Laser-Induced Intracellular Delivery: Exploiting Gold-Coated Spiky Polymeric Nanoparticles and Gold Nanorods under Near-Infrared Pulses for Single-Cell Nano-Photon-Poration.

This study explores the potential of laser-induced nano-photon-poration as a non-invasive technique for the intracellular delivery of micro/macromolecules at the single-cell level. This research proposes the utilization of gold-coated spiky polymeric nanoparticles (Au-PNPs) and gold nanorods (GNRs) to achieve efficient intracellular micro/macromolecule delivery at the single-cell level. By shifting the operating wavelength towards the near-infrared (NIR) range, the intracellular delivery efficiency and viability of Au-PNP-mediated photon-poration are compared to those using GNR-mediated intracellular delivery. Employing Au-PNPs as mediators in conjunction with nanosecond-pulsed lasers, a highly efficient intracellular delivery, while preserving high cell viability, is demonstrated. Laser pulses directed at Au-PNPs generate over a hundred hot spots per particle through plasmon resonance, facilitating the formation of photothermal vapor nanobubbles (PVNBs). These PVNBs create transient pores, enabling the gentle transfer of cargo from the extracellular to the intracellular milieu, without inducing deleterious effects in the cells. The optimization of wavelengths in the NIR region, coupled with low laser fluence (27 mJ/cm2) and nanoparticle concentrations (34 µg/mL), achieves outstanding delivery efficiencies (96%) and maintains high cell viability (up to 99%) across the various cell types, including cancer and neuronal cells. Importantly, sustained high cell viability (90-95%) is observed even 48 h post laser exposure. This innovative development holds considerable promise for diverse applications, encompassing drug delivery, gene therapy, and regenerative medicine. This study underscores the efficiency and versatility of the proposed technique, positioning it as a valuable tool for advancing intracellular delivery strategies in biomedical applications.

Protocol for generating embedding-free brain organoids enriched with oligodendrocytes.

In response to the scarcity of advanced in vitro models dedicated to human CNS white matter research, we present a protocol to generate neuroectoderm-derived embedding-free human brain organoids enriched with oligodendrocytes. We describe steps for neuroectoderm differentiation, development of neural spheroids, and their transferal to Matrigel. We then detail procedures for the development, maturation, and application of oligodendrocyte-enriched brain organoids. The presence of myelin-producing cells makes these organoids useful for studying human white matter diseases, such as leukodystrophy.

Magnitude and Impact of Workplace Violence Against Obstetric Healthcare Personnel: A Multicentre Cross-Sectional Study.

Background: Workplace violence is defined by the World Health Organization (WHO) as incidents where staff is abused, threatened or assaulted in work settings. In emergency predominated branch like obstetrics, there is a need to study the magnitude and impact of violence against healthcare workers (HCW). Materials and Methodology: This cross-sectional study was conducted in the Department of Obstetrics at 2 centres in Lucknow district, for a period of 6 months. The study population included trainee residents, senior residents, nursing staff and consultants. Standard definitions from the WHO were used to define the types of violence. The validated questionnaire was designed in English with 25 questions to understand the incidence of workplace violence, prevention policy, reporting and follow-ups of incidents and impact of violence. Results: With a response rate of 90%, 274 HCW participated in the study. In total, 172 HCW (62.7%) either faced physical or verbal assault. In 70% of incidents, patient, their relatives or public were perpetrators of violence, and the rest 30% incidents were by colleagues or management. Majority of the incidents were in emergency areas. Only 22% of the abused reported to the concerned authorities. At least 123 (71.5%) HCW were extremely dissatisfied with the action taken. Action was taken against only 9.8% of the perpetrators. None of the respondents received any training to handle workplace violence. Conclusion: There is an alarming high prevalence of workplace violence by patients and colleagues. Adequate training to handle these incidents, improvement of working environment and unconditional support from management will bring a positive work experience. Supplementary Information: The online version contains supplementary material available at 10.1007/s13224-023-01809-0.

A semi-supervised approach to unobtrusively predict abnormality in breathing patterns using hydraulic bed sensor data in older adults aging in place.

Shortness of breath is often considered a repercussion of aging in older adults, as respiratory illnesses like COPD1 or respiratory illnesses due to heart-related issues are often misdiagnosed, under-diagnosed or ignored at early stages. Continuous health monitoring using ambient sensors has the potential to ameliorate this problem for older adults at aging-in-place facilities. In this paper, we leverage continuous respiratory health data collected by using ambient hydraulic bed sensors installed in the apartments of older adults in aging-in-place Americare facilities to find data-adaptive indicators related to shortness of breath. We used unlabeled data collected unobtrusively over the span of three years from a COPD-diagnosed individual and used data mining to label the data. These labeled data are then used to train a predictive model to make future predictions in older adults related to shortness of breath abnormality. To pick the continuous changes in respiratory health we make predictions for shorter time windows (60-s). Hence, to summarize each day's predictions we propose an abnormal breathing index (ABI) in this paper. To showcase the trajectory of the shortness of breath abnormality over time (in terms of days), we also propose trend analysis on the ABI quarterly and incrementally. We have evaluated six individual cases retrospectively to highlight the potential and use cases of our approach.

Holistic approach to waste mobil oil bioremediation: Valorizing waste through biosurfactant production for soil restoration.

The combustion of mobil oil leads to the emission of toxic compounds in the environment. In this study, the aromatic and aliphatic hydrocarbon fractions present in a waste mobil oil collected from automobile market were comprehensively identified and their toxicity was evaluated using wheat grain. Lysinibacillus sphaericus strain IITR51 isolated and characterized previously could degrade 30-80% of both aliphatic and aromatic hydrocarbons in liquid culture. Interestingly, the strain IITR51 produced 627 mg/L of rhamnolipid biosurfactant by utilizing 3% (v/v) of waste mobil oil in the presence of 1.5% glycerol as additional carbon source. In a soil microcosm study by employing strain IITR51, 50-86% of 3-6 ring aromatic hydrocarbons and 63-98% of aliphatic hydrocarbons (C8 to C22) were degraded. Addition of 60 µg/mL rhamnolipid biosurfactant enhanced the degradation of both aliphatic and aromatic hydrocarbons from 76.88% to 61.21%-94.11% and 78.27% respectively. The degradation of mobil oil components improved the soil physico-chemical properties and increased soil fertility to 64% as evident by the phytotoxicity assessments. The findings indicate that strain IITR51 with degradation capability coupled with biosurfactant production could be a candidate for restoring hydrocarbon contaminated soils.

The role of accurate identification of vulnerable youth in vocational education and training systems for improved employability: Insights from experimental data.

The role of vocational education and training system (VET) in addressing the economic and social needs of vulnerable youth from low-income households can be significant. It enables economic empowerment, providing them with a pathway to sustainable employment opportunities; improving their overall well-being and sense of personal identity. This article presents qualitative and quantitative data elucidating different elements of employability issues faced by such youth. It distinguishes and divulges a vulnerable group out of a larger population thereby making a strong case for identifying and addressing their needs. The approach is therefore not a 'one size fits all' training method. Students from urban Mumbai and New Delhi were mobilized from multiple channels such as self-help groups (SHGs), the National Institute of Open School (NIOS), distance education institutes, local government colleges, night schools and through direct community outreach. After carefully matching for demographic and economic characteristics, a group of 387 students in the age bracket of 18 to 24 years were selected and interviewed. This first set of data was generated for a range of personal, economic, and household characteristics. Data manifests structural barriers, poor human capital endowments and exclusion. To gain further insight into characteristics and to design a targeted intervention for a sub-group of 130 students from the total population, a second dataset is generated through a questionnaire and interview. Of this, two equal groups - an experiment group and a comparison group are formed as part of quasi-research. The third type of data is generated using a 5-point Likert scale questionnaire and personal discussions. A total of 2600 responses from the experiment (trained and skilled) and comparison (not trained) groups provides a basis for comparison of scores between the two groups pre-and post-intervention. The entire data collection process is practical, straightforward, and simple. Easy to explain - the dataset can be leveraged to generate evidence-based insights, and informed decisions on resource allocation, program design and strategies to mitigate risk factors. The multifaceted approach of data collection can be adapted to accurately identify vulnerable youth and create a newer framework for skill development and re-skilling. It can be used to develop measurement tools for employability by those involved in VET and in their efforts to create viable employment opportunities for high-potential yet disadvantaged youth.

Functionally gradient three-dimensional graphene foam-based polymeric scaffolds for multilayered tissue regeneration.

Physiological bioengineering of multilayered tissues requires an optimized geometric organization with comparable biomechanics. Currently, polymer-reinforced three-dimensional (3D) graphene foams (GFs) are gaining interest in tissue engineering due to their unique morphology, biocompatibility, and similarity to extracellular matrixes. However, the homogeneous reinforcement of single polymers throughout a GF matrix does not provide tissue-level organization. Therefore, a triple-layered structure is developed in a GF matrix to closely mimic native tissue structures of the periodontium of the teeth. The scaffold aims to overcome the issue of layer separation, which generally occurs in multilayered structures due to the poor integration of various layers. The 3D GF matrix was reinforced with a polycaprolactone (PCL), polyvinyl alcohol (PVA), and PCL-hydroxyapatite (HA) mixture, added sequentially, via spin coating, vacuum, and hot air drying. Later, PVA was dissolved to create a middle layer, mimicking the periodontal fibers, while the layers present on either side resembled cementum and alveolar bone, respectively. Scanning electron microscopy and micro-computed tomography revealed the structure of the scaffold with internal differential porosities. The nanoindentation and tensile testing demonstrated the closeness of mechanical properties to that of native tissues. The biocompatibility was assessed by the MTT assay with MG63 cells (human osteosarcoma cells) exhibiting high adhesion and proliferation rate inside the 3D architecture. Summing up, this scaffold has the potential for enhancing the regeneration of various multilayered tissues.

miRador: a fast and precise tool for the prediction of plant miRNAs.

Plant microRNAs (miRNAs) are short, noncoding RNA molecules that restrict gene expression via posttranscriptional regulation and function in several essential pathways, including development, growth, and stress responses. Accurately identifying miRNAs in populations of small RNA sequencing libraries is a computationally intensive process that has resulted in the misidentification of inaccurately annotated miRNA sequences. In recent years, criteria for miRNA annotation have been refined with the aim to reduce these misannotations. Here, we describe miRador, a miRNA identification tool that utilizes the most up-to-date, community-established criteria for accurate identification of miRNAs in plants. We combined target prediction and Parallel Analysis of RNA Ends (PARE) data to assess the precision of the miRNAs identified by miRador. We compared miRador to other commonly used miRNA prediction tools and found that miRador is at least as precise as other prediction tools while being substantially faster than other tools. miRador should be broadly useful for the plant community to identify and annotate miRNAs in plant genomes.

The evolutionary history of small RNAs in Solanaceae.

The Solanaceae or "nightshade" family is an economically important group with remarkable diversity. To gain a better understanding of how the unique biology of the Solanaceae relates to the family's small RNA (sRNA) genomic landscape, we downloaded over 255 publicly available sRNA data sets that comprise over 2.6 billion reads of sequence data. We applied a suite of computational tools to predict and annotate two major sRNA classes: (1) microRNAs (miRNAs), typically 20- to 22-nucleotide (nt) RNAs generated from a hairpin precursor and functioning in gene silencing and (2) short interfering RNAs (siRNAs), including 24-nt heterochromatic siRNAs typically functioning to repress repetitive regions of the genome via RNA-directed DNA methylation, as well as secondary phased siRNAs and trans-acting siRNAs generated via miRNA-directed cleavage of a polymerase II-derived RNA precursor. Our analyses described thousands of sRNA loci, including poorly understood clusters of 22-nt siRNAs that accumulate during viral infection. The birth, death, expansion, and contraction of these sRNA loci are dynamic evolutionary processes that characterize the Solanaceae family. These analyses indicate that individuals within the same genus share similar sRNA landscapes, whereas comparisons between distinct genera within the Solanaceae reveal relatively few commonalities.

Sustainable strategies for combating hydrocarbon pollution: Special emphasis on mobil oil bioremediation.

The global rise in industrialization and vehicularization has led to the increasing trend in the use of different crude oil types. Among these mobil oil has major application in automobiles and different machines. The combustion of mobil oil renders a non-usable form that ultimately enters the environment thereby causing problems to environmental health. The aliphatic and aromatic hydrocarbon fraction of mobil oil has serious human and environmental health hazards. These components upon interaction with soil affect its fertility and microbial diversity. The recent advancement in the omics approach viz. metagenomics, metatranscriptomics and metaproteomics has led to increased efficiency for the use of microbial based remediation strategy. Additionally, the use of biosurfactants further aids in increasing the bioavailability and thus biodegradation of crude oil constituents. The combination of more than one approach could serve as an effective tool for efficient reduction of oil contamination from diverse ecosystems. To the best of our knowledge only a few publications on mobil oil have been published in the last decade. This systematic review could be extremely useful in designing a micro-bioremediation strategy for aquatic and terrestrial ecosystems contaminated with mobil oil or petroleum hydrocarbons that is both efficient and feasible. The state-of-art information and future research directions have been discussed to address the issue efficiently.

Microfluidic platforms for single neuron analysis.

Single-neuron actions are the basis of brain function, as clinical sequelae, neuronal dysfunction or failure for most of the central nervous system (CNS) diseases and injuries can be identified via tracing single-neurons. The bulk analysis methods tend to miscue critical information by assessing the population-averaged outcomes. However, its primary requisite in neuroscience to analyze single-neurons and to understand dynamic interplay of neurons and their environment. Microfluidic systems enable precise control over nano-to femto-liter volumes via adjusting device geometry, surface characteristics, and flow-dynamics, thus facilitating a well-defined micro-environment with spatio-temporal control for single-neuron analysis. The microfluidic platform not only offers a comprehensive landscape to study brain cell diversity at the level of transcriptome, genome, and/or epigenome of individual cells but also has a substantial role in deciphering complex dynamics of brain development and brain-related disorders. In this review, we highlight recent advances of microfluidic devices for single-neuron analysis, i.e., single-neuron trapping, single-neuron dynamics, single-neuron proteomics, single-neuron transcriptomics, drug delivery at the single-neuron level, single axon guidance, and single-neuron differentiation. Moreover, we also emphasize limitations and future challenges of single-neuron analysis by focusing on key performances of throughput and multiparametric activity analysis on microfluidic platforms.

Microfluidic mechanoporation for cellular delivery and analysis.

Highly efficient intracellular delivery strategies are essential for developing therapeutic, diagnostic, biological, and various biomedical applications. The recent advancement of micro/nanotechnology has focused numerous researches towards developing microfluidic device-based strategies due to the associated high throughput delivery, cost-effectiveness, robustness, and biocompatible nature. The delivery strategies can be carrier-mediated or membrane disruption-based, where membrane disruption methods find popularity due to reduced toxicity, enhanced delivery efficiency, and cell viability. Among all of the membrane disruption techniques, the mechanoporation strategies are advantageous because of no external energy source required for membrane deformation, thereby achieving high delivery efficiencies and increased cell viability into different cell types with negligible toxicity. The past two decades have consequently seen a tremendous boost in mechanoporation-based research for intracellular delivery and cellular analysis. This article provides a brief review of the most recent developments on microfluidic-based mechanoporation strategies such as microinjection, nanoneedle arrays, cell-squeezing, and hydroporation techniques with their working principle, device fabrication, cellular delivery, and analysis. Moreover, a brief discussion of the different mechanoporation strategies integrated with other delivery methods has also been provided. Finally, the advantages, limitations, and future prospects of this technique are discussed compared to other intracellular delivery techniques.

Microfluidic nanomaterials: From synthesis to biomedical applications.

Microfluidic platforms gain popularity in biomedical research due to their attractive inherent features, especially in nanomaterials synthesis. This review critically evaluates the current state of the controlled synthesis of nanomaterials using microfluidic devices. We describe nanomaterials' screening in microfluidics, which is very relevant for automating the synthesis process for biomedical applications. We discuss the latest microfluidics trends to achieve noble metal, silica, biopolymer, quantum dots, iron oxide, carbon-based, rare-earth-based, and other nanomaterials with a specific size, composition, surface modification, and morphology required for particular biomedical application. Screening nanomaterials has become an essential tool to synthesize desired nanomaterials using more automated processes with high speed and repeatability, which can't be neglected in today's microfluidic technology. Moreover, we emphasize biomedical applications of nanomaterials, including imaging, targeting, therapy, and sensing. Before clinical use, nanomaterials have to be evaluated under physiological conditions, which is possible in the microfluidic system as it stimulates chemical gradients, fluid flows, and the ability to control microenvironment and partitioning multi-organs. In this review, we emphasize the clinical evaluation of nanomaterials using microfluidics which was not covered by any other reviews. In the future, the growth of new materials or modification in existing materials using microfluidics platforms and applications in a diversity of biomedical fields by utilizing all the features of microfluidic technology is expected.

Severity and mortality prediction models to triage Indian COVID-19 patients.

As the second wave in India mitigates, COVID-19 has now infected about 29 million patients countrywide, leading to more than 350 thousand people dead. As the infections surged, the strain on the medical infrastructure in the country became apparent. While the country vaccinates its population, opening up the economy may lead to an increase in infection rates. In this scenario, it is essential to effectively utilize the limited hospital resources by an informed patient triaging system based on clinical parameters. Here, we present two interpretable machine learning models predicting the clinical outcomes, severity, and mortality, of the patients based on routine non-invasive surveillance of blood parameters from one of the largest cohorts of Indian patients at the day of admission. Patient severity and mortality prediction models achieved 86.3% and 88.06% accuracy, respectively, with an AUC-ROC of 0.91 and 0.92. We have integrated both the models in a user-friendly web app calculator, https://triage-COVID-19.herokuapp.com/, to showcase the potential deployment of such efforts at scale.

Effect of COVID-19 lockdown on lifestyle habits and self-care practices of diabetic and hypertensive patients in rural Shimla and Udaipur - Findings from the HealthRise India program.

Background: COVID-19 pandemic has increased the risk of mortality among patients with noncommunicable diseases. Maintaining a good metabolic control, lifestyle modification along with improved self-care practices are not only associated with less severe COVID-19 infections but also with a high recovery rate. Objectives: This research article explores the changes in lifestyle habits, self-care practices, and metabolic control among patients enrolled in the HealthRise program. The study compares behavioral changes, before COVID-19 pandemic and during COVID-19 pandemic, between intervention and control arms in Shimla and Udaipur. Methods: A quasi-experimental study design was employed for program implementation in select villages of Shimla district, and Udaipur district. A total of 459 patients from Shimla and 309 patients from Udaipur with diabetes mellitus or hypertension or with both were enrolled and followed for 1 year. Results: Metabolic control in Shimla intervention arm was 2.6 times higher than in control arm (P = 0.001) before COVID-19 pandemic. During COVID-19 pandemic, Odds of metabolic control in Shimla intervention was 1.5 times higher when compared with control arm (P = 0.03). In Udaipur, metabolic control before COVID-19 pandemic was comparable between control and intervention arms. During the pandemic, metabolic control in intervention arm of Udaipur was 5 times higher when compared to the control arm ((P = 0.001). Conclusion: Participants exposed to support, appreciate, learn, and transfer-community life competence process (SALT-CLCP) intervention maintained metabolic control during the COVID-19 pandemic with improved behavioral and self-care practices. Community-based interventions such as SALT-CLCP method bring ownership and empower community in achieving the better health outcomes.

Leveraging Unsupervised Machine Learning to Discover Patterns in Linguistic Health Summaries for Eldercare.

The Center for Eldercare and Rehabilitation Technology, at University of Missouri, has researched the use of smart, unobtrusive sensors for older adult residents' health monitoring and alerting in aging-in-place communities for many years. Sensors placed in the apartments of older adult residents generate a deluge of daily data that is automatically aggregated, analyzed, and summarized to aid in health awareness, clinical care, and research for healthy aging. When anomalies or concerning trends are detected within the data, the sensor information is converted into linguistic health messages using fuzzy computational techniques, so as to make it understandable to the clinicians. Sensor data are analyzed at the individual level, therefore, through this study we aim to discover various combinations of patterns of anomalies happening together and recurrently in the older adult's population using these text summaries. Leveraging various computational text data processing techniques, we are able to extract relevant analytical features from the health messages. These features are transformed into a transactional encoding, then processed with frequent pattern mining techniques for association rule discovery. At individual level analysis, resident ID 3027 was considered as an exemplar to describe the analysis. Seven combinations of anomalies/rules/associations were discovered in this resident, out of which rule group three showed an increased recurrence during the COVID lockdown of facility. At the population level, a total of 38 associations were discovered that highlight the health patterns, and we continue to explore the health conditions associated with them. Ultimately, our goal is to correlate the combinations of anomalies with certain health conditions, which can then be leveraged for predictive analytics and preventative care. This will improve the current clinical care systems for older adult residents in smart sensor, aging-in-place communities.

Novel Candidate Genes Differentially Expressed in Glyphosate-Treated Horseweed (Conyza canadensis).

The evolution of herbicide-resistant weed species is a serious threat for weed control. Therefore, we need an improved understanding of how gene regulation confers herbicide resistance in order to slow the evolution of resistance. The present study analyzed differentially expressed genes after glyphosate treatment on a glyphosate-resistant Tennessee ecotype (TNR) of horseweed (Conyza canadensis), compared to a susceptible biotype (TNS). A read size of 100.2 M was sequenced on the Illumina platform and subjected to de novo assembly, resulting in 77,072 gene-level contigs, of which 32,493 were uniquely annotated by a BlastX alignment of protein sequence similarity. The most differentially expressed genes were enriched in the gene ontology (GO) term of the transmembrane transport protein. In addition, fifteen upregulated genes were identified in TNR after glyphosate treatment but were not detected in TNS. Ten of these upregulated genes were transmembrane transporter or kinase receptor proteins. Therefore, a combination of changes in gene expression among transmembrane receptor and kinase receptor proteins may be important for endowing non-target-site glyphosate-resistant C. canadensis.

Detrimental effect of diabetes and hypertension on the severity and mortality of COVID-19 infection: A multi-center case-control study from India.

AIMS: This study aims to find a quantitative association between the presence of co-existing diabetes mellitus (DM) and/or hypertension (HTN) with COVID-19 infection severity and mortality. METHODS: A total of 813 patients with a positive COVID-19 were included. A case-control design was used to dissect the association between DM and HTN with COVID-19 severity and mortality. RESULTS: According to MOHFW guidelines, 535 (65.7%) patients had mild, 160 (19.7%) patients had moderate, and 118 (14.5%) patients had severe disease outcomes including mortality in 52 patients. Age, Neutrophil%, and Diabetes status were significantly associated with severe COVID-19 infection. After adjusting for age, patients with diabetes were 2.46 times more likely to have severe disease (Chi-squared = 18.89, p-value<0.0001) and 2.11 times more likely to have a fatal outcome (Chi-squared = 6.04, p-value = 0.014). However, we did not find evidence for Hypertension modifying the COVID-19 outcomes in Diabetic patients. CONCLUSION: COVID-19 severity and mortality both were significantly associated with the status of DM and its risk may not be modified by the presence of HTN.