Emerging trends in antimicrobial resistance in bloodstream infections: multicentric longitudinal study in India (2017-2022)

Background

Antimicrobial resistance (AMR) has escalated to pandemic levels, posing a significant global health threat. This study examines the patterns and trends of AMR in Bloodstream Infections (BSIs) across India, aiming to inform better surveillance and intervention strategies.

Methods

Six-year data from 21 tertiary care centers in the Indian Council of Medical Research's AMR Surveillance Network (IAMRSN) were retrospectively analyzed to estimate cluster-robust trends in resistance. Time-series analysis was used to discern lead/lag relationships between antibiotic pairs and the directional influence of resistance in community and hospital-acquired BSIs(CA/HA BSIs). A data-driven Bayesian network ensemble averaged over 301 bootstrap samples was modelled to uncover systemic associations between AMR and Sustainable Development Goals (SDGs).

Findings

Our findings indicate significant (p < 0.001) monthly increases in Imipenem and Meropenem resistance for Klebsiella, E. coli, and Acinetobacter BSIs. Importantly, Carbapenem resistance in HA-BSIs preceded that in CA-BSIs for Klebsiella and Acinetobacter (p < 0.05). At a national level, Cefotaxime resistance emerged as a potential early indicator for emerging Carbapenem resistance, proposing a novel surveillance marker. In Klebsiella BSIs, states with higher achievement of SDG3 goals showed lower Imipenem resistance. A model-based AMR scorecard is introduced for focused interventions and continuous monitoring.

Interpretation

The identified spatiotemporal trends and drug resistance associations offer critical insights for AMR surveillance aligning with WHO GLASS standards.The escalation of carbapenem resistance in BSIs demands vigilant monitoring and may be crucial for achieving SDGs by 2030. Implementing the proposed framework for data-driven evidence can help nations achieve proactive AMR surveillance.

Funding

No specific funding was received for this analysis.

Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study

Background

Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online.

Objective

This study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia.

Methods

We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories-emotions and influencing factors. Using cosine distance from selected seed words' embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks.

Results

Our findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41% to 39% in India. We also observed a significant change (P<.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42% of tweets coming from India and 45% of tweets from the United States represented the "vaccine_rollout" category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases.

Conclusions

By extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions.

Single-cell multiomics revealed the dynamics of antigen presentation, immune response and T cell activation in the COVID-19 positive and recovered individuals

Introduction

Despite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome.

Methods

By combining single-cell multi-omics (Whole Transcriptome Analysis plus Antibody-seq) and machine learning-based analysis, we aim to better understand the functional aspects of cellular and immunological heterogeneity in the COVID-19 positive, recovered and the healthy individuals.

Results

Based on single-cell transcriptome and surface marker study of 163,197 cells (124,726 cells after data QC) from the 33 individuals (healthy=4, COVID-19 positive=16, and COVID-19 recovered=13), we observed a reduced MHC Class-I-mediated antigen presentation and dysregulated MHC Class-II-mediated antigen presentation in the COVID-19 patients, with restoration of the process in the recovered individuals. B-cell maturation process was also impaired in the positive and the recovered individuals. Importantly, we discovered that a subset of the naive T-cells from the healthy individuals were absent from the recovered individuals, suggesting a post-infection inflammatory stage. Both COVID-19 positive patients and the recovered individuals exhibited a CD40-CD40LG-mediated inflammatory response in the monocytes and T-cell subsets. T-cells, NK-cells, and monocyte-mediated elevation of immunological, stress and antiviral responses were also seen in the COVID-19 positive and the recovered individuals, along with an abnormal T-cell activation, inflammatory response, and faster cellular transition of T cell subtypes in the COVID-19 patients. Importantly, above immune findings were used for a Bayesian network model, which significantly revealed FOS, CXCL8, IL1β, CST3, PSAP, CD45 and CD74 as COVID-19 severity predictors.

Discussion

In conclusion, COVID-19 recovered individuals exhibited a hyper-activated inflammatory response with the loss of B cell maturation, suggesting an impeded post-infection stage, necessitating further research to delineate the dynamic immune response associated with the COVID-19. To our knowledge this is first multi-omic study trying to understand the differential and dynamic immune response underlying the sample subtypes.

Early prediction of hypothermia in pediatric intensive care units using machine learning

Hypothermia is a life-threatening condition where the temperature of the body drops below 35°C and is a key source of concern in Intensive Care Units (ICUs). Early identification can help to nudge clinical management to initiate early interventions. Despite its importance, very few studies have focused on the early prediction of hypothermia. In this study, we aim to monitor and predict Hypothermia (30 min-4 h) ahead of its onset using machine learning (ML) models developed on physiological vitals and to prospectively validate the best performing model in the pediatric ICU. We developed and evaluated ML algorithms for the early prediction of hypothermia in a pediatric ICU. Sepsis advanced forecasting engine ICU Database (SafeICU) data resource is an in-house ICU source of data built in the Pediatric ICU at the All-India Institute of Medical Science (AIIMS), New Delhi. Each time-stamp at 1-min resolution was labeled for the presence of hypothermia to construct a retrospective cohort of pediatric patients in the SafeICU data resource. The training set consisted of windows of the length of 4.2 h with a lead time of 30 min-4 h from the onset of hypothermia. A set of 3,835 hand-engineered time-series features were calculated to capture physiological features from the time series. Features selection using the Boruta algorithm was performed to select the most important predictors of hypothermia. A battery of models such as gradient boosting machine, random forest, AdaBoost, and support vector machine (SVM) was evaluated utilizing five-fold test sets. The best-performing model was prospectively validated. A total of 148 patients with 193 ICU stays were eligible for the model development cohort. Of 3,939 features, 726 were statistically significant in the Boruta analysis for the prediction of Hypothermia. The gradient boosting model performed best with an Area Under the Receiver Operating Characteristic curve (AUROC) of 85% (SD = 1.6) and a precision of 59.2% (SD = 8.8) for a 30-min lead time before the onset of Hypothermia onset. As expected, the model showed a decline in model performance at higher lead times, such as AUROC of 77.2% (SD = 2.3) and precision of 41.34% (SD = 4.8) for 4 h ahead of Hypothermia onset. Our GBM(gradient boosting machine) model produced equal and superior results for the prospective validation, where an AUROC of 79.8% and a precision of 53% for a 30-min lead time before the onset of Hypothermia whereas an AUROC of 69.6% and a precision of 38.52% for a (30 min-4 h) lead time prospective validation of Hypothermia. Therefore, this work establishes a pipeline termed ThermoGnose for predicting hypothermia, a major complication in pediatric ICUs.

Heart rate variability during head-up tilt shows inter-individual differences among healthy individuals of extreme Prakriti types

Autonomic modulation is critical during various physiological activities, including orthostatic stimuli and primarily evaluated by heart rate variability (HRV). Orthostatic stress affects people differently suggesting the possibility of identification of predisposed groups to autonomic dysfunction-related disorders in a healthy state. One way to understand this kind of variability is by using Ayurvedic approach that classifies healthy individuals into Prakriti types based on clinical phenotypes. To this end, we explored the differential response to orthostatic stress in different Prakriti types using HRV. HRV was measured in 379 subjects(Vata = 97, Pitta = 68, Kapha = 68, and Mixed Prakriti = 146) from two geographical regions(Vadu and Delhi NCR) for 5 min supine (baseline), 3 min head-up-tilt (HUT) at 60°, and 5 min resupine. We observed that Kapha group had lower baseline HRV than other two groups, although not statistically significant. The relative change (%Δ1&2 ) in various HRV parameters in response to HUT was although minimal in Kapha group. Kapha also had significantly lower change in HR, LF (nu), HF (nu), and LF/HF than Pitta in response to HUT. The relative change (%Δ1 ) in HR and parasympathetic parameters (RMSSD, HF, SD1) was significantly greater in the Vata than in the Kapha. Thus, the low baseline and lower response to HUT in Kapha and the maximum drop in parasympathetic activity of Vata may indicate a predisposition to early autonomic dysfunction and associated conditions. It emphasizes the critical role of Prakriti-based phenotyping in stratifying the differential responses of cardiac autonomic modulation in various postures among healthy individuals across different populations.

Early Prediction of Hemodynamic Shock in Pediatric Intensive Care Units With Deep Learning on Thermal Videos

Shock is one of the major killers in intensive care units, and early interventions can potentially reverse it. In this study, we advance a noncontact thermal imaging modality for continuous monitoring of hemodynamic shock working on 1,03,936 frames from 406 videos recorded longitudinally upon 22 pediatric patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with the heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 h. Our models achieved the best area under the receiver operating characteristic curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 h, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline that can provide better care and save lives.

A call for citizen science in pandemic preparedness and response: beyond data collection

The COVID-19 pandemic has underlined the need to partner with the community in pandemic preparedness and response in order to enable trust-building among stakeholders, which is key in pandemic management. Citizen science, defined here as a practice of public participation and collaboration in all aspects of scientific research to increase knowledge and build trust with governments and researchers, is a crucial approach to promoting community engagement. By harnessing the potential of digitally enabled citizen science, one could translate data into accessible, comprehensible and actionable outputs at the population level. The application of citizen science in health has grown over the years, but most of these approaches remain at the level of participatory data collection. This narrative review examines citizen science approaches in participatory data generation, modelling and visualisation, and calls for truly participatory and co-creation approaches across all domains of pandemic preparedness and response. Further research is needed to identify approaches that optimally generate short-term and long-term value for communities participating in population health. Feasible, sustainable and contextualised citizen science approaches that meaningfully engage affected communities for the long-term will need to be inclusive of all populations and their cultures, comprehensive of all domains, digitally enabled and viewed as a key component to allow trust-building among the stakeholders. The impact of COVID-19 on people’s lives has created an opportune time to advance people’s agency in science, particularly in pandemic preparedness and response.

VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning

A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy, especially in countries with access inequities and temporal separation of hot-spots, might be an effective way of halting the disease spread. We approach this problem by proposing a novel pipeline VacSIM that dovetails Deep Reinforcement Learning models into a Contextual Bandits approach for optimizing the distribution of COVID-19 vaccine. Whereas the Reinforcement Learning models suggest better actions and rewards, Contextual Bandits allow online modifications that may need to be implemented on a day-to-day basis in the real world scenario. We evaluate this framework against a naive allocation approach of distributing vaccine proportional to the incidence of COVID-19 cases in five different States across India (Assam, Delhi, Jharkhand, Maharashtra and Nagaland) and demonstrate up to 9039 potential infections prevented and a significant increase in the efficacy of limiting the spread over a period of 45 days through the VacSIM approach. Our models and the platform are extensible to all states of India and potentially across the globe. We also propose novel evaluation strategies including standard compartmental model-based projections and a causality-preserving evaluation of our model. Since all models carry assumptions that may need to be tested in various contexts, we open source our model VacSIM and contribute a new reinforcement learning environment compatible with OpenAI gym to make it extensible for real-world applications across the globe. 2

Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning

The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases 2 months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.

COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.

A machine learning application for raising WASH awareness in the times of COVID-19 pandemic

The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot "Satya" increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.

Cortical and Subcortical Brain Area Atrophy in SCA1 and SCA2 Patients in India: The Structural MRI Underpinnings and Correlative Insight Among the Atrophy and Disease Attributes

Introduction

Genetically defined spinocerebellar ataxia (SCA) type 1 and 2 patients have differential clinical profile along with probable distinctive cortical and subcortical neurodegeneration. We compared the degree of brain atrophy in the two subtypes with their phenotypic and genotypic parameters.

Methods

MRI was performed using a 3T scanner (Philips, Achieva) to obtain 3D T1-weighted scans of the whole brain and analyzed by FreeSurfer (version 5.3 and 6 dev.) software. Genetically proven SCA1 (n = 18) and SCA2 (n = 25) patients with age-matched healthy controls (n = 8) were recruited. Clinical severity was assessed by the International Cooperative Ataxia Rating Scale (ICARS). To know the differential pattern of atrophy, the groups were compared using ANOVA/Kruskal-Wallis test and followed by correlation analysis with multiple corrections. Further, machine learning-based classification of SCA subtypes was carried out.

Result

We found (i) bilateral frontal, parietal, temporal, and occipital atrophy in SCA1 and SCA2 patients; (ii) reduced volume of cerebellum, regions of brain stem, basal ganglia along with the certain subcortical areas such as hippocampus, amygdala, thalamus, diencephalon, and corpus callosum in SCA1 and SCA2 subtypes; (iii) higher subcortical atrophy SCA2 than SCA1 (iv) correlation between brain atrophy and disease attributes; (v) differential predictive pattern of two SCA subtypes using machine learning approach.

Conclusion

The present study suggests that SCA1 and SCA2 do not differ in cortical thinning while a characteristic pattern of subcortical atrophy SCA2 > SCA1 is observed along with correlation of brain atrophy and disease attributes. This may provide the diagnostic guidance of MRI to SCA subtypes and differential therapies.

Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature with Unsupervised Word Embeddings and Machine Learning (Preprint)

Background

Evidence from peer-reviewed literature is the cornerstone for designing responses to global threats such as COVID-19. In massive and rapidly growing corpuses, such as COVID-19 publications, assimilating and synthesizing information is challenging. Leveraging a robust computational pipeline that evaluates multiple aspects, such as network topological features, communities, and their temporal trends, can make this process more efficient.

Objective

We aimed to show that new knowledge can be captured and tracked using the temporal change in the underlying unsupervised word embeddings of the literature. Further imminent themes can be predicted using machine learning on the evolving associations between words.

Methods

Frequently occurring medical entities were extracted from the abstracts of more than 150,000 COVID-19 articles published on the World Health Organization database, collected on a monthly interval starting from February 2020. Word embeddings trained on each month’s literature were used to construct networks of entities with cosine similarities as edge weights. Topological features of the subsequent month’s network were forecasted based on prior patterns, and new links were predicted using supervised machine learning. Community detection and alluvial diagrams were used to track biomedical themes that evolved over the months.

Results

We found that thromboembolic complications were detected as an emerging theme as early as August 2020. A shift toward the symptoms of long COVID complications was observed during March 2021, and neurological complications gained significance in June 2021. A prospective validation of the link prediction models achieved an area under the receiver operating characteristic curve of 0.87. Predictive modeling revealed predisposing conditions, symptoms, cross-infection, and neurological complications as dominant research themes in COVID-19 publications based on the patterns observed in previous months.

Conclusions

Machine learning–based prediction of emerging links can contribute toward steering research by capturing themes represented by groups of medical entities, based on patterns of semantic relationships over time.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

(UN)MASKED COVID-19 TRENDS FROM SOCIAL MEDIA

Background

The adoption of nonpharmaceutical interventions and their surveillance are critical for detecting and stopping possible transmission routes of COVID-19. A study of the effects of these interventions can help shape public health decisions. The efficacy of nonpharmaceutical interventions can be affected by public behaviors in events, such as protests. We examined mask use and mask fit in the United States, from social media images, especially during the Black Lives Matter (BLM) protests, representing the first large-scale public gatherings in the pandemic.

Objective

This study assessed the use and fit of face masks and social distancing in the United States and events of large physical gatherings through public social media images from 6 cities and BLM protests.

Methods

We collected and analyzed 2.04 million public social media images from New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis between February 1, 2020, and May 31, 2020. We evaluated correlations between online mask usage trends and COVID-19 cases. We looked for significant changes in mask use patterns and group posting around important policy decisions. For BLM protests, we analyzed 195,452 posts from New York and Minneapolis from May 25, 2020, to July 15, 2020. We looked at differences in adopting the preventive measures in the BLM protests through the mask fit score.

Results

The average percentage of group pictures dropped from 8.05% to 4.65% after the lockdown week. New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis observed increases of 5.0%, 7.4%, 7.4%, 6.5%, 5.6%, and 7.1%, respectively, in mask use between February 2020 and May 2020. Boston and Minneapolis observed significant increases of 3.0% and 7.4%, respectively, in mask use after the mask mandates. Differences of 6.2% and 8.3% were found in group pictures between BLM posts and non-BLM posts for New York City and Minneapolis, respectively. In contrast, the differences in the percentage of masked faces in group pictures between BLM and non-BLM posts were 29.0% and 20.1% for New York City and Minneapolis, respectively. Across protests, 35% of individuals wore a mask with a fit score greater than 80%.

Conclusions

The study found a significant drop in group posting when the stay-at-home laws were applied and a significant increase in mask use for 2 of 3 cities where masks were mandated. Although a positive trend toward mask use and social distancing was observed, a high percentage of posts showed disregard for the guidelines. BLM-related posts captured the lack of seriousness to safety measures, with a high percentage of group pictures and low mask fit scores. Thus, the methodology provides a directional indication of how government policies can be indirectly monitored through social media.

Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study

BACKGROUND

The COVID-19 pandemic has affected the health, economic, and social fabric of many nations worldwide. Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being.

OBJECTIVE

This study is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID-19 pandemic.

METHODS

In this study, we have used a survey of 17,764 adults in the United States from different age groups, genders, and socioeconomic statuses. Through initial statistical analysis and Bayesian network inference, we have identified key factors affecting mental health during the COVID-19 pandemic. Integrating Bayesian networks with classical machine learning approaches led to effective modeling of the level of mental health prevalence.

RESULTS

Overall, females were more stressed than males, and people in the age group 18-29 years were more vulnerable to anxiety than other age groups. Using the Bayesian network model, we found that people with a chronic mental illness were more prone to mental disorders during the COVID-19 pandemic. The new realities of working from home; homeschooling; and lack of communication with family, friends, and neighbors induces mental pressure. Financial assistance from social security helps in reducing mental stress during the COVID-19-generated economic crises. Finally, using supervised machine learning models, we predicted the most mentally vulnerable people with ~80% accuracy.

CONCLUSIONS

Multiple factors such as social isolation, digital communication, and working and schooling from home were identified as factors of mental illness during the COVID-19 pandemic. Regular in-person communication with friends and family, a healthy social life, and social security were key factors, and taking care of people with a history of mental disease appears to be even more important during this time.

Use of artificial intelligence based models for learning better policy for maternal and child health

More than 640,000 babies died of sepsis before they reach the age of one month in India in 2016. Despite a large number of government schemes aimed at reducing this rate, this number still remains high because of the complexity and interplay of factors involved. Finding an optimum policy and solutions to this problem needs learning from data. We integrated diverse sources of data and applied Bayesian Artificial Intelligence methods for learning to mitigate sepsis and adverse pregnancy outcomes in India. In this project, we created models that combine the robustness of ensemble averaged Baeysian Networks with decision learning and impact evaluation by using simulations and counterfactual reasoning respectively. We will demonstrate the process of learning these models and how these led us to infer the pivotal role of Water, Sanitation and Hygiene for reducing Adverse Pregnancy Outcome and neonatal sepsis in the population studied. We will also demonstrate the creation of explainable AI models for complex public health challenges and their deployment with wiseR, our in-house, open source platform for doing end-to-end Bayesian Decision Network learning.

Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine

The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice; the role of -omics technologies in Health Care; the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases; and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances.

Role of Impulse Oscillometry in Assessing Asthma Control in Children

BACKGROUND

Impulse oscillometry is an effort-independent technique of assessment of airway resistance and reactance, and can be performed in children unable to complete spirometry.

OBJECTIVE

To evaluate the utility of impulse oscillometry and spirometry for assessing asthma control in children.

STUDY DESIGN

Prospective cohort study.

PARTICIPANTS

Children aged 5-15 years, with mild to severe persistent asthma.

INTERVENTION

On each 3-monthly follow-up visit, clinical assessment, classification of control of asthma, impulse oscillometry and spirometry were performed.

OUTCOME

Utility of impulse oscillometry parameters [impedance (Z5), resistance (R5), reactance (X5) at 5 Hz, and R5-20 (resistance at 20Hz -5Hz) (% predicted), and area of reactance (AX, actual values)] and FEV1 (% predicted) to discriminate between controlled and uncontrolled asthma was assessed by receiver operating characteristic (ROC) curve. Association of FEV1 and impulse oscillometry parameters over time with controlled asthma was evaluated by generalized estimating equation model.

RESULTS

Number of visits in 256 children [mean (SD) age, 100 (41.6) mo; boys: 198 (77.3%)], where both impulse oscillometry and spirometry were performed was 2616; symptoms were controlled in 48.9% visits. Area under the curve for discrimination between controlled and uncontrolled asthma by FEV1, AX, R5-20, Z5, R5, and X5 were 0.58, 0.55, 0.55, 0.52, 0.52 and 0.52, respectively. FEV1 [OR (95% CI): 1.02 (1.01-1.03)] and AX [OR (95% CI): 0.88 (0.81-0.97)] measured over the duration of follow-up were significantly associated with controlled asthma.

CONCLUSIONS

Spirometry and impulse oscillometry parameters are comparable in ascertaining controlled asthma. Impulse oscillometry being less effort-dependent may be performed for monitoring control of childhood asthma, especially in younger children.

Predicting Hemodynamic Shock from Thermal Images using Machine Learning

Proactive detection of hemodynamic shock can prevent organ failure and save lives. Thermal imaging is a non-invasive, non-contact modality to capture body surface temperature with the potential to reveal underlying perfusion disturbance in shock. In this study, we automate early detection and prediction of shock using machine learning upon thermal images obtained in a pediatric intensive care unit of a tertiary care hospital. 539 images were recorded out of which 253 had concomitant measurement of continuous intra-arterial blood pressure, the gold standard for shock monitoring. Histogram of oriented gradient features were used for machine learning based region-of-interest segmentation that achieved 96% agreement with a human expert. The segmented center-to-periphery difference along with pulse rate was used in longitudinal prediction of shock at 0, 3, 6 and 12 hours using a generalized linear mixed-effects model. The model achieved a mean area under the receiver operating characteristic curve of 75% at 0 hours (classification), 77% at 3 hours (prediction) and 69% at 12 hours (prediction) respectively. Since hemodynamic shock associated with critical illness and infectious epidemics such as Dengue is often fatal, our model demonstrates an affordable, non-invasive, non-contact and tele-diagnostic decision support system for its reliable detection and prediction.

Target Oxygen Saturation Among Preterm Neonates on Supplemental Oxygen Therapy: A Quality Improvement Study

OBJECTIVE

To avoid excessive oxygen exposure and achieve target oxygen saturation (SpO2) within intended range of 88%-95% among preterm neonates on oxygen therapy.

METHODS

20 preterm neonates receiving supplemental oxygen in the first week of life were enrolled. The percentage of time per epoch (a consecutive time interval of 10 hours/day) spent by them within the target SpO2 range was measured in phase 1 followed by implementation of a unit policy on oxygen administration and targeting in phase 2. In phase 3, oxygen saturation histograms constructed from pulse-oximeter data were used as daily feedback to nurses and compliance with oxygen-targeting was measured again.

RESULTS

48 epochs in phase 1 and 69 in phase 3 were analyzed. The mean (SD) percent time spent within target SpO2 range increased from 65.9% (21.4) to 76.5% (12.6) (P=0.001).

CONCLUSION

Effective implementation of oxygen targeting policy and feedback using oxygen saturation histograms may improve compliance with oxygen targeting.

Stewarding antibiotic stewardship in intensive care units with Bayesian artificial intelligence

Emerging antimicrobial resistance (AMR) is a global threat to life. Injudicious use of antibiotics is the biggest driver of resistance evolution, creating selection pressures on micro-organisms. Intensive care units (ICUs) are the strongest contributors to this pressure, owing to high infection and antibiotic usage rates. Antimicrobial stewardship programs aim to control antibiotic use; however, these are mostly limited to descriptive statistics. Genomic analyses lie at the other extreme of the value-spectrum, and together these factors predispose to siloing of knowledge arising from AMR stewardship. In this study, we bridged the value-gap at a Pediatric ICU by creating Bayesian network (BN) artificial intelligence models with potential impacts on antibiotic stewardship. Methods, actionable insights and an interactive dashboard for BN analysis upon data observed over 3 years at the PICU are described. BNs have several desirable properties for reasoning from data, including interpretability, expert knowledge injection and quantitative inference. Our pipeline leverages best practices of enforcing statistical rigor through bootstrapping, ensemble averaging and Monte Carlo simulations. Competing, shared and independent drug resistances were discovered through the presence of network motifs in BNs. Inferences guided by these visual models are also discussed, such as increasing the sensitivity testing for chloramphenicol as a potential mechanism of avoiding ertapenem overuse in the PICU. Organism, tissue and temporal influences on drug co-resistances are also discussed. While the model represents inferences that are tailored to the site, BNs are excellent tools for building upon pre-learnt structures, hence the model and inferences were wrapped into an interactive dashboard not only deployed at the site, but also made openly available to the community via GitHub. Shared repositories of such models could be a viable alternative to raw-data sharing and could promote partnering, learning across sites and charting a joint course for antimicrobial stewardship programs in the race against AMR.

Predictors of long-term outcomes in patients with acute severe colitis: A northern Indian cohort study

BACKGROUND AND AIM

Knowledge of long-term outcomes following an index episode of acute severe colitis (ASC) can help informed decision making at a time of acute exacerbation especially when colectomy is an option. We aimed to identify long-term outcomes and their predictors after a first episode of ASC in a large North Indian cohort.

METHODS

Hospitalized patients satisfying Truelove and Witts' criteria under follow-up at a single center from January 2003 to December 2013 were included. Patients avoiding colectomy at index admission were categorized as complete (≤ 3 non bloody stool per day) or incomplete responders, based upon response to corticosteroids at day 7. Random Forest-based machine learning models were constructed to predict the long-term risk of colectomy or steroid dependence following an index episode of ASC.

RESULTS

Of 1731 patients with ulcerative colitis, 179 (10%) had an index episode of ASC. Nineteen (11%) patients underwent colectomy at index admission and 42 (26%) over a median follow-up of 56 (1-159) months. Hazard ratio for colectomy for incomplete responder was 3.6 (1.7-7.5, P = 0.001) compared with complete responder. Modeling based on four variables, response at day 7 of hospitalization, steroid use during the first year of diagnosis, longer disease duration before ASC, and number of extra-intestinal manifestations, was able to predict colectomy with an accuracy of 77%.

CONCLUSIONS

Disease behavior of ASC in India is similar to the West, with a third undergoing colectomy at 10 years. Clinical features, especially response at day 7 hospitalization for index ASC, can predict both colectomy and steroid dependence with reasonable accuracy.

Exhaled breath condensate metabolome clusters for endotype discovery in asthma

Background

Asthma is a complex, heterogeneous disorder with similar presenting symptoms but with varying underlying pathologies. Exhaled breath condensate (EBC) is a relatively unexplored matrix which reflects the signatures of respiratory epithelium, but is difficult to normalize for dilution.

Methods

Here we explored whether internally normalized global NMR spectrum patterns, combined with machine learning, could be useful for diagnostics or endotype discovery. Nuclear magnetic resonance (NMR) spectroscopy of EBC was performed in 89 asthmatic subjects from a prospective cohort and 20 healthy controls. A random forest classifier was built to differentiate between asthmatics and healthy controls. Clustering of the spectra was done using k-means to identify potential endotypes.

Results

NMR spectra of the EBC could differentiate between asthmatics and healthy controls with 80% sensitivity and 75% specificity. Unsupervised clustering within the asthma group resulted in three clusters (n = 41,11, and 9). Cluster 1 patients had lower long-term exacerbation scores, when compared with other two clusters. Cluster 3 patients had lower blood eosinophils and higher neutrophils, when compared with other two clusters with a strong family history of asthma.

Conclusion

Asthma clusters derived from NMR spectra of EBC show important clinical and chemical differences, suggesting this as a useful tool in asthma endotype-discovery.

Electronic supplementary material

The online version of this article (10.1186/s12967-017-1365-7) contains supplementary material, which is available to authorized users.

Symptoms and medical conditions in 204 912 patients visiting primary health-care practitioners in India: a 1-day point prevalence study (the POSEIDON study)

BACKGROUND

India has one of the highest disease burdens in the world. A better understanding of what ails India will help policy makers plan appropriate health-care services and infrastructure development, design medical education curricula, and identify health research priorities that are relevant to the needs of the country. The POSEIDON study aimed to record the prevalence of symptoms and medical conditions for which patients visit a primary health-care practitioner in India.

METHODS

We randomly selected 12 000 general practitioners, general physicians, and paediatricians from 880 cities and towns and invited them to record demographic details, symptoms, and medical conditions for every patient they saw on Feb 1, 2011. A further 1225 practitioners volunteered to participate and their responses were included. We did simple descriptive analyses of prevalence rates and used χ(2) tests to study comorbid associations. Through application of systems biology methods, we visualised inter-relations between organ involvement of diseases and symptoms and deciphered how these associations change with age and gender.

FINDINGS

We included responses from 7400 health-care practitioners, which represented data for 204 912 patients, who presented with 554 146 reasons for visit. Fever (35·5%) was the most common presenting symptom. More than half of all patients presented with respiratory symptoms across all age groups and regions of India. Other common presentations were digestive system symptoms (25%), circulatory symptoms (12·5%), skin complaints (9%), and endocrine disorders (6·6%). Hypertension (14·52%), obstructive airways diseases (14·51%), and upper respiratory tract infections (12·9%) were the most common diagnoses reported. Of note was that 21·4% of all patients with hypertension reported by the primary health-care practitioners were younger than 40 years. Anaemia was the fourth most common disease reported by these health-care practitioners and was most common in women of menstrual age living outside metro cities.

INTERPRETATION

The POSEIDON study provides insight into the reasons that patients visit primary health-care practitioners in India; our results highlight important social and medical challenges in the developing world.

FUNDING

Chest Research Foundation, Council of Scientific and Industrial Research-Institute of Genomics and Integrated Biology (CSIR-IGIB), and Cipla Ltd.

Immune Response to Dengue Virus Infection in Pediatric Patients in New Delhi, India--Association of Viremia, Inflammatory Mediators and Monocytes with Disease Severity

Dengue virus, a mosquito-borne flavivirus, is a causative agent for dengue infection, which manifests with symptoms ranging from mild fever to fatal dengue shock syndrome. The presence of four serotypes, against which immune cross-protection is short-lived and serotype cross-reactive antibodies that might enhance infection, pose a challenge to further investigate the role of virus and immune response in pathogenesis. We evaluated the viral and immunological factors that correlate with severe dengue disease in a cohort of pediatric dengue patients in New Delhi. Severe dengue disease was observed in both primary and secondary infections. Viral load had no association with disease severity but high viral load correlated with prolonged thrombocytopenia and delayed recovery. Severe dengue cases had low Th1 cytokines and a concurrent increase in the inflammatory mediators such as IL-6, IL-8 and IL-10. A transient increase in CD14⁺CD16⁺ intermediate monocytes was observed early in infection. Sorting of monocytes from dengue patient peripheral blood mononuclear cells revealed that it is the CD14+ cells, but not the CD16+ or the T or B cells, that were infected with dengue virus and were major producers of IL-10. Using the Boruta algorithm, reduced interferon-α levels and enhanced aforementioned pro-inflammatory cytokines were identified as some of the distinctive markers of severe dengue. Furthermore, the reduction in the levels of IL-8 and IL-10 were identified as the most significant markers of recovery from severe disease. Our results provide further insights into the immune response of children to primary and secondary dengue infection and help us to understand the complex interplay between the intrinsic factors in dengue pathogenesis.

Dengue virus is a human pathogen that causes dengue fever, which can either resolve after mild fever or lead to severe dengue hemorrhagic fever/dengue shock syndrome. The role of dengue virus levels in the blood and the kinetics of infection and immune response that results in severe dengue disease in humans is not well characterized. In this study, we analyzed 97 children with varying degrees of dengue disease, and we show that the dengue virus quantity in blood does not show any significant association with severe disease. However, most severe dengue patients had lower levels of interferons and Th1 cytokines and increased levels of secreted factors such as IL-6, IL-8 and IL-10 that could potentially cause leakage in blood capillaries. Our results indicate that monocytes, which are infected with dengue virus in patients, could possibly play a major role in dengue pathogenesis. Furthermore, using computational analysis we identified association of some of the secreted factors with severe disease and also predicted the markers that could serve as indicators of recovery from severe dengue.

A comparative evaluation of the change in hardness, of two commonly used maxillofacial prosthetic silicone elastomers, as subjected to simulated weathering in tropical climatic conditions

Silicone elastomers have become the materials of choice for fabrication of facial prostheses. However such prostheses need periodic replacement due to the degradation of their physical properties due to weathering of polymers. The effect of environmental factors, disinfection solutions and skin secretions on weathering of silicones has been reported. However, the literature does not report on the comparative evaluation on the change in hardness of two commonly used maxillofacial prosthetic silicone elastomers, cured by different techniques and subjected to tropical climatic conditions. This study provides improved insight and understanding into the behavior of such materials for better material selection and treatment results.

Advances in the prevention and treatment of lung cancer

Lung cancer remains the most common fatal malignancy in the Western world. Survival rates have only improved modestly over the past three decades and new approaches are urgently required. It is clear that a concerted effort to reduce cigarette smoking is required. However, about 10% of patients with lung cancer are never smokers, indicating genetic or other predisposition. Lung cancer screening programmes are being trialled to target high-risk populations. Genetic strategies will provide new methods for screening and predicting response to treatment. Current therapy for lung cancer has reached a plateau and novel agents have shown modest clinical efficacy. Understanding the mechanisms by which chronic inflammatory disorders such as chronic obstructive pulmonary disease contribute to lung cancer development will help to identify new biological targets and biomarkers of early disease. This review focuses on recent advances in lung cancer prevention and treatment.

Structure and function of the tuberculous lung: considerations for inhaled therapies

Inhaled therapies for pulmonary tuberculosis are in development and appear promising at first look. A fundamental premise of such therapy is efficient delivery of drug at high concentrations to the active disease site, while minimizing systemic delivery. This assumes that inhaled drug will actually reach the diseased lung, which while intuitive for healthy lungs, may be untrue for diseased lungs with abnormal structure or function. This review discusses the structural and functional aspects of respiratory physiology that are likely to impact local drug delivery and presents the available evidence on how this pertains to tuberculous lungs.

Bronchial Carcinoid Tumour in Pregnancy

Summary

Breathlessness is a commonly encountered symptom in pregnancy. The differential diagnosis for acute breathlessness in pregnancy is broad and possibilities range from physiological changes and psychological causes to acute respiratory disorders, which include pulmonary embolism (PE), acute asthma, infection and pneumothorax. It is rare to find a malignancy as the underlying cause of breathlessness. We report a case of severe breathlessness and chest pain in a 26-year-old woman presenting at 36 weeks gestation. A chest radiograph was normal but a computertomogram (CT) identified a mass, which was subsequently demonstrated to be a malignant bronchial carcinoid tumour. Although abdominal carcinoid tumours have been reported in the literature, to our knowledge, this is the first reported pulmonary carcinoid tumour, presenting during pregnancy.

Extracellular matrix regulation of drug resistance in small-cell lung cancer

PURPOSE

Lung cancer is the leading cause of cancer deaths in the developed world. Small cell lung cancer (SCLC) has the worst prognosis due to the emergence of resistance to chemotherapy. This article will review recent work that has defined mechanisms of chemo-resistance focusing on the role of integrins.

RESULTS

SCLC is surrounded by an extensive stroma of extracellular matrix (ECM) and high levels of expression correlate with poor prognosis. ECM protects SCLC cells against chemotherapy-induced cell death by activating beta1 integrins leading to activation of phosphoinositide-3-OH kinase (PI3-kinase), which prevents etoposide-induced caspase-3 activation and subsequent apoptosis. Engagement of ECM prevents etoposide and radiation induced G2/M cell cycle arrest in SCLC cells by blocking the up-regulation of p21Cip1/WAF1 and p27Kip1 and the down-regulation of cyclins E, A and B. These effects are abrogated by pharmacological and genetic inhibition of PI3-kinase signalling.

CONCLUSIONS

Thus, ECM via beta1 integrin-mediated PI3-kinase activation allows SCLC cells to survive treatment induced cell cycle arrest and apoptosis with persistent DNA damage, providing a model to account for the emergence of acquired drug resistance. Novel therapeutic strategies may therefore be directed at inhibiting integrin-mediated cell survival signals improving response rates and cure in this devastating cancer.

ECM overrides DNA damage-induced cell cycle arrest and apoptosis in small-cell lung cancer cells through β1 integrin-dependent activation of PI3-kinase

The emergence of resistance to chemotherapy remains a principle problem in the treatment of small-cell lung cancer (SCLC). We demonstrate that extracellular matrix (ECM) activates phosphatidyl inositol 3-kinase (PI3-kinase) signaling in SCLC cells and prevents etoposide-induced caspase-3 activation and subsequent apoptosis in a beta1 integrin/PI3-kinase-dependent manner. Crucially we show that etoposide and radiation induce G2/M cell cycle arrest in SCLC cells prior to apoptosis and that ECM prevents this by overriding the upregulation of p21(Cip1/WAF1) and p27(Kip1) and the downregulation of cyclins E, A and B. These effects are abrogated by pharmacological and genetic inhibition of PI3-kinase signaling. Importantly we show that chemoprotection is not mediated by altered SCLC cell proliferation or DNA repair. Thus, ECM via beta1 integrin-mediated PI3-kinase activation overrides treatment-induced cell cycle arrest and apoptosis, allowing SCLC cells to survive with persistent DNA damage, providing a model to account for the emergence of acquired drug resistance.

Expression of V1A and GRP receptors leads to cellular transformation and increased sensitivity to substance-P analogue-induced growth inhibition

Small-cell lung cancer (SCLC) is a particularly aggressive cancer, which metastasises early. Despite initial sensitivity to radio- and chemo-therapy, it invariably relapses, so that the 2-year survival remains less than 5%. Neuropeptides particularly arginine vasopressin (AVP) and gastrin-releasing peptide (GRP) act as autocrine and paracrine growth factors and the expression of these and their receptors are a hallmark of the disease. Substance-P analogues including [D-Arg1,D-Phe5,D-Trp7,9,Leu11]-substance-P (SP-D) and [Arg6,D-Trp7,9,NmePhe8]-substance-P (6-11) (SP-G) inhibit the growth of SCLC cells by modulating neuropeptide signalling. We show that GRP and V1A receptors expression leads to the development of a transformed phenotype. Addition of neuropeptide provides some protection from etoposide-induced cytotoxicity. Receptor expression also leads to an increased sensitivity to substance-P analogue-induced growth inhibition. We show that SP-D and SP-G act as biased agonists at GRP and V1A receptors causing blockade of Gq-mediated Ca2+ release while directing signalling to activate ERK via a pertussis toxin-sensitive pathway. This is the first description of biased agonism at V1A receptors. This unique pharmacology governs the antiproliferative properties of these agents and highlights their potential therapeutic potential for the treatment of SCLC and particularly in tumours, which have developed resistance to chemotherapy.

Galectin-3: differential expression between small-cell and non-small-cell lung cancer

AIMS

To compare the histological expression of galectin-3 in different lung cancers, including small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). Lung cancer is the leading cause of cancer deaths in the UK. Galectin-3 is a beta-galactoside binding protein with a controversial role in malignant transformation. SCLC metastasizes early and is initially chemosensitive; NSCLC metastasizes later, offering the chance of surgical cure, but is much less chemosensitive. Mixed tumours present a diagnostic and therapeutic problem, with a poorer response to therapy. Insight into the cellular mechanisms that govern metastasis and chemoresistance will profoundly influence the future management of this disease.

METHODS AND RESULTS

In this study the histological expression of galectin-3 was assessed in a panel of lung tumour specimens, using the indirect streptavidin-biotin method. A striking difference in galectin-3 expression was observed between tumours, with high expression in NSCLC (42/47 samples) and low expression in SCLC (negative in 13/18, weak in 5/18).

CONCLUSION

This differential expression of galectin-3 between histological types of lung carcinoma suggests that galectin-3 may have an important influence on tumour cell adhesion, apoptosis and the response of tumours to chemotherapy.

Increased gastrin-releasing peptide (GRP) receptor expression in tumour cells confers sensitivity to [Arg6,D-Trp7,9,NmePhe8]-substance P (6-11)-induced growth inhibition

[Arg(6),D-Trp(7,9),N(me)Phe(8)]-substance P (6-11) (SP-G) is a novel anticancer agent that has recently completed phase I clinical trials. SP-G inhibits mitogenic neuropeptide signal transduction and small cell lung cancer (SCLC) cell growth in vitro and in vivo. Using the SCLC cell line series GLC14, 16 and 19, derived from a single patient during the clinical course of their disease and the development of chemoresistance, it is shown that there was an increase in responsiveness to neuropeptides. This was paralleled by an increased sensitivity to SP-G. In a selected panel of tumour cell lines (SCLC, non-SCLC, ovarian, colorectal and pancreatic), the expression of the mitogenic neuropeptide receptors for vasopressin, gastrin-releasing peptide (GRP), bradykinin and gastrin was examined, and their sensitivity to SP-G tested in vitro and in vivo. The tumour cell lines displayed a range of sensitivity to SP-G (IC(50) values from 10.5 to 119 microM). The expression of the GRP receptor measured by reverse transcriptase-polymerase chain reaction, correlated significantly with growth inhibition by SP-G. Moreover, introduction of the GRP receptor into rat-1A fibroblasts markedly increased their sensitivity to SP-G. The measurement of receptor expression from biopsy samples by polymerase chain reaction could provide a suitable diagnostic test to predict efficacy to SP-G clinically. This strategy would be of potential benefit in neuropeptide receptor-expressing tumours in addition to SCLC, and in tumours that are relatively resistant to conventional chemotherapy.