Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis. CCS CONCEPTS • Information systems → Data mining; • Human-centered computing → User studies; Ubiquitous and mobile computing; • Computing methodologies → Machine learning. * Ordered alphabetically, equal contribution.

We examined the microRNA (miRNA) expression profile of 40 prostatectomy specimens from stage T2a/b, early relapse and nonrelapse cancer patients, to better understand the relationship between miRNA dysregulation and prostate oncogenesis. Paired analysis was carried out with microdissected, malignant and non-involved areas of each specimen, using high-throughput liquidphase hybridization (mirMASA) reactions and 114 miRNA probes. Five miRNAs (miR-23b, -100, -145, -221 and -222) were significantly downregulated in malignant tissues, according to significance analysis of microarrays and paired t-test with Bonferroni correction. Lowered expression of miR-23b, -145, -221 and -222 in malignant tissues was validated by quantitative reverse transcription (qRT)-PCR analyses. Ectopic expression of these miRNAs significantly reduced LNCaP cancer cell growth, suggesting growth modulatory roles for these miRNAs. Patient subset analysis showed that those with post-surgery elevation of prostatespecific antigen (chemical relapse) displayed a distinct expression profile of 16 miRNAs, as compared with patients with nonrelapse disease. A trend of increased expression (440%) of miR-135b and miR-194 was observed by qRT-PCR confirmatory analysis of 11 patients from each clinical subset. These findings indicate that an altered miRNA expression signature accompanied the prostate oncogenic process. Additional, aberrant miRNA expression features may reflect a tendency for early disease relapse. Growth inhibition through the reconstitution of miRNAs is potentially applicable for experimental therapy of prostate cancer, pending molecular validation of targeted genes.

Over 90% of chondroblastomas contain a heterozygous mutation replacing lysine 36 with methionine (K36M) in the histone H3 variant H3.3. Here, we show that H3K36 methylation is reduced globally in chondroblastomas and in chondrocytes harboring the same genetic mutation due to inhibition of at least two H3K36 methyltransferases, MMSET and SETD2, by the H3.3K36M mutant proteins. Genes with altered expression as well as H3K36 di- and trimethylation in H3.3K36M cells are enriched in cancer pathways. In addition, H3.3K36M chondrocytes exhibit several hallmarks of cancer cells including increased ability to form colonies, resistance to apoptosis and defects in differentiation. Thus, H3.3K36M proteins reprogram H3K36 methylation landscape and contribute to tumorigenesis in part through altering the expression of cancer-associated genes.

Twist is a critical epithelial-mesenchymal transition (EMT)-inducing transcription factor that increases expression of vimentin. How Twist1 regulates this expression remains unclear. Here, we report that Twist1 regulates Cullin2 (Cul2) circular RNA to increase expression of vimentin in EMT. Twist1 bound the Cul2 promoter to activate its transcription and to selectively promote expression of Cul2 circular RNA (circ-10720), but not mRNA. circ-10720 positively correlated with Twist1, tumor malignance, and poor prognosis in hepatocellular carcinoma (HCC). Twist1 promoted vimentin expression by increasing levels of circ-10720, which can absorb miRNAs that target vimentin. circ-10720 knockdown counteracted the tumor-promoting activity of Twist1 and in patient-derived xenograft and diethylnitrosamine-induced TetOn-Twist1 transgenic mouse HCC models. These data unveil a mechanism by which Twist1 regulates vimentin during EMT. They also provide potential therapeutic targets for HCC treatment and provide new insight for circular RNA (circRNA)-based diagnostic and therapeutic strategies. A circRNA-based mechanism drives Twist1-mediated regulation of vimentin during EMT and provides potential therapeutic targets for treatment of HCC. http://cancerres.aacrjournals.org/content/canres/78/15/4150/F1.large.jpg .

Background & Aims New-onset diabetes in patients with pancreatic cancer is likely to be a paraneoplastic phenomenon caused by tumor-secreted products. We aimed to identify the diabetogenic secretory product(s) of pancreatic cancer Methods Using microarray analysis, we identified adrenomedullin as a potential mediator of diabetes in patients with pancreatic cancer. Adrenomedullin was up-regulated in pancreatic cancer cell lines, in which supernatants reduced insulin signaling in beta cell lines. We performed quantitative reverse-transcriptase polymerase chain reaction and immunohistochemistry on human pancreatic cancer and healthy pancreatic tissues (controls) to determine expression of adrenomedullin messenger RNA and protein, respectively. We studied the effects of adrenomedullin on insulin secretion by beta cell lines and whole islets from mice and on glucose tolerance in pancreatic xenografts in mice. We measured plasma levels of adrenomedullin in patients with pancreatic cancer, patients with type 2 diabetes mellitus, and individuals with normal fasting glucose levels (controls) Results Levels of adrenomedullin messenger RNA and protein were increased in human pancreatic cancer samples compared with controls. Adrenomedullin and conditioned media from pancreatic cell lines inhibited glucose-stimulated insulin secretion from beta cell lines and islets isolated from mice; the effects of conditioned media from pancreatic cancer cells were reduced by small hairpin RNA-mediated knockdown of adrenomedullin. Conversely, overexpression of adrenomedullin in mice with pancreatic cancer led to glucose intolerance. Mean plasma levels of adrenomedullin (femtomoles per liter) were higher in patients with pancreatic cancer compared with patients with diabetes or controls. Levels of adrenomedullin were higher in patients with pancreatic cancer who developed diabetes compared those who did not. Conclusions Adrenomedullin is up-regulated in patients with pancreatic cancer and causes insulin resistance in β cells and mice.