects when using a nebulizer but only in about half the subjects when using a DPI. There was no significant difference in oral deposition efficiency between inspiration and expiration, unlike subregional deposition, which shows significantly different patterns between the two breathing phases. These results highlight the need for incorporating a morphological variation of the upper airway in predictive models of aerosol deposition for accurate predictions of particle dosimetry in the intrathoracic region of the lung.

Preserved peak exercise capacity in Andean highlanders with excessive erythrocytosis both before and after isovolumic hemodilution.

In chronic mountain sickness (CMS), increased blood oxygen (O2)-carrying capacity due to excessive erythrocytosis (EE, [Hb] ≥ 21 g/dL) could be offset, especially during exercise by both impaired cardiac output (Q̇t) and O2 diffusion limitation in lungs and muscle. We hypothesized that EE results in reduced peak V̇o2 despite increased blood O2-carrying capacity, and that isovolumic hemodilution (IVHD) improves exercise capacity. In 14 male residents of Cerro de Pasco, Peru (4,340 m), six with and eight without EE, we measured peak cycle-exercise capacity, V̇o2, Q̇t, arterial blood gas parameters, and (resting) blood volume. This was repeated for participants with EE after IVHD, reducing hematocrit by 20% (from 67% to 53%). From these data, we quantified the major O2 transport pathway components (ventilation, pulmonary alveolar-capillary diffusion, Q̇t, and blood-muscle mitochondria diffusion). Participants with EE had similar peak V̇o2, systemic O2 delivery, and O2 extraction as non-EE controls, however, with lower Q̇t and higher arterial [O2]. After IVHD, peak V̇o2 was preserved (but not enhanced), with lower O2 delivery (despite higher Q̇t) balanced by greater O2 extraction. The considerable variance in exercise capacity across the 14 individuals was explained essentially completely by differences in both pulmonary and muscle O2 diffusional conductances and not by any differences in ventilation, [Hb], nor Q̇t. In conclusion, EE does not result in lower peak V̇o2 in Andean males, and IVHD maintains, but does not enhance, exercise capacity.NEW & NOTEWORTHY Male Andean highlanders with and without excessive erythrocytosis (EE) have similar peak V̇o2 at 4,340 m, with higher arterial [O2] in EE and lower cardiac output (Q̇t), thus maintaining similar O2 delivery. Peak V̇o2 in participants with EE was unaffected by isovolumic hemodilution (hematocrit reduced from 67% to 53%), with lower O2 delivery balanced by slightly increased Q̇t and greater O2 extraction. Differences in lung and muscle diffusing capacity, and not hematocrit variation, accounted for essentially all interindividual variance in peak V̇o2.

Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics.

Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.

Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique.

BACKGROUND: The recent development of artificial intelligence (AI) technologies coupled with medical imaging data has gained considerable attention, and offers a non-invasive approach for cancer diagnosis and prognosis. In this context, improved breast cancer (BC) molecular characteristics assessment models are foreseen to enable personalized strategies with better clinical outcomes compared to existing screening strategies. And it is a promising approach to developing models for hormone receptors (HR) and subtypes of BC patients from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. METHODS: In this institutional review board-approved study, 174 BC patients with both DCE-MRI and RNA-seq data in the local database were analyzed. Slice images from tumor lesions and multi-scale peri-tumor regions were used as model inputs, and five representative pre-trained transfer learning (TF) networks, such as Inception-v3 and Xception, were employed to establish prediction models. A comprehensive analysis was performed using five-fold cross-validation to avoid overfitting, and accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) to evaluate model performance. RESULTS: Xception achieved the superior results when using solely tumor regions, with highest AUROCs of 0.844 (95% CI: [0.841, 0.847]) and 0.784 (95% CI: [0.781, 0.788]) for estrogen receptor (ER) and progesterone receptor (PR), respectively, and best ACC of 0.467 (95% CI: [0.462, 0.470]) for PAM50 subtypes. A significant improvement in the model performance was observed when images of the peri-tumor region were included, with optimal results achieved using images of the tumor and the 10 mm peri-tumor regions. Xception-based TF models performed most effectively in predicting ER and PR statuses, with the AUROCs were 0.942 (95% CI: [0.940, 0.944]) and 0.920 (95% CI: [0.917, 0.922]), respectively, whereas for PAM50 subtypes, the Inception-v3-based network yielded the highest ACC as 0.742 (95% CI: [0.738, 0.746]). CONCLUSIONS: Transfer learning analysis based on DCE-MRI data of tumor and peri-tumor regions was helpful to the non-invasive assessment of molecular characteristics of BC.

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced-MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer.

Background: To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods: Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial-temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results: Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions: Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.

Circular RNAs in peripheral blood mononuclear cells are more stable than linear RNAs upon sample processing delay.

Circular RNAs (circRNAs) are a novel class of RNAs with closed loop structure. Blood circRNAs are widely acknowledged to be more stable than linear mRNAs, which show promising prospect to be liquid biopsy biomarkers for clinical applications. However, accumulating studies have demonstrated that sample processing delays have profound effects on blood transcriptome expression profiles, wherein knowledge remains elusive about the impacts of prolonged sample processing on blood expression profiles of circRNAs. We collected whole blood samples from three donors and isolated peripheral blood mononuclear cells (PBMCs) at six different incubation time points. We measured total RNA expression profiles using RNA sequencing (RNA-seq) and investigated the differentially expressed circRNAs, mRNAs and lncRNAs upon blood processing delay. Meanwhile, we explored the underlying inducement of aberrant expression of circRNAs against their corresponding mRNA transcripts. Finally, we utilized rMATS-turbo and CIRI-AS, respectively, to screen out differential alternative splicing (AS) events in linear mRNAs and circRNAs. Sample incubation at 4°C lasting to 48 hours (h) led to minimal effects to circRNAs' expression. However, it induced extensive alterations for mRNAs and lncRNAs when the incubation time was beyond 12 h. Additionally, only 2 h processing delays may result in profound impacts on AS events of linear mRNAs, while less impact on the equivalence of circRNAs. Our results suggested that PBMC circRNAs are stable upon sample processing delay, which are more suitable to be liquid biopsy biomarkers.

Time Domains of Hypoxia Responses and -Omics Insights.

The ability to respond rapidly to changes in oxygen tension is critical for many forms of life. Challenges to oxygen homeostasis, specifically in the contexts of evolutionary biology and biomedicine, provide important insights into mechanisms of hypoxia adaptation and tolerance. Here we synthesize findings across varying time domains of hypoxia in terms of oxygen delivery, ranging from early animal to modern human evolution and examine the potential impacts of environmental and clinical challenges through emerging multi-omics approaches. We discuss how diverse animal species have adapted to hypoxic environments, how humans vary in their responses to hypoxia (i.e., in the context of high-altitude exposure, cardiopulmonary disease, and sleep apnea), and how findings from each of these fields inform the other and lead to promising new directions in basic and clinical hypoxia research.

Isovolemic hemodilution in chronic mountain sickness acutely worsens nocturnal oxygenation and sleep apnea severity.

STUDY OBJECTIVES: Chronic mountain sickness (CMS) is commonly observed among Andean and other highland populations. Sleep-disordered breathing (SDB) is highly prevalent at high altitude, and SDB and nocturnal hypoxemia have been observed in CMS. Phlebotomy is commonly performed to treat CMS, but it is unknown whether reducing hematocrit improves SDB. We hypothesized that isovolemic hemodilution (IVHD) in CMS would reduce SBD severity and improve sleep efficiency. METHODS: Six participants with CMS and 8 without CMS, all residents of Cerro de Pasco, Peru (altitude 4340 m), completed baseline nocturnal sleep studies. CMS participants then underwent IVHD, and nocturnal sleep studies were repeated 24-48 hours after IVHD. We analyzed sleep apnea severity, nocturnal oxygenation, and sleep quality in those with CMS relative to those without CMS, and the effects of IVHD in CMS participants. RESULTS: Participants with CMS did not have altered sleep architecture, sleep apnea severity, or nocturnal oxygenation relative to non-CMS participants. However, IVHD in CMS increased apnea-hypopnea index (40.9 ± 6.9 events/h to 61.5 ± 7.7 events/h, P = .009). IVHD increased oxyhemoglobin desaturation index (P = .008) and the percentage of sleep time spent with oxyhemoglobin saturation at or below 80% (P = .012). There was no effect of IVHD on sleep efficiency, arousal index, or sleep staging. CONCLUSIONS: In this cohort, CMS was not associated with worsened SDB or changes in sleep architecture. IVHD, a putative therapeutic option for participants with CMS, appears to worsen nocturnal oxygenation and SDB within 48 hours post-IVHD. CITATION: Sanchez-Azofra A, Villafuerte FC, DeYoung PN, et al. Isovolemic hemodilution in chronic mountain sickness acutely worsens nocturnal oxygenation and sleep apnea severity. J Clin Sleep Med. 2022;18(10):2423-2432.