Deep Reinforcement Learning-Based Approach for Efficient and Reliable Droplet Routing on MEDA Biochips

The micro-electrode-dot-array (MEDA) architecture provides precise droplet control and real-time sensing in digital microfluidic biochips. Previous work has shown that trapped charge under microelectrodes (MCs) leads to droplets being stuck and failures in fluidic operations. A recent approach utilizes real-time sensing of MC health status, and attempts to avoid degraded electrodes during droplet routing. However, the problem with this solution is that the computational complexity is unacceptable for MEDA biochips of realistic size. Consequently, in this work, we introduce a deep reinforcement learning (DRL)-based approach to bypass degraded electrodes and enhance the reliability of routing. The DRL model utilizes the information of health sensing in real time to proactively reduce the likelihood of charge trapping and avoid using degraded MCs. Simulation results show that our approach provides effective routing strategies for COVID-19 testing protocols. We also validate our DRL-based approach using fabricated prototype biochips. Experimental results show that the developed DRL model completed the routing tasks using a fewer number of clock cycles and shorter total execution time, compared with a baseline routing method. Moreover, our DRL-based approach provides reliable routing strategies even in the presence of degraded electrodes. Our experimental results show that the proposed DRL-based routing is robust to occurrences of electrode faults, as well as increases the lifetime and usability of microfluidic biochips compared to existing strategies.

A global mental health opportunity: How can cultural concepts of distress broaden the construct of immobility?

(Im)mobility studies often focus on people on the move, neglecting those who stay, are immobile, or are trapped. The duality of the COVID-19 pandemic and the climate crisis creates a global mental health challenge, impacting the most structurally oppressed, including immobile populations. The construct of immobility is investigated in the context of socio-political variables but lacks examination of the clinical psychological factors that impact immobility. Research is beginning to identify self-reported emotions that immobile populations experience through describing metaphors like feeling trapped. This article identifies links in the literature between Cultural Concepts of Distress drawn from transcultural psychiatry and immobility studies. Feeling trapped is described in mental health research widely. Among (im)mobile people and non-mobility contexts, populations experience various mental health conditions from depression to the cultural syndrome, nervios. The connection of feeling trapped to CCD research lends itself to potential utility in immobility research. The conceptualisation can support broadening and deepening the comprehension of this global mental health challenge - how immobile populations' experience feeling trapped. To broaden the analytical framework of immobility and incorporate CCD, evidence is needed to fill the gaps on the psychological aspects of immobility research.

Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy).

COVID-19 infection evokes various systemic alterations that push patients not only towards severe acute respiratory syndrome but causes an important metabolic dysregulation with following multi-organ alteration and potentially poor outcome. To discover novel potential biomarkers able to predict disease's severity and patient's outcome, in this study we applied untargeted lipidomics, by a reversed phase ultra-high performance liquid chromatography-trapped ion mobility mass spectrometry platform (RP-UHPLC-TIMS-MS), on blood samples collected at hospital admission in an Italian cohort of COVID-19 patients (45 mild, 54 severe, 21 controls). In a subset of patients, we also collected a second blood sample in correspondence of clinical phenotype modification (longitudinal population). Plasma lipid profiles revealed several lipids significantly modified in COVID-19 patients with respect to controls and able to discern between mild and severe clinical phenotype. Severe patients were characterized by a progressive decrease in the levels of LPCs, LPC-Os, PC-Os, and, on the contrary, an increase in overall TGs, PEs, and Ceramides. A machine learning model was built by using both the entire dataset and with a restricted lipid panel dataset, delivering comparable results in predicting severity (AUC= 0.777, CI: 0.639-0.904) and outcome (AUC= 0.789, CI: 0.658-0.910). Finally, re-building the model with 25 longitudinal (t1) samples, this resulted in 21 patients correctly classified. In conclusion, this study highlights specific lipid profiles that could be used monitor the possible trajectory of COVID-19 patients at hospital admission, which could be used in targeted approaches.

Feelings of Entrapment during the COVID-19 Pandemic Based on ACE Star Model: A Concept Analysis.

This study aimed to analyze the concept of the "feelings of entrapment" during the COVID-19 (coronavirus disease 2019) pandemic using a systematic review. We included literature based on content and outcomes related to feelings of entrapment, such as antecedents, attributes, and consequences. The exclusion criteria were studies that did not have inappropriate subject, content, conceptual definition, and degree thesis was excluded. Walker and Avant's process of concept analysis was used in this systematic literature review. The attributes of the concept of feelings of entrapment during the COVID-19 pandemic were found to be feelings of: (1) being out of control, (2) no escape, (3) being trapped, (4) being robbed, and (5) hopelessness. The causes for these were identified as (1) the COVID-19 pandemic, (2) lockdown system, (3) restricted situation, (4) uncertain future, (5) economic hardship, and (6) poor coping abilities. Consequences of the concept were: (1) increased suicide, (2) decreased mental health, and (3) decreased well-being. In situations such as COVID-19, it is important need to know what feelings of entrapment's antecedents and attributes are to prevent suicide and enhance mental health and well-being. Based on the results of this study, counseling services, policies, and systems for relieving feelings of entrapment in the COVID-19 situation are recommended.