Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.Comment: publishe

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.0443

A retrospective study: does upper airway morphology differ between non-positional and positional obstructive sleep apnea?

Objective The objective of this study was to explore the differences in upper airway morphology between positional (POSA) and non-positional (NPOSA) obstructive sleep apnea. Methods This retrospective study enrolled 75 patients (45 NPOSA and 30 POSA) who underwent polysomnography (PSG) and computed tomography (CT). The differences in, and relationships of, the PSG values and CT data between POSA and NPOSA were analyzed. Results Significant (p < 0.05) differences between the two groups were found in the apnea/hypopnea index (AHI), lateral-AHI (L-AHI), soft palate length (SPL), cross-sectional palatopharyngeal area, and the coronal diameter (CD) of the palatopharyngeal area at the narrowest part of the glossopharynx, which were all higher in POSA, except for SPL, AHI, and L-AHI. L-AHI was correlated with the cross-sectional area (r =  − 0.306, p = 0.008) and CD (r =  − 0.398, p < 0.001) of the palatopharyngeal area, the cross-sectional area (r =  − 0.241, p = 0.038) and CD (r =  − 0.297, p = 0.010) of the narrowest level of the glossopharynx, the CD of the glossopharynx (r = 0.284, p = 0.013), body mass index (BMI, r = 0.273, p = 0.018), SPL (r = 0.284, p = 0.014), and vallecula-tip of tongue (r = 0.250, p = 0.030). The SPL and CD at the narrowest part of the glossopharynx were included in the simplified screening model. Conclusions In NPOSA, the CD of the upper airway was smaller, and the soft palate was longer, than in POSA. These differences may play significant roles in explaining the main differences between NPOSA and POSA

Analysis of the Salivary Microbiome in Obstructive Sleep Apnea Syndrome Patients

Background. Oral microbiota plays an important role in oral and systemic diseases, while few reports referred to obstructive sleep apnea syndrome (OSAS). Thus, this study aimed to explore the different salivary microbiome in patients with OSAS and controls. Materials and Methods. Saliva was collected from 15 OSAS patients and nine healthy controls, and bacterial genomic DNA was extracted for 16S rRNA amplicon sequencing based on the Illumina platform. Results. The alpha and beta diversities were not significantly different between patients with OSAS and controls. The main phyla in the two groups were Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria, and Fusobacteria, which accounted for 95% of the abundance. The main genera were Streptococcus, Rothia, Actinomyces, Prevotella, and Neisseria. Based on the genus and operational taxonomic units, Peptostreptococcus, Alloprevotella, and Granulicatella were enriched in controls, while only Scardovia species were significantly more abundant in patients with OSAS. Conclusions. There was no significant difference in the relative abundance of bacteria between OSAS and controls. So, further studies will need to focus on the metagenome of bacteria in OSAS patients

An effective model for screening obstructive sleep apnea: a large-scale diagnostic study.

BACKGROUND: Obstructive sleep apnea (OSA) causes high morbidity and mortality and is independently associated with an increased likelihood of multiple complications. The diagnosis of OSA is presently time-consuming, labor-intensive and inaccessible. AIM: This study sought to develop a simple and efficient model for identifying OSA in Chinese adult population. METHODS: In this study, the efficiency of Epworth Sleepiness Scale (ESS) and a new established prediction model for screening OSA were evaluated in the test cohort (2,032 participants) and confirmed in an independent validation cohort (784 participants). RESULTS: In the test cohort, a high specificity (82.77%, 95% confidence interval [CI], 77.36-87.35) and a moderate sensitivity (61.65%, 95% CI, 59.35-63.91) were obtained at the threshold of nine for the ESS alone. Notably, sex-stratified analysis revealed different optimum cut-off points: nine for males and six for females. The new generated screening model, including age, waist circumference, ESS score, and minimum oxygen saturation (SaO2) as independent variables, revealed a higher sensitivity (89.13%, 95% CI, 87.60-90.53) and specificity (90.34%, 95% CI, 85.85-93.77) at the best cut-off point. Through receiver operating characteristics curve analysis, the area under the receiver operating characteristics curve of the model was found significantly larger than that of the ESS alone (0.955 vs. 0.774, P<0.0001). All these results were confirmed in the validation cohort. CONCLUSIONS: A practical screening model comprising minimum SaO2 and other parameters could efficiently identify undiagnosed OSA from the high-risk patients. Additionally, a sex-specific difference should be considered if the ESS alone is used

Effect of the Interaction between Obstructive Sleep Apnea and Lipoprotein(a) on Insulin Resistance: A Large-Scale Cross-Sectional Study

Both obstructive sleep apnea (OSA) and decreased serum lipoprotein(a) (Lp(a)) concentrations are associated with insulin resistance. However, their interaction effect on insulin resistance has never been investigated. Therefore, we performed a cross-sectional study on OSA-suspected Chinese Han participants. Laboratory-based polysomnographic variables, biochemical indicators, anthropometric measurements, and medical history were collected. Linear regression and binary logistic regression analyses with interaction terms were used to investigate the potential effects of the interaction between the severity of OSA (assessed by the apnea-hypopnea index (AHI)) and Lp(a) concentrations on insulin resistance (assessed by the homeostasis model assessment of insulin resistance (HOMA-IR)), after adjusting for potential confounders including age, gender, body mass index, waist-to-hip circumference ratio, mean arterial pressure, smoking status, drinking status, and lipid profiles. A total of 4,152 participants were enrolled. In the OSA-suspected population, AHI positively correlated with insulin resistance and serum Lp(a) concentrations independently and inversely correlated with insulin resistance. In addition, the interaction analysis showed that the linear association between lgAHI and lgHOMA-IR was much steeper and more significant in subjects with relatively low Lp(a) concentrations, suggesting a significant positive interaction between lgLp(a) and lgAHI on lgHOMA-IR (P=0.013). Furthermore, the interaction on a multiplicative scale also demonstrated a significant positive interaction (P=0.044). A stronger association between AHI quartiles and the presence of insulin resistance (defined as HOMA-IR > 3) could be observed for participants within lower Lp(a) quartiles. In conclusion, a significant positive interaction was observed between OSA and decreased Lp(a) with respect to insulin resistance. This association might be relevant to the assessment of metabolic or cardiovascular disease risk in OSA patients

Global burden of head and neck cancers from 1990 to 2019

Summary: Head and neck cancer (HNC) exerts a significant healthcare burden worldwide. Insufficient data impedes a comprehensive understanding of its global impact. Through analysis of the 2019 Global Burden of Disease (GBD) database, our secondary investigation unveiled a surging global incidence of HNC, yet a decline in associated mortality and disability-adjusted life years (DALYs) owing to enhanced prognosis. Particularly noteworthy is the higher incidence of escalation among females compared to males. Effective resource allocation, meticulous control of risk factors, and tailored interventions are imperative to curtail mortality rates among young individuals afflicted with HNC in underprivileged regions, as well as in elderly individuals grappling with thyroid cancer

Development and validation of a simple-to-use clinical nomogram for predicting obstructive sleep apnea

Abstract Background The high cost and low availability of polysomnography (PSG) limits the timely diagnosis of OSA. Herein, we developed and validated a simple-to-use nomogram for predicting OSA. Methods We collected and analyzed the cross-sectional data of 4162 participants with suspected OSA, seen at our sleep center between 2007 and 2016. Demographic, biochemical and anthropometric data, as well as sleep parameters were obtained. A least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensionality, select factors, and construct the nomogram. The performance of the nomogram was assessed using calibration and discrimination. Internal validation was also performed. Results The LASSO regression analysis identified age, sex, body mass index, neck circumference, waist circumference, glucose, insulin, and apolipoprotein B as significant predictive factors of OSA. Our nomogram model showed good discrimination and calibration in terms of predicting OSA, and had a C-index value of 0.839 according to the internal validation. Discrimination and calibration in the validation group was also good (C-index = 0.820). The nomogram identified individuals at risk for OSA with an area under the curve (AUC) of 0.84 [95% confidence interval (CI), 0.83–0.86]. Conclusions Our simple-to-use nomogram is not intended to replace standard PSG, but will help physicians better make decisions on PSG arrangement for the patients referred to sleep center

The Relationship between Simple Snoring and Metabolic Syndrome: A Cross-Sectional Study

Purpose. This cross-sectional study was performed to assess the relationship between simple snoring and metabolic syndrome (MetS). Methods. A total of 5635 participants including 300 healthy volunteers without snoring allegedly were initially included from 2007 to 2016. Polysomnographic variables, anthropometric measurements, and biochemical indicators were collected. The polynomial linear trend test was used to assess the linear trend across snoring intensity for metabolic score, and logistic regression was used to evaluate the odds ratios (ORs) for MetS after controlling for age, sex, obesity, smoking status, and alcohol consumption. Results. The final study population consisted of 866 participants. Simple snorers showed more severe metabolic disorders and higher prevalence of MetS than nonsnorers. A significant linear trend was observed between snoring intensity and metabolic score. Simple snoring was significantly associated with increased odds for MetS among all participants (OR=2.328, 95% CI: 1.340–4.045) and female participants (OR=2.382, 95% CI: 1.136–4.994) after multivariable adjustment. With regard to MetS components, simple snoring was significantly associated with increased odds for hypertension (OR=1.730, 95% CI: 1.130–2.650), abdominal obesity (OR=1.810, 95% CI: 1.063–3.083), and hyper-triglycerides (TG) (OR=1.814, 95% CI: 1.097–2.998) among all participants, with hypertension (OR=3.493, 95% CI: 1.748–6.979) among males and with abdominal obesity (OR=2.306, 95% CI: 1.245–4.270) and hyper-TG (OR=2.803, 95% CI: 1.146–6.856) among females after multivariable adjustment. Conclusions. After excluding the influence of repeated apnea and hypoxia, simple snoring was still significantly associated with MetS, especially in women. Furthermore, the associations were more obvious for hypertension among males and for abdominal obesity and hyper-TG among females. In addition to OSA, simple snoring also should be valued