Dexamethasone can protect hair-cell-like HEI-OC1 cells from ERS damage, which may be one of the mechanisms of action for GCs in SNHL treatment.Marchalina hellenica is a sap sucking scale insect endemic to the Aegean basin and it has been introduced to several regions in Greece and Turkey to increase pine honey production. It is also considered as a pest since heavy infestation may leave the host trees vulnerable to secondary pests. An understanding of its natural predators would facilitate planning biocontrol programs. Although there are several studies reporting the predators of M. hellenica in its native range, there is no study identifying those in its introduced range. We aimed to determine predators of M. hellenica in Burdur, one of its introduced sites in Turkey. We carried out sampling through regular visits in an M. hellenica-infested locality nearby Burdur Lake. Through field and laboratory observations, we identified 19 species predating upon M. hellenica. Comparing predators reported in previous studies in its native range and those we found in the present study showed that 12 of the species that we found are new reports for the species predating upon M. hellenica. The highest number of predator individuals belonged to the monophagous Neoleucopis kartliana. Myrrha octodecimguttata, Chilocorus bipustulatus and Harmonia quadripunctata were also the most frequently observed predators.Long noncoding RNA (lncRNA) highly upregulated in liver cancer (HULC) has been reported to be implicated in chemoresistance. However, the potential mechanism of HULC in paclitaxel (PTX)-resistant ovarian cancer (OC) remains undefined.  https://www.selleckchem.com/products/avacopan-ccx168-.html  The expression of RNAs and proteins was measured by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) and Western blot assay. The PTX resistance and apoptotic rate were assessed via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and flow cytometry, respectively. Furthermore, the interaction between miR-137 and HULC or integrin beta-8 (ITGB8) was predicted by miRcode and starBase v2.0 and then verified by dual luciferase reporter and RNA pull-down assays. In addition, the xenograft mice model was established to explore the effects of HULC in vivo. HULC was significantly upregulated and miR-137 was downregulated in PTX-resistant OC tissues and cells. Also, the HULC depletion suppressed tumor growth and PTX resistance in PTX-treated mice. miR-137 was verified as a target of HULC and directly targeted ITGB8. And HULC knockdown downregulated ITGB8 expression by targeting miR-137. miR-137 inhibitor or ITGB8 overexpression mitigated the suppressive impacts of HULC knockdown on PTX resistance. Collectively, HULC modulated ITGB8 expression to promote PTX resistance of OC by sponging miR-137.
  Several case series of ACS have been reported in COVID 19 patients. We aim to study its incidence, characteristics, and three-month prognosis. To put this incidence in perspective we compared it with the incidence of in-hospital ACS during the same period of 2019.
 
  Observational multicenter cohort study of 3.108 COVID-19 patients admitted to two hospitals in Madrid between March 1st and May 15th, 2020. Ten patients suffered an ACS while being hospitalized for COVID 19 and were followed for three months. The ACS incidence in hospitalized patients during the same period of 2019 was also studied.
 
  The incidence of ACS in COVID-19 patients was 3.31‰, significantly higher than in the 2019 period, 1.01‰ (p=0.013). COVID-19 patients that suffered and ACS frequently had a severe infection, presented with STEMI (80%), and had multivessel disease (67%). Mortality rate (30%) and hospital readmissions at three months (20%) were very high.
 
  Severe COVID-19 patients develop ACS more frequently than expected. Although the overall incidence was low, it carried a poor immediate and three-month prognosis.
 Severe COVID-19 patients develop ACS more frequently than expected. Although the overall incidence was low, it carried a poor immediate and three-month prognosis.
  In the context of the global COVID-19 pandemic, the different clinical manifestations of this infection pose a challenge for healthcare professionals. Respiratory involvement, the main symptom of SARS-CoV-2 infection, means that other manifestations, such as neurological, take a back seat, with the consequent delay in diagnosis and treatment.
 
  All COVID-19 patients admitted with neurological symptoms or diagnosed with encephalitis since March 2020 in a tertiary hospital in Zaragoza, Spain.
 
  Two patients with COVID-19 infection confirmed by nasopharyngeal PCR and whose clinical picture consisted of neurological alterations compatible with encephalitis. Cerebrospinal fluid (CSF) microbiology was negative for bacteria and viruses, including SARS-CoV-2 but, given the clinical suspicion of encephalitis due to the latter, antiviral treatment with immunoglobulins and plasmapheresis was started early. Despite this, the evolution was not satisfactory.
 
  COVID-19 encephalitis is a recently described clinical entity, whose pathophysiology is still unknown and no treatment with clinical evidence is available to date.
 COVID-19 encephalitis is a recently described clinical entity, whose pathophysiology is still unknown and no treatment with clinical evidence is available to date.
  The effects of Artificial Intelligence (AI) technology applications are already felt in healthcare in general and in the practice of medicine in the disciplines of radiology, pathology, ophthalmology, and oncology. The expanding interface between digital data science, emerging AI technologies and healthcare is creating a demand for AI technology literacy in health professions.
 
  To assess medical student and faculty attitudes toward AI, in preparation for teaching AI foundations and data science applications in clinical practice in an integrated medical education curriculum.
 
  An online 15-question semi-structured survey was distributed among medical students and faculty. The questionnaire consisted of 3 parts participant's background, AI awareness, and attitudes toward AI applications in medicine.
 
  A total of 121 medical students and 52 clinical faculty completed the survey. Only 30% of students and 50% of faculty responded that they were aware of AI topics in medicine. The majority of students (72%) and faculty (59%) learned about AI from the media.