SMARThealth PRegnancy And Mental Health study: protocol for a situational analysis of perinatal mental health in women living in rural India.

Introduction: The situation for women experiencing mental health problems during pregnancy and postpartum in rural India is critical: a high burden of disease, a high estimated number of women are undiagnosed and untreated with mental health problems, a substantial gap in research on women's perinatal health, and severe stigma and discrimination. The SMARThealth Pregnancy study is a cluster randomised trial using a digital intervention to identify and manage anaemia, hypertension, and diabetes in the first year after birth in rural India. Within this study, the SMARThealth Pregnancy and Mental Health (PRAMH) study is a situational analysis to understand mental health problems during pregnancy and in the first year following birth in this population. Methods/design: This situational analysis aims to analyse and to assess the context of perinatal mental health, health services, barriers, facilitators, and gaps in Siddipet district of Telangana state in India, to develop an implementation framework for a future intervention. A tested, standardised situational analysis tool will be adapted and applied to perinatal mental health in rural India. A desktop and policy review will be conducted to identify and analyse relevant mental health and pregnancy care policies at the national and state levels. We will conduct in-depth interviews with policymakers, planners, mental health professionals and other experts in perinatal mental health (n = 10-15). We will also conduct focus group discussions with key stakeholders, including women with perinatal mental health problems, their families and carers, and community health workers (n = 24-40). A theory of change workshop with key stakeholders will be conducted which will also serve as a priority setting exercise, and will clarify challenges and opportunities, priorities, and objectives for a pilot intervention study. The analysis of qualitive data will be done using thematic analysis. Based on the data analysis and synthesis of the findings, an implementation framework will be developed to guide development, testing and scale up of a contextually relevant intervention for perinatal mental health. Discussion: The situational analysis will help to establish relationships with all relevant stakeholders, clarify the context and hypotheses for the pilot intervention and implementation.

A deep learning method to detect opioid prescription and opioid use disorder from electronic health records.

OBJECTIVE: As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD. MATERIALS AND METHODS: We developed an informatics algorithm that trains two deep learning models over patient Electronic Health Records (EHRs) using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both challenging outcomes. RESULTS: Our deep learning models incorporate elements from EHRs to predict opioid prescription with an F1-score of 0.88 ± 0.003 and an AUC-ROC of 0.93 ± 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 ± 0.05 and AUC-ROC of 0.94 ± 0.008. DISCUSSION: Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a) deep learning approaches in predicting OUD and b) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore our model is the first to predict opioid prescribing behavior. CONCLUSION: Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic.

Space in cancer biology: its role and implications.

Tumor cells present complex behaviors in their interactions with other cells. This intricate behavior is driving the need to develop new tools to understand these ecosystems. The surge of spatial technologies allows evaluation of the complexity of relationships between cells present in a tumor, giving insights about tumor heterogeneity and the tumor microenvironment while providing clinically relevant metrics for tumor classification. In this review, we describe key results obtained using spatial techniques, present recent advances in methods to uncover spatially relevant biological significance, and summarize their main characteristics. We expect spatial technologies to significantly broaden our understanding of tumor biology and to generate clinically relevant tools that will ultimately impact personalized medicine.

Quantification of tumor heterogeneity: from data acquisition to metric generation.

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Biopatterning: The Art of Patterning Biomolecules on Surfaces.

Patterning biomolecules on surfaces provides numerous opportunities for miniaturizing biological assays; biosensing; studying proteins, cells, and tissue sections; and engineering surfaces that include biological components. In this Feature Article, we summarize the themes presented in our recent Langmuir Lecture on patterning biomolecules on surfaces, miniaturizing surface assays, and interacting with biointerfaces using three key technologies: microcontact printing, microfluidic networks, and microfluidic probes.

Mapping Spatial Genetic Landscapes in Tissue Sections through Microscale Integration of Sampling Methodology into Genomic Workflows.

In cancer research, genomic profiles are often extracted from homogenized macrodissections of tissues, with the histological context lost and a large fraction of material underutilized. Pertinently, the spatial genomic landscape provides critical complementary information in deciphering disease heterogeneity and progression. Microscale sampling methods such as microdissection to obtain such information are often destructive to a sizeable fraction of the biopsy sample, thus showing limited multiplexability and adaptability to different assays. A modular microfluidic technology is here implemented to recover cells at the microscale from tumor tissue sections, with minimal disruption of unsampled areas and tailored to interface with genome profiling workflows, which is directed here toward evaluating intratumoral genomic heterogeneity. The integrated workflow-GeneScape-is used to evaluate heterogeneity in a metastatic mammary carcinoma, showing distinct single nucleotide variants and copy number variations in different tumor tissue regions, suggesting the polyclonal origin of the metastasis as well as development driven by multiple location-specific drivers.

Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study.

BACKGROUND: Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to the provision of inappropriate care. Initial testing has revealed that artificially intelligent bots can detect misinformation regarding tanning and essential oils on Reddit dermatology forums and may be able to produce responses to posts containing misinformation. OBJECTIVE: To analyze the ability of bots to find and respond to tanning and essential oil-related health misinformation on Reddit's dermatology forums in a controlled test environment. METHODS: Using natural language processing techniques, we trained bots to target misinformation, using relevant keywords and to post prefabricated responses. By evaluating different model architectures across a held-out test set, we compared performances. RESULTS: Our models yielded data test accuracies ranging 95%-100%, with a Bidirectional Encoder Representations from Transformers (BERT) fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective prefabricated responses to misinformation in a test environment. CONCLUSIONS: Using a limited data set, bots accurately detected examples of health misinformation within Reddit dermatology forums. Given that these bots can then post prefabricated responses, this technique may allow for interception of misinformation. Providing correct information does not mean that users will be receptive or find such interventions persuasive. Further studies should investigate this strategy's effectiveness to inform future deployment of bots as a technique in combating health misinformation.

The CLASSE GATOR (CLinical Acronym SenSE disambiGuATOR): A Method for predicting acronym sense from neonatal clinical notes.

OBJECTIVE: To develop an algorithm for identifying acronym 'sense' from clinical notes without requiring a clinically annotated training set. MATERIALS AND METHODS: Our algorithm is called CLASSE GATOR: Clinical Acronym SenSE disambiGuATOR. CLASSE GATOR extracts acronyms and definitions from PubMed Central (PMC). A logistic regression model is trained using words associated with specific acronym-definition pairs from PMC. CLASSE GATOR uses this library of acronym-definitions and their corresponding word feature vectors to predict the acronym 'sense' from Beth Israel Deaconess (MIMIC-III) neonatal notes. RESULTS: We identified 1,257 acronyms and 8,287 definitions including a random definition from 31,764 PMC articles on prenatal exposures and 2,227,674 PMC open access articles. The average number of senses (definitions) per acronym was 6.6 (min = 2, max = 50). The average internal 5-fold cross validation was 87.9 % (on PMC). We found 727 unique acronyms (57.29 %) from PMC were present in 105,044 neonatal notes (MIMIC-III). We evaluated the performance of acronym prediction using 245 manually annotated clinical notes with 9 distinct acronyms. CLASSE GATOR achieved an overall accuracy of 63.04 % and outperformed random for 8/9 acronyms (88.89 %) when applied to clinical notes. We also compared our algorithm with UMN's acronym set, and found that CLASSE GATOR outperformed random for 63.46 % of 52 acronyms when using logistic regression, 75.00 % when using Bert and 76.92 % when using BioBert as the prediction algorithm within CLASSE GATOR. CONCLUSIONS: CLASSE GATOR is the first automated acronym sense disambiguation method for clinical notes. Importantly, CLASSE GATOR does not require an expensive manually annotated acronym-definition corpus for training.

Spatially Resolved Genetic Analysis of Tissue Sections Enabled by Microscale Flow Confinement Retrieval and Isotachophoretic Purification.

We have developed a method for spatially resolved genetic analysis of formalin-fixed paraffin-embedded (FFPE) cell block and tissue sections. This method involves local sampling using hydrodynamic flow confinement of a lysis buffer, followed by electrokinetic purification of nucleic acids from the sampled lysate. We characterized the method by locally sampling an array of points with a circa 200 µm diameter footprint, enabling the detection of single KRAS and BRAF point mutations in small populations of RKO and MCF-7 FFPE cell blocks. To illustrate the utility of this approach for genetic analysis, we demonstrate spatially resolved genotyping of FFPE sections of human breast invasive ductal carcinoma.

Quantitative microimmunohistochemistry for the grading of immunostains on tumour tissues.

Immunohistochemistry is the gold-standard method for cancer-biomarker identification and patient stratification. Yet, owing to signal saturation, its use as a quantitative assay is limited as it cannot distinguish tumours with similar biomarker-expression levels. Here, we introduce a quantitative microimmunochemistry assay that enables the acquisition of dynamic information, via a metric of the evolution of the immunohistochemistry signal during tissue staining, for the quantification of relative antigen density on tissue surfaces. We used the assay to stratify 30 patient-derived breast-cancer samples into conventional classes and to determine the proximity of each sample to the other classes. We also show that the assay enables the quantification of multiple biomarkers (human epidermal growth factor receptor, oestrogen receptor and progesterone receptor) in a standard breast-cancer panel. The integration of quantitative microimmunohistochemistry into current pathology workflows may lead to improvements in the precision of biomarker quantification.

High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization.

Accurate profiling of tumors using immunohistochemistry (IHC) is essential in cancer diagnosis. The inferences drawn from IHC-stained images depend to a great extent on the quality of immunostaining, which is in turn affected strongly by assay parameters. To optimize assay parameters, the available tissue sample is often limited. Moreover, with current practices in pathology, exploring the entire assay parameter space is not feasible. Thus, the evaluation of IHC stained slides is conventionally a subjective task, in which diagnoses are commonly drawn on images that are suboptimal. In this work, we introduce a framework to analyze IHC staining quality and its sensitivity to process parameters. To that extent, first histopathological sections are segmented automatically. Then, machine learning techniques are employed to extract disease-specific staining quality metrics (SQMs) targeting a quantitative assessment of staining quality. Finally, an approach to efficiently analyze the parameter space is introduced to infer sensitivity to process parameters. We present results on microscale IHC tissue samples of five breast tumor classes, based on disease state and protein expression. A disease-type classification F1-score of 0.82 and a contrast-level classification F1-score of 0.95 were achieved. With the proposed SQMs, an area under the curve of 0.85 was achieved on average over different disease types. Our methodology provides a promising step in automatically evaluating and quantifying staining quality of IHC stained tissue sections, and it can potentially standardize immunostaining across diagnostic laboratories.

Development and validation of the PEPPER framework (Prenatal Exposure PubMed ParsER) with applications to food additives.

Background: Globally, 36% of deaths among children can be attributed to environmental factors. However, no comprehensive list of environmental exposures exists. We seek to address this gap by developing a literature-mining algorithm to catalog prenatal environmental exposures. Methods: We designed a framework called. PEPPER: Prenatal Exposure PubMed ParsER to a) catalog prenatal exposures studied in the literature and b) identify study type. Using PubMed Central, PEPPER classifies article type (methodology, systematic review) and catalogs prenatal exposures. We coupled PEPPER with the FDA's food additive database to form a master set of exposures. Results: We found that of 31 764 prenatal exposure studies only 53.0% were methodology studies. PEPPER consists of 219 prenatal exposures, including a common set of 43 exposures. PEPPER captured prenatal exposures from 56.4% of methodology studies (9492/16 832 studies). Two raters independently reviewed 50 randomly selected articles and annotated presence of exposures and study methodology type. Error rates for PEPPER's exposure assignment ranged from 0.56% to 1.30% depending on the rater. Evaluation of the study type assignment showed agreement ranging from 96% to 100% (kappa = 0.909, p < .001). Using a gold-standard set of relevant prenatal exposure studies, PEPPER achieved a recall of 94.4%. Conclusions: Using curated exposures and food additives; PEPPER provides the first comprehensive list of 219 prenatal exposures studied in methodology papers. On average, 1.45 exposures were investigated per study. PEPPER successfully distinguished article type for all prenatal studies allowing literature gaps to be easily identified.

Tissue lithography: Microscale dewaxing to enable retrospective studies on formalin-fixed paraffin-embedded (FFPE) tissue sections.

We present a new concept, termed tissue lithography (TL), and its implementation which enables retrospective studies on formalin-fixed paraffin-embedded tissue sections. Tissue lithography uses a microfluidic probe to remove microscale areas of the paraffin layer on formalin-fixed paraffin-embedded biopsy samples. Current practices in sample utilization for research and diagnostics require complete deparaffinization of the sample prior to molecular testing. This imposes strong limitations in terms of the number of tests as well as the time when they can be performed on a single sample. Microscale dewaxing lifts these constraints by permitting deprotection of a fraction of a tissue for testing while keeping the remaining of the sample intact for future analysis. After testing, the sample can be sent back to storage instead of being discarded, as is done in standard workflows. We achieve this microscale dewaxing by hydrodynamically confining nanoliter volumes of xylene on top of the sample with a probe head. We demonstrate micrometer-scale, chromogenic and fluorescence-based immunohistochemistry against multiple biomarkers (p53, CD45, HER2 and ß-actin) on tonsil and breast tissue sections and microarrays. We achieve stain patterns as small as 100 µm × 50 µm as well as multiplexed immunostaining within a single tissue microarray core with a 20-fold time reduction for local dewaxing as compared to standard protocols. We also demonstrate a 10-fold reduction in the rehydration time, leading to lower processing times between different stains. We further show the potential of TL for retrospective studies by sequentially dewaxing and staining four individual cores within the same tissue microarray over four consecutive days. By combining tissue lithography with the concept of micro-immunohistochemistry, we implement each step of the IHC protocol-dewaxing, rehydration and staining-with the same microfluidic probe head. Tissue lithography brings a new level of versatility and flexibility in sample processing and budgeting in biobanks, which may alleviate current sample limitations for retrospective studies in biomarker discovery and drug screening.

Rapid Subtractive Patterning of Live Cell Layers with a Microfluidic Probe.

The microfluidic probe (MFP) facilitates performing local chemistry on biological substrates by confining nanoliter volumes of liquids. Using one particular implementation of the MFP, the hierarchical hydrodynamic flow confinement (hHFC), multiple liquids are simultaneously brought in contact with a substrate. Local chemical action and liquid shaping using the hHFC, is exploited to create cell patterns by locally lysing and removing cells. By utilizing the scanning ability of the MFP, user-defined patterns of cell monolayers are created. This protocol enables rapid, real-time and spatially controlled cell patterning, which can allow selective cell-cell and cell-matrix interaction studies.

Selective local lysis and sampling of live cells for nucleic acid analysis using a microfluidic probe.

Heterogeneity is inherent to biology, thus it is imperative to realize methods capable of obtaining spatially-resolved genomic and transcriptomic profiles of heterogeneous biological samples. Here, we present a new method for local lysis of live adherent cells for nucleic acid analyses. This method addresses bottlenecks in current approaches, such as dilution of analytes, one-sample-one-test, and incompatibility to adherent cells. We make use of a scanning probe technology - a microfluidic probe - and implement hierarchical hydrodynamic flow confinement (hHFC) to localize multiple biochemicals on a biological substrate in a non-contact, non-destructive manner. hHFC enables rapid recovery of nucleic acids by coupling cell lysis and lysate collection. We locally lysed ~300 cells with chemical systems adapted for DNA or RNA and obtained lysates of ~70 cells/µL for DNA analysis and ~15 cells/µL for mRNA analysis. The lysates were introduced into PCR-based workflows for genomic and transcriptomic analysis. This strategy further enabled selective local lysis of subpopulations in a co-culture of MCF7 and MDA-MB-231 cells, validated by characteristic E-cadherin gene expression in individually extracted cell types. The developed strategy can be applied to study cell-cell, cell-matrix interactions locally, with implications in understanding growth, progression and drug response of a tumor.

Hierarchical hydrodynamic flow confinement: efficient use and retrieval of chemicals for microscale chemistry on surfaces.

We devised, implemented, and tested a new concept for efficient local surface chemistry that we call hierarchical hydrodynamic flow confinement (hierarchical HFC). This concept leverages the hydrodynamic shaping of multiple layers of liquid to address challenges inherent to microscale surface chemistry, such as minimal dilution, economical consumption of reagent, and fast liquid switching. We illustrate two modes of hierarchical HFC, nested and pinched, by locally denaturing and recovering a 26 bp DNA with as little as 2% dilution and by efficiently patterning an antibody on a surface, with a 5 µm resolution and a 100-fold decrease of reagent consumption compared to microcontact printing. In addition, valveless switching between nanoliter volumes of liquids was achieved within 20 ms. We believe hierarchical HFC will have broad utility for chemistry on surfaces at the microscale.

Development of functional biomaterials with micro- and nanoscale technologies for tissue engineering and drug delivery applications.

Micro- and nanotechnologies have emerged as potentially effective fabrication tools for addressing the challenges faced in tissue engineering and drug delivery. The ability to control and manipulate polymeric biomaterials at the micron and nanometre scale with these fabrication techniques has allowed for the creation of controlled cellular environments, engineering of functional tissues and development of better drug delivery systems. In tissue engineering, micro- and nanotechnologies have enabled the recapitulation of the micro- and nanoscale detail of the cell's environment through controlling the surface chemistry and topography of materials, generating 3D cellular scaffolds and regulating cell-cell interactions. Furthermore, these technologies have led to advances in high-throughput screening (HTS), enabling rapid and efficient discovery of a library of materials and screening of drugs that induce cell-specific responses. In drug delivery, controlling the size and geometry of drug carriers with micro- and nanotechnologies have allowed for the modulation of parametres such as bioavailability, pharmacodynamics and cell-specific targeting. In this review, we introduce recent developments in micro- and nanoscale engineering of polymeric biomaterials, with an emphasis on lithographic techniques, and present an overview of their applications in tissue engineering, HTS and drug delivery.

Chondrocyte culture in three dimensional alginate sulfate hydrogels promotes proliferation while maintaining expression of chondrogenic markers.

The loss of expression of chondrogenic markers during monolayer expansion remains a stumbling block for cell-based treatment of cartilage lesions. Here, we introduce sulfated alginate hydrogels as a cartilage biomimetic biomaterial that induces cell proliferation while maintaining the chondrogenic phenotype of encapsulated chondrocytes. Hydroxyl groups of alginate were converted to sulfates by incubation with sulfur trioxide-pyridine complex (SO3/pyridine), yielding a sulfated material cross-linkable with calcium chloride. Passage 3 bovine chondrocytes were encapsulated in alginate and alginate sulfate hydrogels for up to 35 days. Cell proliferation was five-fold higher in alginate sulfate compared with alginate (p=0.038). Blocking beta1 integrins in chondrocytes within alginate sulfate hydrogels significantly inhibited proliferation (p=0.002). Sulfated alginate increased the RhoA activity of chondrocytes compared with unmodified alginate, an increase that was blocked by ß1 blocking antibodies (p=0.017). Expression and synthesis of type II collagen, type I collagen, and proteoglycan was not significantly affected by the encapsulation material evidenced by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry. Alginate sulfate constructs showed an opaque appearance in culture, whereas the unmodified alginate samples remained translucent. In conclusion, alginate sulfate provides a three dimensional microenvironment that promotes both chondrocyte proliferation and maintenance of the chondrogenic phenotype and represents an important advance for chondrocyte-based cartilage repair therapies providing a material in which cell expansion can be done in situ.