Artificial intelligence in neurology: opportunities, challenges, and policy implications

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

Artificial Intelligence in Cardiovascular Care - Part 2: Applications: JACC Review Topic of the Week

Recent Artificial Intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. Over 600 Food and Drug Administration (FDA)-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care - Part 1: Advances: JACC Review Topic of the Week

Recent AI advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitates rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

An intentional approach to managing bias in general purpose embedding models

Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuation, or exacerbation of care disparities. Reinforcing these concerns is the finding that medical images often reveal signals about sensitive attributes in ways that are hard to pinpoint by both algorithms and people. This finding raises a question about how to best design general purpose pretrained embeddings (GPPEs, defined as embeddings meant to support a broad array of use cases) for building downstream models that are free from particular types of bias. The downstream model should be carefully evaluated for bias, and audited and improved as appropriate. However, in our view, well intentioned attempts to prevent the upstream components-GPPEs-from learning sensitive attributes can have unintended consequences on the downstream models. Despite producing a veneer of technical neutrality, the resultant end-to-end system might still be biased or poorly performing. We present reasons, by building on previously published data, to support the reasoning that GPPEs should ideally contain as much information as the original data contain, and highlight the perils of trying to remove sensitive attributes from a GPPE. We also emphasise that downstream prediction models trained for specific tasks and settings, whether developed using GPPEs or not, should be carefully designed and evaluated to avoid bias that makes models vulnerable to issues such as distributional shift. These evaluations should be done by a diverse team, including social scientists, on a diverse cohort representing the full breadth of the patient population for which the final model is intended.

Genetic Discovery Enabled by A Large Language Model

Artificial intelligence (AI) has been used in many areas of medicine, and recently large language models (LLMs) have shown potential utility for clinical applications. However, since we do not know if the use of LLMs can accelerate the pace of genetic discovery, we used data generated from mouse genetic models to investigate this possibility. We examined whether a recently developed specialized LLM (Med-PaLM 2) could analyze sets of candidate genes generated from analysis of murine models of biomedical traits. In response to free-text input, Med-PaLM 2 correctly identified the murine genes that contained experimentally verified causative genetic factors for six biomedical traits, which included susceptibility to diabetes and cataracts. Med-PaLM 2 was also able to analyze a list of genes with high impact alleles, which were identified by comparative analysis of murine genomic sequence data, and it identified a causative murine genetic factor for spontaneous hearing loss. Based upon this Med-PaLM 2 finding, a novel bigenic model for susceptibility to spontaneous hearing loss was developed. These results demonstrate Med-PaLM 2 can analyze gene-phenotype relationships and generate novel hypotheses, which can facilitate genetic discovery.

Large language models encode clinical knowledge

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model1 (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM2 on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA3, MedMCQA4, PubMedQA5 and Measuring Massive Multitask Language Understanding (MMLU) clinical topics6), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications.

Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians

Predictive artificial intelligence (AI) systems based on deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings, but can make errors in cases accurately diagnosed by clinicians and vice versa. We developed Complementarity-Driven Deferral to Clinical Workflow (CoDoC), a system that can learn to decide between the opinion of a predictive AI model and a clinical workflow. CoDoC enhances accuracy relative to clinician-only or AI-only baselines in clinical workflows that screen for breast cancer or tuberculosis (TB). For breast cancer screening, compared to double reading with arbitration in a screening program in the UK, CoDoC reduced false positives by 25% at the same false-negative rate, while achieving a 66% reduction in clinician workload. For TB triaging, compared to standalone AI and clinical workflows, CoDoC achieved a 5-15% reduction in false positives at the same false-negative rate for three of five commercially available predictive AI systems. To facilitate the deployment of CoDoC in novel futuristic clinical settings, we present results showing that CoDoC's performance gains are sustained across several axes of variation (imaging modality, clinical setting and predictive AI system) and discuss the limitations of our evaluation and where further validation would be needed. We provide an open-source implementation to encourage further research and application.

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such 'out of distribution' performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for 'Robust and Efficient Medical Imaging with Self-supervision'), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1-33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging.

Optimization with photonic wave-based annealers

Many NP-hard combinatorial optimization (CO) problems can be cast as a quadratic unconstrained binary optimization model, which maps naturally to an Ising model. The final spin configuration in the Ising model can adiabatically arrive at a solution to a Hamiltonian, given a known set of interactions between spins. We enhance two photonic Ising machines (PIMs) and compare their performance against classical (Gurobi) and quantum (D-Wave) solvers. The temporal multiplexed coherent Ising machine (TMCIM) uses the bistable response of an electro-optic modulator to mimic the spin up and down states. We compare TMCIM performance on Max-cut problems. A spatial photonic Ising machine (SPIM) convolves the wavefront of a coherent laser beam with the pixel distribution of a spatial light modulator to adiabatically achieve a minimum energy configuration, and solve a number partitioning problem (NPP). Our computational results on Max-cut indicate that classical solvers are still a better choice, while our NPP results show that SPIM is better as the problem size increases. In both cases, connectivity in Ising hardware is crucial for performance. Our results also highlight the importance of better understanding which CO problems are most likely to benefit from which type of PIM. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.

Efficient big data security analysis on HDFS based on combination of clustering and data perturbation algorithm using health care database

In this manuscript proposes an efficient big data security analysis on HDFS based on the combination of Improved Deep K-means Clustering (IDFKM) algorithm and Modified 3D rotation data perturbation algorithm using health care database. To compile a similar group of data, an Improved Deep K-means Clustering (IDFKM) Algorithm is used as partitioning the medical data. After clustering, Modified 3D rotation data perturbation technique is used to satisfy the privacy requirement of the client. Modified 3D rotation Data Perturbation technique perturbs each and every sensitive data of the cluster and all the key parameters values used for clustering have warehoused in the database file sector. The proposed approach is executed by Java program, its efficiency is assessed by Health care database. The metrics under the study of memory usage attains higher accuracy 34.765%, 23.44%, 52.74%, 18.74%, lower execution time 35.23%, 23.76%, 27.86%, 27.76%, higher Efficiency 26.85%, 38.97%, 28.97%, 35.65%. then the proposed method is compared with the existing methods such asSecurity Analysis of SDN Applications for Big Data with spoofing identity, Tampering with data, Repudiation threats, Information disclosure, Denial of service and Elevation of privileges (STRIDE), Big Data Analysis-based Secure Cluster Management for using Ant Colony Optimization (ACA) Optimized Control Plane in Software-Defined Networks, System Architecture for Secure Authentication and Data Sharing in Cloud Enabled Big Data Environment using LemperlZivMarkow Algorithm (LZMA) and Density-based Clustering of Applications with Noise (DBSCAN), Big Data Based Security Analytics using data based security analytics (BDSA) approach for Protecting Virtualized Infrastructures in Cloud Computing respectively.

Effectiveness of a home-based cardiovascular disease prevention program during the COVID-19 pandemic

Funding Acknowledgements

Type of funding sources: None.

Background

Home-based cardiovascular disease (CVD) primary prevention (HBPP) and cardiac rehabilitation (HBCR) programs which occupied a small proportion of the overall Preventive Cardiology work in the past have become mainstream during the COVID-19 pandemic.

Purpose

This study aims to analyse the effectiveness of a home-based CVD prevention program implemented during the pandemic in India.

Methods

A retrospective study was conducted on pre-pandemic and pandemic enrolees. Health behaviour, CVD risk factors, physical and mental component score (PCS, MCS) from SF-12 questionnaire, body mass index (BMI), 6-minute walk distance (6MWD), and clinical and biochemical parameters were assessed. A multidisciplinary team consisting of Physician, Physiotherapist, Dietician and Counselling Psychologist provided the program using tele-health platforms.

Results

Of the 66 subjects (55 ± 13 years, 73% male), 17 (26%) enrolled pre-pandemic and 49 (74%) enrolled during-pandemic, 28 (42%) were HBPP and 38 (58%) were HBCR participants. Majority of the subjects (n = 51, 77%), with significantly more HBCR than HBPP participants, harboured 4 or more risk factors (p = 0.04). In the 60 (91%) program completers, BMI, 6MWD, PCS and MCS had improved significantly. SBP, DBP, LVEF, HbA1c, total cholesterol and LDL had improved significantly in affected subjects. Completely home-based participants (n = 44, 67%) who never had any in-person contact with the team during the program also showed significant improvement. No adverse events were reported.

Conclusion

Comprehensive home-based CVD prevention programs are effective in improving anthropometric, clinical, biochemical and psychosocial parameters, are a safe alternative to conventional programs and could potentially become the standard-of-care in the post-pandemic era.

A survey-based comparison of cardiovascular health risk behaviours in Indian men and women during the pandemic

Funding Acknowledgements

Type of funding sources: None.

Background

Cardiovascular disease (CVD), the leading cause of mortality globally, is caused by a combination of behavioral, clinical and genetic risk factors. The rising prevalence of unhealthy diet, inadequate exercise, psychosocial factors and tobacco use in urban and rural populations of India has posed a major epidemiological and healthcare threat in the recent past.

Purpose

This study aims to understand the cardiovascular health risk behaviors in Indian men and women during the pandemic.

Methods

A survey-based study was conducted at different time points between April 2020 and August 2021. A pre-tested anonymized self-administered questionnaire was distributed online as a pre-program assessment of participants registering for the health awareness webinars conducted by our team. Demographic data and CVD risk behaviors pertaining to dietary consumption, exercise routine, sedentary behavior, psychosocial factors, sleep pattern, tobacco use and alcohol consumption were collected. Not meeting daily intake of whole grains and at least 4 portions of vegetables and fruits, non-performance of at least 150 minutes of brisk aerobic exercise per week, chronic stress and use of tobacco were considered risky behaviors, as per the international guidelines.

Results

Of the 573 subjects, 305 (53%) were women and 236 (41%) were 15-30 years old. Majority of subjects were not meeting their requirements pertaining to whole grains (76%) and fruits/vegetables (72%). Inadequate exercise (48%), chronic stress (31%) and tobacco use (5%) were noted. Significantly more women than men (p = 0.0001) were falling short in their exercise requirement. Significantly more men than women (p = 0.0001) were using tobacco products (Figure).

Conclusion

Cardiovascular risk behaviors are highly prevalent in Indian men and women during the pandemic calling for a coordinated effort to increase awareness and modify to a healthy behavior. Abstract Figure. Risk behaviors in study subjects  Abstract Figure. Comparison of risk behaviors

Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions

Supervised deep learning models have proven to be highly effective in classification of dermatological conditions. These models rely on the availability of abundant labeled training examples. However, in the real-world, many dermatological conditions are individually too infrequent for per-condition classification with supervised learning. Although individually infrequent, these conditions may collectively be common and therefore are clinically significant in aggregate. To prevent models from generating erroneous outputs on such examples, there remains a considerable unmet need for deep learning systems that can better detect such infrequent conditions. These infrequent 'outlier' conditions are seen very rarely (or not at all) during training. In this paper, we frame this task as an out-of-distribution (OOD) detection problem. We set up a benchmark ensuring that outlier conditions are disjoint between the model training, validation, and test sets. Unlike traditional OOD detection benchmarks where the task is to detect dataset distribution shift, we aim at the more challenging task of detecting subtle differences resulting from a different pathology or condition. We propose a novel hierarchical outlier detection (HOD) loss, which assigns multiple abstention classes corresponding to each training outlier class and jointly performs a coarse classification of inliers vs. outliers, along with fine-grained classification of the individual classes. We demonstrate that the proposed HOD loss based approach outperforms leading methods that leverage outlier data during training. Further, performance is significantly boosted by using recent representation learning methods (BiT, SimCLR, MICLe). Further, we explore ensembling strategies for OOD detection and propose a diverse ensemble selection process for the best result. We also perform a subgroup analysis over conditions of varying risk levels and different skin types to investigate how OOD performance changes over each subgroup and demonstrate the gains of our framework in comparison to baseline. Furthermore, we go beyond traditional performance metrics and introduce a cost matrix for model trust analysis to approximate downstream clinical impact. We use this cost matrix to compare the proposed method against the baseline, thereby making a stronger case for its effectiveness in real-world scenarios.

Provision of a local anaesthetic minor procedures service by surgical advanced clinical practitioners: 5-year study

BACKGROUND

Surgical advanced clinical practitioners (SACPs) form part of the extended surgical workforce drawn from a variety of allied healthcare backgrounds. The primary aim of this study was to determine whether there was a financial benefit in having minor surgical procedures undertaken by dedicated SACPs compared with operating lists assigned to consultant surgeons.

METHODS

This was a retrospective cohort study including all patients who had minor 'lumps and bumps' procedures undertaken between April 2014 and August 2019 at East Lancashire Hospitals NHS Trust under local anaesthetic by the general surgery team. Clinical patient information, including lesion type, was collected along with operating room staffing levels and duration of operation. The cost of the procedure was calculated as operating time multiplied by cost of staff per minute according to local banding.

RESULTS

A total of 1399 patients had a lesion excised; 907 procedures were carried out by a doctor, and the rest independently by a SACP. The majority of lesions excised were lipomas and cysts. There was no difference in the median surgical time taken between SACPs and doctors (20 (i.q.r. 14-28) min). Minor procedures carried out on consultant surgeon lists cost 62.3 per cent (€25.33) more on average than those on SACP lists (median €65.96 versus 40.63 respectively; P < 0.001).

CONCLUSION

A dedicated and independent SACP 'lumps and bumps' list was financially beneficial. Operating times were similar to those of doctors. These lists safely free trainee and consultant surgeons to undertake more complex work.

A deep learning system for differential diagnosis of skin diseases

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions.

Use of a Novel Computerized Drill for Pedicle Screw Insertion in the Thoracic and Lumbar Spine: A Cadaveric Study

Background

A variety of techniques have been utilized to improve the accuracy of pedicle screw instrumentation. Recently, a novel handheld computerized drill system, IntelliSense Drill Technology, has been used in orthopedic trauma to improve drilling accuracy and reduce radiation and iatrogenic injury. The specialized drill technology detects changes in cortical density to prevent inadvertent cortical violation. The aim of this study is to assess the ability of this system to identify pedicle trajectories in the thoracic and lumbar spine compared to a standard freehand technique.

Methods

Two spine surgeons, including 1 senior-level and 1 junior-level attending drilled pedicle screw tracts using a freehand technique and computerized drill technology in 4 cadaveric spines from T2 to S1. A total of 134 pedicle screws were placed, including 70 by the senior surgeon and 64 by the junior surgeon. Cortical violations were assessed using computed tomography after instrumenting each pedicle tract, and procedure time for insertion of pedicle screws was recorded.

Results

A total of 15 (22.4%) and 12 (18.2%) pedicle violations were noted using the freehand and computerized drill technique, respectively (P = .767). Perforations using the computerized drill decreased from 31.1% in the first attempt to 5.9% in the second attempt (P = .027). Mean drill time per pedicle using the freehand and computerized drill techniques were comparable (12.2 ± 8.4 versus 12.1 ± 13.2, P = .871), and both surgeons had an improvement in procedure time using the computerized drill (surgeon 1: 12.3 ± 13.7-5.7 ± 3.8 [SD], P = .059; surgeon 2: 20.3 ± 20.0-10.4 ± 5.6 [SD], P = .063).

Conclusions

We demonstrate the use of a novel drill technology for placement of pedicle screws in the thoracic and lumbar spine. After an initial learning curve, this technology is comparable to use of a freehand technique by both a senior-level and a junior-level attending surgeon in a cadaveric model. Further investigation is needed to identify the clinical role of this technology in spine surgery.

Is There Value in Having Radiology Provide a Second Reading in Pediatric Orthopaedic Clinic?

BACKGROUND

The Joint Commission on Accreditation of Healthcare Organizations specifically mandates the dual interpretation of musculoskeletal radiographs by a radiologist in addition to the orthopaedist in all hospital-based orthopaedic clinics. Previous studies have questioned the utility of this practice. The purpose of this study was to further investigate the clinical significance of having the radiologist provide a second interpretation in a hospital-based pediatric orthopaedic clinic.

METHODS

A retrospective review was performed of all patients who had plain radiographs obtained in the pediatric orthopaedic clinic at an academic children's hospital over a 4-month period. For each radiographic series, the orthopaedist's note and the radiology interpretation were reviewed and a determination was made of whether the radiology read provided new clinically useful information and/or a new diagnosis, whether it recommended further imaging, or if it missed a diagnosis that was reflected in the orthopaedist's note. The hospital charges associated with the radiology read for each study were also quantified.

RESULTS

The charts of 1570 consecutive clinic patients who were seen in the pediatric orthopaedic clinic from January to April, 2012 were reviewed. There were 2509 radiographic studies performed, of which 2264 had both a documented orthopaedist's note and radiologist's read. The radiologist's interpretation added new, clinically important information in 1.0% (23/2264) of these studies. In 1.7% (38/2264) of the studies, it was determined that the radiologist missed the diagnosis or clinically important information that could affect treatment. The total amount of the professional fees charged for the radiologists' interpretations was $87,362. On average, the hospital charges for each occurrence in which the radiologist's read provided an additional diagnosis or clinically important information beyond the orthopaedist's note were $3798.

CONCLUSIONS

The results of this study suggest that eliminating the requirement to have the radiologist interpret radiographs in the pediatric orthopaedic clinic would have few clinical consequences.

LEVEL OF EVIDENCE

Level III-This is a diagnostic retrospective cohort study.

Marine Enzymes and Microorganisms for Bioethanol Production

Bioethanol is a potential alternative fuel to fossil fuels. Bioethanol as a fuel has several economic and environmental benefits. Though bioethanol is produced using starch and sugarcane juice, these materials are in conflict with food availability. To avoid food-fuel conflict, the second-generation bioethanol production by utilizing nonfood lignocellulosic materials has been extensively investigated. However, due to the complexity of lignocellulose architecture, the process is complicated and not economically competitive. The cultivation of lignocellulosic energy crops indirectly affects the food supplies by extensive land use. Marine algae have attracted attention to replace the lignocellulosic feedstock for bioethanol production, since the algae grow fast, do not use land, avoid food-fuel conflict and have several varieties to suit the cultivation environment. The composition of algae is not as complex as lignocellulose due to the absence of lignin, which renders easy hydrolysis of polysaccharides to fermentable sugars. Marine organisms also produce cold-active enzymes for hydrolysis of starch, cellulose, and algal polysaccharides, which can be employed in bioethanol process. Marine microoorganisms are also capable of fermenting sugars under high salt environment. Therefore, marine biocatalysts are promising for development of efficient processes for bioethanol production.

ID: 115: TRKA TRANSACTIVATION BY LYSOPHOSPHATIDIC ACID REGULATES LUNG EPITHELIAL CELL MIGRATION

Background

Lysophosphatidic acid (LPA) is a bioactive lysophospholipid, which plays a crucial role in cell proliferation, migration, and differentiation. LPA exerts its biological effects mainly through cell-surface LPA receptors (LPA1-6), which belong to the G protein-coupled receptor (GPCR) family. Recent studies suggest that cross-talk between receptor tyrosine kinases (RTKs) and GPCRs modulates GPCR-mediated signaling. TrkA receptor is a RTK, which mediates nerve growth factor (NGF)-induced biological functions including cell migration in neuronal and non-neuronal tissues.

Methods and Results

Here, we show that LPA treatment of the murine lung epithelial cell line (MLE-12 cells) induced tyrosine phosphorylation of TrkA in both time- and dose-dependent manners. These effects were attenuated by downregulating the LPA1 receptor. Furthermore, LPA induces interaction between LPA1 and TrkA. Co-immunoprecipitation experiment reveals that c-terminus of LPA1 contains the binding site for TrkA. Further, we found that LPA1 and phosphorylated TrkA were co-localized in both the plasma membrane and cytoplasm. Pretreatment with a TrkA inhibitor attenuated LPA-induced phosphorylation of TrkA receptor and LPA1 internalization as well as lung epithelial cell migration.

Conclusion

These studies reveal a molecular mechanism for the transactivation of TrkA by LPA, and suggest that the cross-talk between LPA1 and TrkA regulates LPA-induced receptor internalization and lung epithelial cell migration.

This work was supported by the National Institutes of Health (R01HL091916 and R01HL112791 to Y.Z, R01GM115389 to J.Z.), American Heart Association 12SDG9050005 (J.Z.), American Lung Association Biomedical Research Grant RG350146 (J.Z.).

ID: 123: ROLE OF MICRORNA-1 IN REGULATING PULMONARY VASCULAR REMODELING IN PULMONARY ARTERIAL HYPERTENSION

Rationale

Pulmonary arterial hypertension (PAH) is a severe, progressive disease characterized by increased pulmonary arterial pressure and resistance due in part to uncontrolled vascular remodeling. The mechanisms contributing to vascular remodeling in PAH are poorly understood and involve rampant pulmonary artery smooth muscle cell (PASMC) proliferation. We recently demonstrated the important role of sphingosine kinase 1 (SphK1), a lipid kinase producing pro-proliferative sphingosine-1-phosphate (S1P), in the development of pulmonary vascular remodeling in PAH. However, the regulatory processes involved in upregulation of SphK1 in this disease are unknown.

Objective

In this study, we aimed to identify novel molecular mechanisms governing the regulation of SphK1 expression, with a focus on microRNA (miR). Using both in vitro studies in pulmonary artery smooth muscle cells (PASMCs) and an in vivo mouse model of experimental hypoxia-mediated pulmonary hypertension (HPH), we explored the role of miR in controlling SphK1 expression in the development of pulmonary vascular remodeling.

Methods and Results

In silico analysis identified hsa-miR-1-3p (miR-1) as a candidate targeting SphK1. We demonstrate miR-1 is down-regulated by hypoxia in human PASMCs and in lung tissues of mice with HPH, coinciding with upregulation of SphK1 expression. PASMCs isolated from patients with PAH had significantly reduced expression of miR-1. Transfection of human PASMCs with miR-1 mimics significantly attenuated activity of a SphK1-3'-UTR luciferase reporter construct and SphK1 protein expression. miR-1 overexpression in human PASMCs also inhibited proliferation and migration under normoxic and hypoxic conditions, both important in pathogenic vascular remodeling in PAH. Finally, we demonstrated that intravenous administration of miR-1 mimics prevents the development of experimental HPH in mice and attenuates induction of SphK1 in PASMCs.

Conclusion

These data demonstrate that miR-1 expression in reduced in PASMCs from PAH patients, is modulated by hypoxia, and regulates the expression of SphK1. Key phenotypic aspects of vascular remodeling are influenced by miR-1 and its overexpression can prevent the development of HPH in mice. These studies further our understanding of the mechanisms underlying pathogenic pulmonary vascular remodeling in PAH and could lead to novel therapeutic targets.

Supported by grants NIH/NHLBI R01 HL127342 and R01 HL111656 to RFM, NIH/NHLBI P01 HL98050 and R01 HL127342 to VN, American Heart Association Predoctoral Fellowship (15PRE2190004) to JRS, and NIH/NLHBI NRSA F30 Fellowship (FHL128034A) to JRS.

ID: 112: ROLE OF PHOSPHOLIPASE D IN IDIOPATHIC PULMONARY FIBROSIS

Idiopathic pulmonary fibrosis (IPF) is a deadly interstitial disease that leads to scarring and fibrosis of the lung tissue. In pulmonary fibrosis, there is injury and denudation of the alveolar epithelium, which further leads to activation of fibroblasts which differentiate into myofibroblasts. This includes several mechanisms including epithelial to mesenchymal transition (EMT). In this study, we investigated the role of phospholipase D (PLD) in IPF and also its underlying mechanism like EMT and fibroblast proliferation and differentiation. An in vivo murine model of bleomycin-induced pulmonary fibrosis (PF) and in vitro models of murine alveolar type-II epithelial cells (MLE-12) and human lung fibroblasts were used. C57BL/6 and genetically engineered PLD2−/− mice were intratracheally challenged with bleomycin (1.5 U/kg animal) for 14 days and markers of inflammation, EMT and fibrosis were determined. MLE-12 cells were treated with specific PLD1 or PLD2 inhibitors prior to bleomycin (10 mU/ml) challenge, and the role of PLD in EMT and apoptosis of alveolar epithelial cells was studied. Human lung fibroblasts were serum-starved (3h), pretreated with PLD1 or PLD2 inhibitors, and the effect of TGF-β (5 ng/ml) on differentiation of lung fibroblast to myofibroblast was determined. Intra-tracheal instillation of bleomycin in the mice for 14 days leads to the progression of fibrosis in the lung. The lung tissues of the bleomycin treated mice were found to have increased PLD2 protein expression, myofibroblast markers like α-SMA, fibronectin, mesenchymal markers like vimentin, inflammatory cytokines and collagen. Genetic deletion of PLD2 in mice attenuated bleomycin-induced lung inflammation and pulmonary fibrosis. In vitro, MLE-12 cells pretreated with either PLD1 or PLD2 inhibitor did not show a profound reduction either in apoptosis or the expression of transcription factors such as SNAIL, and other markers of EMT. However, MLE-12 cells pretreated with both PLD1 (250 nM) and PLD2 (500 nM) inhibitors were resistant to bleomycin-induced apoptosis, and exhibited reduced expression of SNAIL and mesenchymal markers. On the contrary, human lung fibroblasts pretreated with PLD1 and PLD2 inhibitors showed increased fibroblast to myofibroblast differentiation mediated by TGF-β. The present study suggests a role for PLD2 in bleomycin-induced PF. In vitro, inhibition of both PLD1 and PLD2 was necessary to attenuate bleomycin-induced EMT in epithelial cells and TGF-β mediated differentiation of fibroblasts to myofibroblasts. The in vivo and in vitro results identify the mechanism by which PLD regualtes PF and suggest PLD as a potential therapeutic target in pulmonary fibrosis. This work was supported by National Institutes of Health grant P01 HL98050 to VN.

ID: 116: ADNIBISTRATION OF SPHINGOSINE KINASE 1 INHIBITOR POST-BLEOMYCIN CHALLENGE ATTENUATES BLEOMYCIN-INDUCED PULMONARY FIBROSIS IN MICE: ROLE OF YAP1

Rationale

We have earlier reported that sphingosine kinase 1 (SphK1) is up-regulated in lung tissues from idiopathic pulmonary fibrosis (IPF) patients and SphK1/sphingosine-1-phosphate (S1P) signaling axis plays a key role in bleomycin- and radiation-induced pulmonary fibrosis in mice. Further, SphK1 mRNA levels in PBMCs from IPF patients directly and significantly correlated with pulmonary function outcomes and overall survival. In this study, we investigated the effect of administration of PF-543, a specific SphK1 inhibitor, 7 days post-bleomycin (BLM) challenge on the development of pulmonary fibrosis in mice.

Objectives

To determine the efficacy of PF-543 in attenuating pulmonary fibrosis in mice post BLM challenge.

Methods

SphK1 inhibitor PF-543 (5 mg/kg body weight, i.p.; twice a week) was administered to control and BLM (1.5 U/kg, intratracheal) challenged mice on day 7 post BLM challenge, and development of pulmonary fibrosis was determined on day 21 by Mason-Trichrome staining of lung tissues, total collagen, fibronectin and α-SMA protein levels in lung tissues. In vitro, the effect of PF-543 on TGF-β (5 ng/ml) induced Yap1 activation, and differentiation, contraction, and proliferation of human lung fibroblast were assessed.

Results

Administration of PF-543 to BLM treated mice on day 7 post challenge, dramatically inhibited BLM-induced lung injury, and attenuated collagen deposition and expression of fibronectin, α-SMA and TGF-β in lung tissues on day 21 post-BLM challenge. In vitro, PF-543 inhibited TGF-β induced differentiation, contraction and proliferation of human lung fibroblasts, as well as blocked TGF-β induced expression and nuclear translocation of YAP, a transcriptional co-factor involved in pulmonary fibrosis. Additionally, siRNA knockdown of YAP1 inhibited TGF-β induced fibroblast differentiation.

Conclusion

These studies demonstrate ability of SphK1 inhibitor, PF-543 to ameliorate BLM-induced pulmonary fibrosis in mice when administered on day 7 after BLM challenge. Also, PF-543 blocked YAP1 activation, a co-transcriptional factor involved in pulmonary fibrosis development. Thus, PF-543 may serve as a potential therapeutic drug in treating pulmonary fibrosis. This work was supported by P01 HL098050 (VN).

ID: 111: THE S1P TRANSPORTER, SPNS2, MEDIATES HGF-INDUCED LAMELLIPODIA FORMATION AND MIGRATION OF HUMAN LUNG ENDOTHELIAL CELLS

Rationale

We have demonstrated earlier that HGF-induced lamellipodia formation in human lung microvascular endothelial cells (HLMVECs) was through c-Met receptor tyrosine kinase and PI3 kinase/Akt signal transduction. Here, we show that HGF-mediated lamellipodia formation is dependent on intracellular S1P generation mediated by sphingosine kinase 1 (SphK1), the S1P transporter, Spns2 and S1P1 in HLMVECs.

Methods

HLMVECs were treated with HGF (20 ng/ml) for different time points. Lamellipodia were detected after HGF treatment by immunofluorescent staining of Spns2, cortactin and actin in lamellipodia, and lamellipodia were quantified by measuring cell periphery fluorescence intensity. Pearson's correlation coefficient was used to statistically quantify co-localization of proteins in lamellipodia. Endogenous SphK activity was blocked by SphK1 specific inhibitor PF-543, and expression of SphK1 in cells was down-regulated by siRNA. Cellular S1P levels were quantified by mass spectrometry.

Results

HGF stimulated phosphorylation of SphK1, and its localization to lamellipodia of HLMVECs. Down-regulation of SphK1, but not SphK2, with siRNA or inhibition of SphK1 with PF-543 (1–5 µM) attenuated HGF-induced lamellipodia formation in HLMVECs. The HGF-mediated phosphorylation of SphK1 and its localization in lamellipodia was dependent on PI3K/Akt and ERK1/2 signaling apthways. HGF increased S1P levels in HLMVECs, which was blocked by inhibition of SphK1 with PF-543. Further, HGF induced serine phosphorylation and translocation of Spns2, the S1P transporter, to lamellipodia, which was Akt dependent. The HGF-induced lamellipodia formation in HLMVECs was blocked by down-regulation of Spns2, suggesting extracellular action of S1P in lamellipodia formation. Down-regulation of S1P1, but not S1P2 or S1P3, with siRNA attenuated HGF-induced lamellipodia formation. Further, HGF stimulation enhanced association of Spns2 with S1P1 and blocking SphK1 activty with PF-543 attenuated the association between Spns2 and S1P1. Additionally, HGF-induced migration of HLMVECs was attenuated by down-regulation of Spns2.

Conclusion

These results suggest that HGF/c-Met mediated lamellipodia formation and motility is dependent on intracellular generation of S1P via activation and localization of SphK1 to cell periphery and Spns2 mediated transport of S1P to outside for signaling via S1P1 in HLMVECs. This work was supported by NIH/HLBI P01 HL98050 to VN.

ID: 120: REGULATION OF ENDOTHELIAL BARRIER PERMEABILITY BY PHOSPHOLIPASE D2

Pulmonary edema is a hallmark of several diseases including acute respiratory distress syndrome (ARDS) and is characterized by the disruption of the pulmonary endothelial barrier at its early stage. Maintaining the integrity of the adherens junctions (AJs) by stabilizing VE-Cadherin (VEC) at the cell membrane after injury could potentially be important to minimize endothelial barrier disruption. Since Phospholipase D (PLD) and its catalytic product, phosphatidic acid (PA), has been shown to be critical in membrane trafficking and in recycling of a number of cell surface receptors, we hypothesized that PLD/PA pathway accelerates the rate of VEC recycling to the lamellipodia to reassemble the AJs. We demonstrate, by measuring the trans endothelial resistance of human lung microvascular endothelial cells (HLMVECs), that inhibiting PLD2-dependent PA production increases the endothelium permeability in response to thrombin. Furthermore, immunostaining shows that the uniform redistribution of VEC to the AJs post thrombin insult is compromised when PLD-dependent PA production is inhibited, and resulted in the appearance of eminent intercellular gaps. Also, PLD2 inhibition prevented the HLMVECs from fully spreading after thrombin stimulation while the protrusive activity remained unaffected, suggesting that PLD2 is not required by HLMVECs to send protrusions, but is critical for the adherence of the protrusions. LPS-induced lung injury was more severe in PLD2 knockout mice compared to WT in an in vivo ARDS model. These observations suggest that PLD/PA signaling plays an important role in resealing of endothelial gaps post LPS-induced lung injury and could potentially be therapeutically utilized to enhance post-injury endothelium recovery.

ID: 130: NOVEL ROLE FOR LYSOCARDIOLIPIN ACYLTRANSFERASE (LYCAT) IN NON-SMALL CELL LUNG CANCER

Lung cancer is the leading cause of deaths in United States and non-small cell lung cancer (NSCLC) accounts for ∼85% of all lung cancers with a 5-year survival rate of approximately ∼16%. Therefore there is an immediate need to develop new strategies for early detection and more effective treatments options. Mitochondrial dysfunction including but not limited to defects in mitochondrial genomics and dynamics has long been implicated to play a role in human health and disease particularly in cancer initiation, progression and treatment options since it plays a pivotal role in cell death and survival. Lysocardiolipin acyltransferase (LYCAT), a Cardiolipin remodeling enzyme regulating the 18:2 linoleic acid pattern of mammalian mitochondrial cardiolipin, plays a crucial role in maintaining normal mitochondrial function and vascular development. LYCAT was shown to be up-regulated in cancers; however, the role of LYCAT in lung cancer is yet unclear. Probing the protein expression of LYCAT in lung cancer specimens, non-transformed bronchial epithelial cell lines and 5 lung cancer cell lines revealed increased LYCAT expression and activity in all the lung cancer samples and cell lines tested in comparison to the control lung tissues and non-transformed epithelial cell lines. To determine the role of LYCAT in lung cancer, NSCLC cell lines H2122 and H23 were transfected with either scrambled or LYCAT shRNA and differences in serum-induced cell proliferation, migration, clonogenecity and mitochondrial dynamics were determined. Our results demonstrated that down-regulation of LYCAT by shRNA significantly attenuated cell migration, proliferation, and invasion in NSCLC cell lines compared to control cell lines. Furthermore knockdown of LYCAT expression in NSCLC cell lines inhibited mitochondrial fragmentation and enhanced mitochondrial fusion. Taken together, these data demonstrate a strong association between increased LYCAT expression and cell proliferation, motility, invasion and mitochondrial dynamics in NSCLC cells. Thus, development of targeted therapies to reduce LYCAT expression in NSCLC should be beneficial. This work in part was supported by funds from the College of Medicine, UIC and NIH HL98050 to VN.

Evaluation of terrestrial plants extracts for uranium sorption and characterization of potent phytoconstituents

Sorption capacity of four plants (Funaria hygrometrica, Musa acuminata, Brassica juncea and Helianthus annuus) extracts/fractions for uranium, a radionuclide was investigated by EDXRF and tracer studies. The maximum sorption capacity, i.e., 100% (complete sorption) was observed in case of Musa acuminata extract and fractions. Carbohydrate, proteins, phenolics and flavonoids contents in the active fraction (having maximum sorption capacity) were also determined. Further purification of the most active fraction provided three pure molecules, mannitol, sorbitol and oxo-linked potassium oxalate. The characterization of isolated molecules was achieved by using FTIR, NMR, GC-MS, MS-MS, and by single crystal-XRD analysis. Of three molecules, oxo-linked potassium oxalate was observed to have 100% sorption activity. Possible binding mechanism of active molecule with the uranyl cation has been purposed.

Effect of solvents on the bulk growth of 4-aminobenzophenone single crystals: a potential material for blue and green lasers

Although 4-aminobenzophenone (4-ABP) is the best derivative of benzophenone with 260 times higher second harmonic generation (SHG) efficiency than potassium dihydrogen phosphate (KDP), growth of high quality bulk crystal still remains a difficult task. In the present work, the effect of solvents on solubility and growth aspects of 4-ABP was investigated to grow inclusion free 4-ABP crystals. The growth processes were discussed based on solute-solvent interaction in two different growth media of ethyl acetate and ethanol. The growth rate and thereby solvent inclusions are relatively higher in ethyl acetate grown crystal than the crystal grown from ethanol. The structural, thermal and optical properties of 4-ABP crystals were studied. The enthalpy of 4-ABP melting process was estimated from differential thermal analysis. The optical transmission study shows that 4-ABP crystals grown from ethanol has high transparency compared to ethyl acetate grown sample due to solvent inclusion in the later crystal.

Structural characterization of titania by X-ray diffraction, photoacoustic, Raman spectroscopy and electron paramagnetic resonance spectroscopy

A titania mineral (obtained from East coast, Orissa, India) was investigated by X-ray diffraction (XRD), photoacoustic spectroscopy (PAS), Raman and Electron Paramagnetic Resonance (EPR) studies. XRD studies indicated the presence of rutile (91%) and anatase (9%) phases in the mineral. Raman investigation supported this information. Both rutile and anatase phases have tetragonal structure (rutile: space group P4(2)/mnm, a=4.5946(1) Å, c=2.9597(1) Å, V=62.48(1) (Å)(3), Z=2; anatase: space group I4(1)/amd, 3.7848(2) Å, 9.5098(11) Å, V=136.22(2) (Å)(3), Z=4). The deconvoluted PAS spectrum showed nine peaks around 335, 370, 415,485, 555, 605, 659, 690,730 and 785 nm and according to the ligand field theory, these peaks were attributed to the presence of V(4+), Cr(3+), Mn(4+) and Fe(3+) species. EPR studies revealed the presence of transition metal ions V(4+)(d(1)), Cr(3+)(d(3)), Mn(4+)(d(3)) and Fe(3+)(d(5)) at Ti(4+) sites. The EPR spectra are characterized by very large crystal filed splitting (D term) and orthorhombic distortion term (E term) for multiple electron system (s>1) suggesting that the transition metal ions substitute the Ti(4+) in the lattice which is situated in distorted octahedral coordination of oxygen. The possible reasons for observation of unusually large D and E term in the EPR spectra of transition metal ions (S=3/2 and 5/2) are discussed.

Synthesis of a flexible poly(chloroprene)/methyl red film dosimeter using an environment-benign shear compounding method

The paper reports synthesis of a new film dosimeter based on a solvent-free route. Methyl red (MR) dye was introduced into poly(chloroprene) (PC) in various concentrations. The films were intensely red with λmax ~515nm. The absorbance decreased linearly with absorbed radiation dose up to 30kGy without a significant change in λmax. Color coordinates of the films were also analyzed. Optical micrographs of the films showed no signs of inhomogeneous distribution of MR in the PC matrix, which was attributed to the polarity of PC. Radiation sensitivity, dose response linearity, effects of irradiation temperature and humidity, as well as the rate of fading, were also investigated.

Probing local site environments and distribution of manganese in SrZrO3:Mn; PL and EPR spectroscopy complimented by DFT calculations

In order to understand the local environment, valence state and distribution of manganese; a combined experimental and theoretical approach based on PL, EPR and DFT is reported for Sr_(1−x)Zr_(1−y)Mn_(x+y)O₃, also explaining the origin of various defect related emissions.

Intense red emitting monoclinic LaPO4:Eu3+ nanoparticles: host–dopant energy transfer dynamics and photoluminescence properties

Visible host emission and dynamics of host–dopant energy transfer in LaPO₄:Eu phosphor is investigated using PL and complimented by DFT.

The role of reaction conditions in the polymorphic control of Eu3+ doped YInO3: structure and size sensitive luminescence

Controlled synthetic conditions yielded YInO₃:Eu³⁺ in both C-type and hexagonal modifications which exhibited a tremendous bearing of size and local structure on Eu³⁺ luminescence, all in a single compound.

Design of Content Based Image Retrieval Scheme for Diabetic Retinopathy Images using Harmony Search Algorithm

Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Automated segmentation of blood vessel is vital for periodic screening and timely diagnosis. An attempt has been made to generate continuous retinal vasculature for the design of Content Based Image Retrieval (CBIR) application. The typical normal and abnormal retinal images are preprocessed to improve the vessel contrast. The blood vessels are segmented using evolutionary based Harmony Search Algorithm (HSA) combined with Otsu Multilevel Thresholding (MLT) method by best objective functions. The segmentation results are validated with corresponding ground truth images using binary similarity measures. The statistical, textural and structural features are obtained from the segmented images of normal and DR affected retina and are analyzed. CBIR in medical image retrieval applications are used to assist physicians in clinical decision-support techniques and research fields. A CBIR system is developed using HSA based Otsu MLT segmentation technique and the features obtained from the segmented images. Similarity matching is carried out between the features of query and database images using Euclidean Distance measure. Similar images are ranked and retrieved. The retrieval performance of CBIR system is evaluated in terms of precision and recall. The CBIR systems developed using HSA based Otsu MLT and conventional Otsu MLT methods are compared. The retrieval performance such as precision and recall are found to be 96% and 58% for CBIR system using HSA based Otsu MLT segmentation. This automated CBIR system could be recommended for use in computer assisted diagnosis for diabetic retinopathy screening.