Roles and Mechanisms of Obstructive Sleep Apnea-Hypopnea Syndrome and Chronic Intermittent Hypoxia in Atherosclerosis: Evidence and Prospective

The morbidity and mortality of obstructive sleep apnea-hypopnea syndrome (OSAHS) are regarded as consequences of its adverse effects on the cardiovascular system. Chronic intermittent hypoxia (CIH) induced by OSAHS can result in vascular endothelial injury, thus promoting development of atherosclerosis (AS). Studies have shown that CIH is an independent risk factor for the occurrence and development of AS, but the underlying mechanism remains unclear. Here, we review clinical and fundamental studies reported during the last 10 years on the occurrence and development of AS mediated by CIH, focusing on inflammation, oxidative stress, insulin resistance, cell apoptosis, vascular endothelial injury, platelet activation, and neuroendocrine disorders. This review will offer current evidence and perspective to researchers for the development of effective intervention strategies for OSAHS-related cardiocerebrovascular diseases

Plant development regulated by cytokinin sinks

Morphogenetic signals control the patterning of multicellular organisms. Cytokinins are mobile signals that are perceived by subsets of plant cells. We found that the responses to cytokinin signaling during Arabidopsis development are constrained by the transporter PURINE PERMEASE 14 (PUP14). In our experiments, the expression of PUP14 was inversely correlated to the cytokinin signaling readout. Loss of PUP14 function allowed ectopic cytokinin signaling accompanied by aberrant morphogenesis in embryos, roots, and the shoot apical meristem. PUP14 protein localized to the plasma membrane and imported bioactive cytokinins, thus depleting apoplastic cytokinin pools and inhibiting perception by plasma membrane–localized cytokinin sensors to create a sink for active ligands. We propose that the spatiotemporal cytokinin sink patterns established by PUP14 determine the cytokinin signaling landscape that shapes the morphogenesis of land plants

InPrePPI: an integrated evaluation method based on genomic context for predicting protein-protein interactions in prokaryotic genomes

Background Although many genomic features have been used in the prediction of protein-protein interactions (PPIs), frequently only one is used in a computational method. After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration. So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison. It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features. Results In this study, we first performed a systematic evaluation on the PPI prediction in Escherichia coli (E. coli) by four genomic context based methods: the phylogenetic profile method, the gene cluster method, the gene fusion method, and the gene neighbor method. The number of predicted PPIs and the average degree in the predicted PPI networks varied greatly among the four methods. Further, no method outperformed the others when we tested using three well-defined positive datasets from the KEGG, EcoCyc, and DIP databases. Based on these comparisons, we developed a novel integrated method, named InPrePPI. InPrePPI first normalizes the AC value (an integrated value of the accuracy and coverage) of each method using three positive datasets, then calculates a weight for each method, and finally uses the weight to calculate an integrated score for each protein pair predicted by the four genomic context based methods. We demonstrate that InPrePPI outperforms each of the four individual methods and, in general, the other two existing integrated methods: the joint observation method and the integrated prediction method in STRING. These four methods and InPrePPI are implemented in a user-friendly web interface. Conclusion This study evaluated the PPI prediction by four genomic context based methods, and presents an integrated evaluation method that shows better performance in E. coli

Neurotrophic Signaling Factors in Brain Ischemia/Reperfusion Rats: Differential Modulation Pattern between Single-Time and Multiple Electroacupuncture Stimulation

Electroacupuncture (EA) treatment has been widely used for stroke-like disorders in traditional Chinese medicine. However, the underlying mechanisms remain unclear. Our previous studies showed that single-time EA stimulation at “Baihui” (GV 20) and “Shuigou” (GV 26) after the onset of ischemia can protect the brain against ischemic injury in rats with middle cerebral artery occlusion (MCAO). Here, we further investigated the differential effects between multiple EA and single-time EA stimulation on ischemic injury. In the present study, we found that both single-time EA and multiple EA stimulation significantly reduced MCAO-induced ischemic infarction, while only multiple EA attenuated sensorimotor dysfunctions. Also, with PCR array screening and ingenuity gene analysis, we revealed that multiple EA and single-time EA stimulation could differentially induce expression changes in neurotrophic signaling related genes. Meanwhile, with western blotting, we demonstrated that the level of glia maturation factor β (GMFβ) increased in the early stage (day 1) of reperfusion, and this upregulation was suppressed only by single-time EA stimulation. These findings suggest that the short-term effect of single-time EA stimulation differs from the cumulative effect of multiple EA, which possibly depends on their differential modulation on neurotrophic signaling molecules expression

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

Abstract Objective Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Methods Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. Results This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. Conclusion The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.This study was supported in part by grants from the NHLBI 5U19HL065962 and the NCI R01CA141307. ML is supported by the NLM training grant 3T15LM007450-08S1. JS is partially supported by the 2010 NARSAD Young Investigator Award. ZZ is partially supported by the 2009 NARSAD Maltz Investigator Award. MM is supported by a Veterans Administration HSR&D Career Development Award (CDA-08-020)

Clinical characteristics and prognosis of sudden sensorineural hearing loss in single-sided deafness patients

BackgroundSudden sensorineural hearing loss (SSNHL) in patients with single-sided deafness (SSD) is rare. The prognosis of the sole serviceable hearing ear is very important for these patients. However, the clinical characteristics and prognosis of SSNHL in SSD patients are not well-documented.ObjectiveThis study aimed to investigate the clinical features and treatment outcomes of SSNHL in SSD patients.MethodsClinical data of 36 SSD patients and 116 non-SSD patients with unilateral SSNHL from January 2013 to December 2022 were retrospectively investigated. The clinical characteristics of the SSD patients were analyzed. All SSD patients were treated with intratympanic steroids plus intravenous steroids. Pure-tone average (PTA) and word recognition score (WRS) before and after treatment were recorded. The hearing recovery of SSNHL in SSD patients in comparison with non-SSD patients was explored. Auditory outcomes in SSD patients with different etiologies were also compared.ResultsInitial hearing threshold showed no significant differences between the SSD group and the non-SSD group (66.41 ± 24.64 dB HL vs. 69.21 ± 31.48 dB HL, p = 0.625). The SSD group had a higher post-treatment hearing threshold (median (interquartile range, IQR) 53.13(36.56) dB HL) than the non-SSD group (median 32.50(47.5) dB HL, p < 0.01). Hearing gains (median 8.75(13.00) dB) and the rate of significant recovery (13.89%) were lower in the SSD group than in the non-SSD group (median 23.75(34.69) dB, 45.69%). The etiology of SSD was classified as SSNHL, special types of infection, chronic otitis media, and unknown causes. SSNHL accounted for the maximum proportion (38.9%) of causes of SSD in the SSD group. Hearing gains were lower in the SSNHL-SSD group than in other causes of the SSD group. A binary logistic regression analysis demonstrated that SSD serves as an indicator of unfavorable hearing recovery outcomes (OR = 5.264, p < 0.01).ConclusionThe prognosis of SSNHL in SSD patients is unsatisfactory. SSNHL accounts for the maximum proportion of causes of SSD in this group of patients. For SSD patients caused by SSNHL, less hearing improvement after treatment was expected when SSNHL occurred in the contralateral ear in comparison with SSD patients with other causes