Differential Experiences of Intimate Partner Violence during COVID-19: A Cross-Regional Study in Mexico

The COVID-19 pandemic brought on a marked increase in intimate partner violence (IPV) worldwide, Mexico being no exception. Factors that exacerbated gender-based violence (GBV) in the household during the pandemic include gendered loss of income, regression in access to social and legal justice resources, reversal to more traditional gender norms and roles, and increased alcoholism. While there are studies about the prevalence and determinants of IPV in rural and urban Mexico, there appears to be a lack of information regarding how these realities differed as they interacted with the compounding pressures of the COVID-19 pandemic. Stemming from 10 ethnographic interviews with women across rural and urban localities of Oaxaca, Mexico City, and Mexico State, who were recruited from NGOs providing psychological and legal services against GBV, we analyze some factors associated with the prevalence of IPV during confinement. We conclude that all women in our study experienced IPV both before and during the pandemic, with variations in IPV patterns influenced by their rural or urban residence, socio-economic status, ethnic-racial identity, and proximity to the abuser's network. We also found that not all impacts were negative, rather COVID-19 measures had a paradoxical effect for some women where restrictions on geographical mobility and decrease in access to alcohol became pivotal protective factors. We recommend that public policymakers and civil society organizations alike pay attention to these differential challenges and benefits in their crisis responses.

Optimization of Interleukin-10 incorporation for dendritic cells embedded in Poly(ethylene glycol) hydrogels

Translational research in biomaterials and immunoengineering is leading to the development of novel advanced therapeutics to treat diseases such as cancer, autoimmunity, and viral infections. Dendritic cells (DCs) are at the center of these therapeutics given that they bridge innate and adaptive immunity. The biomaterial system developed herein uses a hydrogel carrier to deliver immunomodulatory DCs for amelioration of autoimmunity. This biomaterial vehicle is comprised of a poly (ethylene glycol)-4 arm maleimide (PEG-4MAL) hydrogels, conjugated with the immunosuppressive cytokine, interleukin-10, IL-10, and cross-linked with a collagenase-degradable peptide sequence for the injectable delivery of immunosuppressive DCs to an anatomical disease-relevant site of the cervical lymph nodes, for intended application to treat multiple sclerosis. The amount of IL-10 incorporated in the hydrogel was optimized to be 500 ng in vitro, based on immunological endpoints. At this concentration, DCs exhibited the best viability, most immunosuppressive phenotype, and protection against proinflammatory insult as compared with hydrogel-incorporated DCs with lower IL-10 loading amounts. Additionally, the effect of the degradability of the PEG-4MAL hydrogel on the release rate of incorporated IL-10 was assessed by varying the ratio of degradable peptides: VPM (degradable) and DTT (nondegradable) and measuring the IL-10 release rates. This IL-10-conjugated hydrogel delivery system for immunosuppressive DCs is set to be assessed for in vivo functionality as the immunosuppressive cytokine provides a tolerogenic environment that keeps DCs in their immature phenotype, which consequently enhances cell viability and optimizes the system's immunomodulatory functionality.

Copper-Cystine Biohybrid-Embedded Nanofiber Aerogels Show Antibacterial and Angiogenic Properties

Copper-cystine-based high aspect ratio structures (CuHARS) possess exceptional physical and chemical properties and exhibit remarkable biodegradability in human physiological conditions. Extensive testing has confirmed the biocompatibility and biodegradability of CuHARS under diverse biological conditions, making them a viable source of essential Cu2+. These ions are vital for catalyzing the production of nitric oxide (NO) from the decomposition of S-nitrosothiols (RSNOs) found in human blood. The ability of CuHARS to act as a Cu2+ donor under specific concentrations has been demonstrated in this study, resulting in the generation of elevated levels of NO. Consequently, this dual function makes CuHARS effective as both a bactericidal agent and a promoter of angiogenesis. In vitro experiments have shown that CuHARS actively promotes the migration and formation of complete lumens by redirecting microvascular endothelial cells. To maximize the benefits of CuHARS, they have been incorporated into biomimetic electrospun poly(ε-caprolactone)/gelatin nanofiber aerogels. Through the regulated release of Cu2+ and NO production, these channeled aerogels not only provide antibacterial support but also promote angiogenesis. Taken together, the inclusion of CuHARS in biomimetic scaffolds could hold great promise in revolutionizing tissue regeneration and wound healing.

Droplet-based logic gates simulation of viscoelastic fluids under electric field

Nano and microfluidic technologies have shown great promise in the development of controlled drug delivery systems and the creation of microfluidic devices with logic-like functionalities. Here, we focused on investigating a droplet-based logic gate that can be used for automating medical diagnostic assays. This logic gate uses viscoelastic fluids, which are particularly relevant since bio-fluids exhibit viscoelastic properties. The operation of the logic gate is determined by evaluating various parameters, including the Weissenberg number, the Capillary number, and geometric factors. To effectively classify the logic gates operational conditions, we employed a deep learning classification to develop a reduced-order model. This approach accelerates the prediction of operating conditions, eliminating the need for complex simulations. Moreover, the deep learning model allows for the combination of different AND/OR branches, further enhancing the versatility of the logic gate. We also found that non-operating regions, where the logic gate does not function properly, can be transformed into operational regions by applying an external force. By utilizing an electrical induction technique, we demonstrated that the application of an electric field can repel or attract droplets, thereby improving the performance of the logic gate. Overall, our research shows the potential of the droplet-based logic gates in the field of medical diagnostics. The integration of deep learning classification algorithms enables rapid evaluation of operational conditions and facilitates the design of complex logic circuits. Additionally, the introduction of external forces and electrical induction techniques opens up new possibilities for enhancing the functionality and reliability of these logic gates.

Short-term plant-community responses to large mammalian herbivore exclusion in a rewilded Javan savanna

Grassy biomes such as savannas are maintained by an interacting suite of ecosystem processes from herbivory to rainfall to fire. Many studies have examined the impacts of large mammalian herbivores on herbaceous plant communities, but few of these studies have been conducted in humid, fertile savannas. We present the findings of a short-term experiment that investigated the effects of herbivory in a fertile, humid, and semi-managed savanna. We erected large-herbivore exclosures in Alas Purwo National Park, Java, Indonesia where rainfall is high and fire is suppressed to test how herbivores impact plant community development across the growing season. Where large mammalian herbivores were excluded, herbaceous plant communities contained more non-grasses and were less similar; diverging in their composition as the growing season progressed. Effects of herbivore exclusion on plant species richness, evenness, and biomass per quadrat were generally weak. Notably, however, two weedy plant species (one native, Imperata cylindrica and one introduced, Senna cf. tora) appeared to benefit most from herbivore release. Our results suggest that heavy grazing pressure by native large mammalian herbivores controlled the composition of the herbaceous plant community. Moreover, exclusion of large mammalian herbivores led to divergence in the plant species composition of exclosures; compositional dissimilarity between herbivore-exclusion plots was higher than between plots exposed to large mammalian herbivores. Our findings suggest that, at this high-rainfall site, large mammalian herbivores constrained the developmental trajectory of plant communities across the growing season.

A machine learning approach to predict pancreatic islet grafts rejection versus tolerance

The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance.

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO's strengths are 1) fast and accurate tracking, 2) high throughput, 3) an accessible interface and data output and 4) real-time closed-loop hardware control for for sensory and optogenetic stimuli, all of which are optimized for large-scale experiments.