Prevalence of obstructive sleep apnea in patients with squamous cell carcinoma of the tongue following ablation surgery

AbstractBackground/purposeThe purpose of this study was to determine the prevalence of obstructive sleep apnea (OSA) in patients with squamous cell carcinoma (SSC) of the tongue after primary surgical resection and to correlate the presence of OSA with the occurrence of obstructive apnea in this patient population.Materials and methodsThis was a retrospective study of 26 Taiwanese patients, 24 males and two females, aged 37–71 years, after surgical resection of SSC of the tongue. Patients who had a follow-up after treatment of 6 months to 11 years were eligible for inclusion. During the post-treatment period, the occurrence of OSA was determined in these patients. Overnight polysomnography (PSG) was used to determine the apnea–hypopnea index (AHI). Patients were considered to have OSA if the AHI value was >5.ResultsPatients with an AHI value of <5 showed a mean body mass index (BMI) of 22.8 kg/m2, while those with an AHI value of >5 showed a mean BMI of 28.3 kg/m2. The BMI distribution between patients with AHI value of <5 and those with AHI values of >5 was statistically significant (P = 0.018). Using the definition of clinically significant sleep apnea as AHI > 5, 14 of 26 patients (53.85%) had clinical OSA. The OSA and non-OSA groups showed no statistical significance in terms of age, tumor size, tongue ablation, neck dissection [method?], or wound reconstruction methods.ConclusionIncidence of OSA in the patient population with SSC of the tongue was found to be significantly higher than that in the general population. The limitations of this study were the relatively small patient sample size and no presurgical PSG record being obtained from the patients to compare the sleep quality before and after cancer therapy

Development of a New Tacaribe Arenavirus Infection Model and Its Use to Explore Antiviral Activity of a Novel Aristeromycin Analog

Background A growing number of arenaviruses can cause a devastating viral hemorrhagic fever (VHF) syndrome. They pose a public health threat as emerging viruses and because of their potential use as bioterror agents. All of the highly pathogenic New World arenaviruses (NWA) phylogenetically segregate into clade B and require maximum biosafety containment facilities for their study. Tacaribe virus (TCRV) is a nonpathogenic member of clade B that is closely related to the VHF arenaviruses at the amino acid level. Despite this relatedness, TCRV lacks the ability to antagonize the host interferon (IFN) response, which likely contributes to its inability to cause disease in animals other than newborn mice. Methodology/Principal Findings Here we describe a new mouse model based on TCRV challenge of AG129 IFN-α/β and -γ receptor-deficient mice. Titration of the virus by intraperitoneal (i.p.) challenge of AG129 mice resulted in an LD50 of ∼100 fifty percent cell culture infectious doses. Virus replication was evident in the serum, liver, lung, spleen, and brain 4–8 days after inoculation. MY-24, an aristeromycin derivative active against TCRV in cell culture at 0.9 µM, administered i.p. once daily for 7 days, offered highly significant (P\u3c0.001) protection against mortality in the AG129 mouse TCRV infection model, without appreciably reducing viral burden. In contrast, in a hamster model of arenaviral hemorrhagic fever based on challenge with clade A Pichinde arenavirus, MY-24 did not offer significant protection against mortality. Conclusions/Significance MY-24 is believed to act as an inhibitor of S-adenosyl-L-homocysteine hydrolase, but our findings suggest that it may ameliorate disease by blunting the effects of the host response that play a role in disease pathogenesis. The new AG129 mouse TCRV infection model provides a safe and cost-effective means to conduct early-stage pre-clinical evaluations of candidate antiviral therapies that target clade B arenaviruses

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery