Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions.

BACKGROUND: Conversational agents cannot yet express empathy in nuanced ways that account for the unique circumstances of the user. Agents that possess this faculty could be used to enhance digital mental health interventions. OBJECTIVE: We sought to design a conversational agent that could express empathic support in ways that might approach, or even match, human capabilities. Another aim was to assess how users might appraise such a system. METHODS: Our system used a corpus-based approach to simulate expressed empathy. Responses from an existing pool of online peer support data were repurposed by the agent and presented to the user. Information retrieval techniques and word embeddings were used to select historical responses that best matched a user's concerns. We collected ratings from 37,169 users to evaluate the system. Additionally, we conducted a controlled experiment (N=1284) to test whether the alleged source of a response (human or machine) might change user perceptions. RESULTS: The majority of responses created by the agent (2986/3770, 79.20%) were deemed acceptable by users. However, users significantly preferred the efforts of their peers (P<.001). This effect was maintained in a controlled study (P=.02), even when the only difference in responses was whether they were framed as coming from a human or a machine. CONCLUSIONS: Our system illustrates a novel way for machines to construct nuanced and personalized empathic utterances. However, the design had significant limitations and further research is needed to make this approach viable. Our controlled study suggests that even in ideal conditions, nonhuman agents may struggle to express empathy as well as humans. The ethical implications of empathic agents, as well as their potential iatrogenic effects, are also discussed.

Restoration of miR-30a expression inhibits growth, tumorigenicity of medulloblastoma cells accompanied by autophagy inhibition.

Medulloblastoma is a highly malignant pediatric brain tumor. About 30% patients have metastasis at diagnosis and respond poorly to treatment. Those that survive, suffer long term neurocognitive, endocrine and developmental defects due to the cytotoxic treatment to developing child brain. It is therefore necessary to develop targeted treatment strategies based on underlying biology for effective treatment of medulloblastoma with minimal side effects. Medulloblastomas are believed to be the result of deregulated nervous system development as evident from the role of WNT and SHH developmental signaling pathways in pathogenesis of medulloblastomas. MicroRNAs are known to play vital roles in nervous system development as well as in cancer. MicroRNA profiling of medulloblastomas identified miR-30 family members' expression to be downregulated in medulloblastomas belonging to the four known molecular subgroups viz. WNT, SHH, Group 3 and Group 4 as compared to that in normal brain tissues. Furthermore, established medulloblastoma cell lines Daoy, D283 and D425 were also found to underexpress miR-30a. Restoration of miR-30a expression using inducible lentiviral vector inhibited proliferation, clonogenic potential and tumorigenicity of medulloblastoma cells. MiR-30a is known to target Beclin1, a mediator of autophagy. MiR-30a expression was found to downregulate Beclin1 expression and inhibit autophagy in the medulloblastoma cell lines as judged by downregulation of LC3B expression and its turnover upon chloroquine treatment and starvation induced autophagy induction. MiR-30a therefore could serve as a novel therapeutic agent for the effective treatment of medulloblastoma by inhibiting autophagy that is known to play important role in cancer cell growth, survival and malignant behavior.