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Multiwalled carbon nanotubes (purified, p-MWNT and $ NH 2 functionalized, f-MWNT) were melt-mixed with 50/50 cocontinuous blends of polyamide 6 (PA6) and acrylonitrile-butadiene-styrene in a conical twin-screw microcompounder to obtain conductive polymer blends utilizing the conceptual approach of double-percolation. The state of dispersion of the tubes was assessed using AC electrical conductivity measurements and melt-rheology. The rheological and the electrical percolation threshold was observed to be $ 1-2 wt % and $ 3-4 wt %, respectively, for blends with p-MWNT. In case of blends with f-MWNT, the rheological percolation threshold was observed to be higher (2-3 wt %) than p-MWNT but the electrical percolation threshold remained almost same. However, the absolute values were significantly lower than blends with p-MWNT. In addition, significant refinement in the cocontinuous morphology of the blends with increasing concentration of MWNT was observed in both the cases. Further, an attempt was made to understand the underlying concepts in relation to cocontinuous morphologies that how the geometrical percolation threshold which adversely suffered because of the attrition of tubes under prolonged shear contributed further in retaining the rheological percolation threshold.

Background Despite significant advancements in healthcare technology, digital health solutions – especially those for serious mental illnesses – continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India. Methods Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality. Results Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement. Conclusion People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary – not substitutive – of therapeutic care from a clinician.

Background Childhood malnutrition has been a longstanding crisis in Mumbai, India. Despite national IYCF (Infant Young Child Feeding) guidelines to promote best practices for infant/toddler feeding, nearly one-third of children under age five are stunted or underweight. To improve child nutrition, interventions should address the cultural, social, and environmental influences on infant feeding practices. This study is an in-depth qualitative assessment of family barriers and facilitators to implementing recommended nutrition practices in two Mumbai slum communities, within the context of an existing nutrition education-based intervention by a local non-governmental non-profit organization. Methods The population was purposively sampled to represent a variety of household demographics. Data were collected through 33 in-depth semi-structured interviews with caregivers (mothers and paternal grandmothers) of children age 0–2 years. Transcripts were translated and transcribed, and analyzed using qualitative analysis procedures and software. Results A complex set of barriers and facilitators influence mothers’/caregivers’ infant-toddler feeding practices. Most infants were fed complementary foods and non-nutritious processed snacks, counter to IYCF recommendations. Key barriers included: lack of nutrition knowledge and experience, receiving conflicting messages from different sources, limited social support, and poor self-efficacy for maternal decision-making. Key facilitators included: professional nutrition guidance, personal self-efficacy and empowerment, and family support. Interventions to improve child nutrition should address mothers’/caregivers’ key barriers and facilitators to recommended infant-toddler feeding practices. Conclusions Nutrition interventions should prioritize standard messaging across healthcare providers, engage all family members, target prevention of early introduction of sugary and non-nutritious processed foods, and strengthen maternal self-efficacy for following IYCF recommended guidelines.

Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.