Motivation and perception of Hong Kong university students about social media news

With the prevalence of social media in a digital age, accessing news on social media has become a daily routine of university students’ lives. However, little research has been done to examine their social media news use in detail, especially in Asian countries. To fill this gap, we aimed to examine what motivated university students to seek news on social media, to what extent they perceived they were in control of the influences of news, and whether news motives were related to their levels of news media literacy across three domains: (a) authors and audiences; (b) messages and meaning, and (c) representation and reality. One hundred and forty-seven university students from a university in Hong Kong participated. Among the four news motives, socializing was the most powerful predictor for news use on social media. Most students believed they were in control of news influences and demonstrated a high level of news media literacy, and those who believed themselves to be in control of news influences showed a higher level of news media literacy. In this sense, high news-literate students were more likely to seek news for socializing as compared to their low news-literate counterparts. Insights on educating students to use social media in a positive and smart way were discussed

Prognostic impact of TP53 mutations and tumor mutational load in colorectal cancer

The DNA damage response (DDR) is critical for maintaining genome stability, and abnormal DDR—resulting from mutations in DNA damage-sensing and repair proteins—is a hallmark of cancer. Here, we aimed to investigate the predictive power of DDR gene mutations and the tumor mutational load (TML) for survival outcomes in a cohort of 22 rectal cancer patients who received pre-operative neoadjuvant therapy. Univariate analysis revealed that TML-high and TP53 mutations were significantly associated with worse overall survival (OS) with TML-high retaining significance in multivariate analyses. Kaplan–Meier survival analyses further showed TML-high was associated with worse disease-free (p = 0.036) and OS (p = 0.024) results in our patient cohort. A total of 53 somatic mutations were identified in 22 samples with eight (36%) containing mutations in DDR genes, including ATM, ATR, CHEK2, MRE11A, RAD50, NBN, ERCC2 and TP53. TP53 was the most frequently mutated gene, and TP53 mutations were significantly associated with worse OS (p = 0.023) in Kaplan–Meier survival analyses. Thus, our data indicate that TML and TP53 mutations have prognostic value for rectal cancer patients and may be important independent biomarkers for patient management. This suggests that prognostic determination for rectal cancer patients receiving pre-operative neoadjuvant therapy should include consideration of the initial TML and tumor genetic status

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn

Prediction of lithium response using genomic data

Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen's kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [- 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures