Packaging and containerization of computational methods.

Methods for analyzing the full complement of a biomolecule type, e.g., proteomics or metabolomics, generate large amounts of complex data. The software tools used to analyze omics data have reshaped the landscape of modern biology and become an essential component of biomedical research. These tools are themselves quite complex and often require the installation of other supporting software, libraries and/or databases. A researcher may also be using multiple different tools that require different versions of the same supporting materials. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging and containerization are different approaches to satisfy this need by delivering omics tools already wrapped in additional software that makes the tools easier to install and use. In this systematic review, we describe and compare the features of prominent packaging and containerization platforms. We outline the challenges, advantages and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers and system administrators. We also propose principles to make the distribution of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.

An optimized CT-dense agent perfusion and micro-CT imaging protocol for chick embryo developmental stages.

Compared to classical techniques of morphological analysis, micro-CT (µ-CT) has become an effective approach allowing rapid screening of morphological changes. In the present work, we aimed to provide an optimized micro-CT dense agent perfusion protocol and µ-CT guidelines for different stages of chick embryo cardiogenesis. Our study was conducted over a period of 10 embryonic days (Hamburger-Hamilton HH36) in chick embryo hearts. During the perfusion of the micro-CT dense agent at different developmental stages (HH19, HH24, HH27, HH29, HH31, HH34, HH35, and HH36), we demonstrated that durations and volumes of the injected contrast agent gradually increased with the heart developmental stages contrary to the flow rate that was unchanged during the whole experiment. Analysis of the CT imaging confirmed the efficiency of the optimized parameters of the heart perfusion.

RUBICON: a framework for designing efficient deep learning-based genomic basecallers.

Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later steps in genome analysis. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. We present RUBICON, a framework to develop efficient hardware-optimized basecallers. We demonstrate the effectiveness of RUBICON by developing RUBICALL, the first hardware-optimized mixed-precision basecaller that performs efficient basecalling, outperforming the state-of-the-art basecallers. We believe RUBICON offers a promising path to develop future hardware-optimized basecallers.

Determining factors affecting the acceptability of spirometry: A survey study in a tertiary chest diseases center.

Introduction: Unlike other laboratory tests, spirometry requires the participant's full compliance with the maneuvers in the test for an acceptable test result. In this study, we aimed to determine the suitability of spirometric tests regarding acceptability and the factors associated with acceptability. Materials and Methods: Before the test, our 15-scale questionnaire, prepared by us in the respiratory function laboratory, was applied to the participants who requested spirometric examination in our hospital. Afterwards, patients were subjected to spirometric analysis. Spirogram results of the participants were evaluated by four clinicians who were experts in the field based on the acceptability criteria in the American Thoracic Society and European Respiratory Society Spirometry Standardization Guidelines. Participants were divided into two groups as those who met the acceptability criteria and those who did not. Both groups were compared regarding demographic data, comorbidities, education levels, and questions in the questionnaire. Results: The acceptability spirometry rate was 71.2%. The most common error among those who could not perform an acceptable test was the inability to complete the expiratory time to the time that would create a plateau, with 37.3%. Education level and acceptability of spirometry were not related (p= 0.228). Asthma was statistically significantly higher in the group that performed acceptable spirometry (p= 0.049). Acceptable spirometry rate was statistically significantly higher in the participants who had previously performed spirometric tests compared to the other group (p< 0.001). The test success of the participants who did not have success anxiety about the test was significantly higher than the other group (p= 0.033). Conclusion: Reduction of participants' anxiety and repetitive testing increases test acceptability. For this reason, in our clinical practice, we recommend that people who want a spirometry test relieve their anxiety about the test and repeat the test in unacceptable tests.

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes.

Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.

Tuning Privacy-Utility Tradeoff in Genomic Studies Using Selective SNP Hiding.

Researchers need a rich trove of genomic datasets that they can leverage to gain a better understanding of the genetic basis of the human genome and identify associations between phenol-types and specific parts of DNA. However, sharing genomic datasets that include sensitive genetic or medical information of individuals can lead to serious privacy-related consequences if data lands in the wrong hands. Restricting access to genomic datasets is one solution, but this greatly reduces their usefulness for research purposes. To allow sharing of genomic datasets while addressing these privacy concerns, several studies propose privacy-preserving mechanisms for data sharing. Differential privacy is one of such mechanisms that formalize rigorous mathematical foundations to provide privacy guarantees while sharing aggregated statistical information about a dataset. Nevertheless, it has been shown that the original privacy guarantees of DP-based solutions degrade when there are dependent tuples in the dataset, which is a common scenario for genomic datasets (due to the existence of family members). In this work, we introduce a new mechanism to mitigate the vulnerabilities of the inference attacks on differentially private query results from genomic datasets including dependent tuples. We propose a utility-maximizing and privacy-preserving approach for sharing statistics by hiding selective SNPs of the family members as they participate in a genomic dataset. By evaluating our mechanism on a real-world genomic dataset, we empirically demonstrate that our proposed mechanism can achieve up to 40% better privacy than state-of-the-art DP-based solutions, while near-optimally minimizing utility loss.

RawHash: enabling fast and accurate real-time analysis of raw nanopore signals for large genomes.

Nanopore sequencers generate electrical raw signals in real-time while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opportunity for real-time genome analysis. An important feature of nanopore sequencing, Read Until, can eject strands from sequencers without fully sequencing them, which provides opportunities to computationally reduce the sequencing time and cost. However, existing works utilizing Read Until either (i) require powerful computational resources that may not be available for portable sequencers or (ii) lack scalability for large genomes, rendering them inaccurate or ineffective. We propose RawHash, the first mechanism that can accurately and efficiently perform real-time analysis of nanopore raw signals for large genomes using a hash-based similarity search. To enable this, RawHash ensures the signals corresponding to the same DNA content lead to the same hash value, regardless of the slight variations in these signals. RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value. We evaluate RawHash on three applications: (i) read mapping, (ii) relative abundance estimation, and (iii) contamination analysis. Our evaluations show that RawHash is the only tool that can provide high accuracy and high throughput for analyzing large genomes in real-time. When compared to the state-of-the-art techniques, UNCALLED and Sigmap, RawHash provides (i) 25.8× and 3.4× better average throughput and (ii) significantly better accuracy for large genomes, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash.

The association between glycemia and clinical outcomes in patients with diabetes mellitus and pulmonary thromboembolism

ABSTRACT Objective: Various studies have shown that diabetes mellitus (DM) increases the risk of thrombosis in the venous system as well as in the arterial system. In this study, it was aimed to evaluate the association between admission blood glucose levels and clinical severity, recurrence, and mortality in pulmonary embolism in patients with DM. Materials and methods: This study was designed as a retrospective cross-sectional study. Patients with DM who were admitted to a tertiary care hospital due to pulmonary embolism (PE) between 2014 and 2019 were included. Demographic characteristics, radiological findings, clinical class of embolism, and mortality data were retrieved from hospital records. Patients with and without recurrent disease, as well as patients who survived and died, were compared. Also, patients were classified according to quartiles of admission blood glucose levels. The quartiles were compared in terms of mortality, clinical, class, and recurrence. Results: Two hundred ninety-three patients with DM and PE were included in the study. Patients with adverse outcome had significantly higher admission blood glucose levels (respectively, 197.9 ± 96.30 mg/dL vs. 170.7 ± 74.26 mg/dL; p = 0.03). Patients in the third and fourth quartile of admission blood glucose levels (>152 mg/dL) had significantly more severe disease with a higher proportion of massive and sub-massive PE and higher pro-BNP levels (respectively, p = 0.01 and 0.02). Conclusion: Non-survived patients and recurrent disease were associated with higher admission blood glucose levels. Also, patients with admission blood glucose levels higher than 152 mg/dL tend to have clinically more severe diseases.

Experimental assessment of cardiovascular physiology in the chick embryo.

High resolution assessment of cardiac functional parameters is crucial in translational animal research. The chick embryo is a historically well-used in vivo model for cardiovascular research due to its many practical advantages, and the conserved form and function of the chick and human cardiogenesis programs. This review aims to provide an overview of several different technical approaches for chick embryo cardiac assessment. Doppler echocardiography, optical coherence tomography, micromagnetic resonance imaging, microparticle image velocimetry, real-time pressure monitoring, and associated issues with the techniques will be discussed. Alongside this discussion, we also highlight recent advances in cardiac function measurements in chick embryos.

A framework for high-throughput sequence alignment using real processing-in-memory systems.

MOTIVATION: Sequence alignment is a memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory (PIM) architectures alleviate this bottleneck by providing the memory with computing competencies. We propose Alignment-in-Memory (AIM), a framework for high-throughput sequence alignment using PIM, and evaluate it on UPMEM, the first publicly available general-purpose programmable PIM system. RESULTS: Our evaluation shows that a real PIM system can substantially outperform server-grade multi-threaded CPU systems running at full-scale when performing sequence alignment for a variety of algorithms, read lengths, and edit distance thresholds. We hope that our findings inspire more work on creating and accelerating bioinformatics algorithms for such real PIM systems. AVAILABILITY AND IMPLEMENTATION: Our code is available at https://github.com/safaad/aim.

Scrooge: a fast and memory-frugal genomic sequence aligner for CPUs, GPUs, and ASICs.

MOTIVATION: Pairwise sequence alignment is a very time-consuming step in common bioinformatics pipelines. Speeding up this step requires heuristics, efficient implementations, and/or hardware acceleration. A promising candidate for all of the above is the recently proposed GenASM algorithm. We identify and address three inefficiencies in the GenASM algorithm: it has a high amount of data movement, a large memory footprint, and does some unnecessary work. RESULTS: We propose Scrooge, a fast and memory-frugal genomic sequence aligner. Scrooge includes three novel algorithmic improvements which reduce the data movement, memory footprint, and the number of operations in the GenASM algorithm. We provide efficient open-source implementations of the Scrooge algorithm for CPUs and GPUs, which demonstrate the significant benefits of our algorithmic improvements. For long reads, the CPU version of Scrooge achieves a 20.1×, 1.7×, and 2.1× speedup over KSW2, Edlib, and a CPU implementation of GenASM, respectively. The GPU version of Scrooge achieves a 4.0×, 80.4×, 6.8×, 12.6×, and 5.9× speedup over the CPU version of Scrooge, KSW2, Edlib, Darwin-GPU, and a GPU implementation of GenASM, respectively. We estimate an ASIC implementation of Scrooge to use 3.6× less chip area and 2.1× less power than a GenASM ASIC while maintaining the same throughput. Further, we systematically analyze the throughput and accuracy behavior of GenASM and Scrooge under various configurations. As the best configuration of Scrooge depends on the computing platform, we make several observations that can help guide future implementations of Scrooge. AVAILABILITY AND IMPLEMENTATION: https://github.com/CMU-SAFARI/Scrooge.

How does hemodynamics affect rupture tissue mechanics in abdominal aortic aneurysm: Focus on wall shear stress derived parameters, time-averaged wall shear stress, oscillatory shear index, endothelial cell activation potential, and relative residence time.

An abdominal aortic aneurysm (AAA) is a critical health condition with a risk of rupture, where the diameter of the aorta enlarges more than 50% of its normal diameter. The incidence rate of AAA has increased worldwide. Currently, about three out of every 100,000 people have aortic diseases. The diameter and geometry of AAAs influence the hemodynamic forces exerted on the arterial wall. Therefore, a reliable assessment of hemodynamics is crucial for predicting the rupture risk. Wall shear stress (WSS) is an important metric to define the level of the frictional force on the AAA wall. Excessive levels of WSS deteriorate the remodeling mechanism of the arteries and lead to abnormal conditions. At this point, WSS-related hemodynamic parameters, such as time-averaged WSS (TAWSS), oscillatory shear index (OSI), endothelial cell activation potential (ECAP), and relative residence time (RRT) provide important information to evaluate the shear environment on the AAA wall in detail. Calculation of these parameters is not straightforward and requires a physical understanding of what they represent. In addition, computational fluid dynamics (CFD) solvers do not readily calculate these parameters when hemodynamics is simulated. This review aims to explain the WSS-derived parameters focusing on how these represent different characteristics of disturbed hemodynamics. A representative case is presented for spatial and temporal formulation that would be useful for interested researchers for practical calculations. Finally, recent hemodynamics investigations relating WSS-related parameters with AAA rupture risk assessment are presented. This review will be useful to understand the physical representation of WSS-related parameters in cardiovascular flows and how they can be calculated practically for AAA investigations.

The association between glycemia and clinical outcomes in patients with diabetes mellitus and pulmonary thromboembolism.

Objective: Various studies have shown that diabetes mellitus (DM) increases the risk of thrombosis in the venous system as well as in the arterial system. In this study, it was aimed to evaluate the association between admission blood glucose levels and clinical severity, recurrence, and mortality in pulmonary embolism in patients with DM. Materials and methods: This study was designed as a retrospective cross-sectional study. Patients with DM who were admitted to a tertiary care hospital due to pulmonary embolism (PE) between 2014 and 2019 were included. Demographic characteristics, radiological findings, clinical class of embolism, and mortality data were retrieved from hospital records. Patients with and without recurrent disease, as well as patients who survived and died, were compared. Also, patients were classified according to quartiles of admission blood glucose levels. The quartiles were compared in terms of mortality, clinical, class, and recurrence. Results: Two hundred ninety-three patients with DM and PE were included in the study. Patients with adverse outcome had significantly higher admission blood glucose levels (respectively, 197.9 ± 96.30 mg/dL vs. 170.7 ± 74.26 mg/dL; p = 0.03). Patients in the third and fourth quartile of admission blood glucose levels (>152 mg/ dL) had significantly more severe disease with a higher proportion of massive and sub-massive PE and higher pro-BNP levels (respectively, p = 0.01 and 0.02). Conclusion: Non-survived patients and recurrent disease were associated with higher admission blood glucose levels. Also, patients with admission blood glucose levels higher than 152 mg/dL tend to have clinically more severe diseases.

BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis.

Generating the hash values of short subsequences, called seeds, enables quickly identifying similarities between genomic sequences by matching seeds with a single lookup of their hash values. However, these hash values can be used only for finding exact-matching seeds as the conventional hashing methods assign distinct hash values for different seeds, including highly similar seeds. Finding only exact-matching seeds causes either (i) increasing the use of the costly sequence alignment or (ii) limited sensitivity. We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seed matches. BLEND (i) utilizes a technique called SimHash, that can generate the same hash value for similar sets, and (ii) provides the proper mechanisms for using seeds as sets with the SimHash technique to find fuzzy seed matches efficiently. We show the benefits of BLEND when used in read overlapping and read mapping. For read overlapping, BLEND is faster by 2.4×-83.9× (on average 19.3×), has a lower memory footprint by 0.9×-14.1× (on average 3.8×), and finds higher quality overlaps leading to accurate de novo assemblies than the state-of-the-art tool, minimap2. For read mapping, BLEND is faster by 0.8×-4.1× (on average 1.7×) than minimap2. Source code is available at https://github.com/CMU-SAFARI/BLEND.

Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study.

BACKGROUND: Implementing automated facial expression recognition on mobile devices could provide an accessible diagnostic and therapeutic tool for those who struggle to recognize facial expressions, including children with developmental behavioral conditions such as autism. Despite recent advances in facial expression classifiers for children, existing models are too computationally expensive for smartphone use. OBJECTIVE: We explored several state-of-the-art facial expression classifiers designed for mobile devices, used posttraining optimization techniques for both classification performance and efficiency on a Motorola Moto G6 phone, evaluated the importance of training our classifiers on children versus adults, and evaluated the models' performance against different ethnic groups. METHODS: We collected images from 12 public data sets and used video frames crowdsourced from the GuessWhat app to train our classifiers. All images were annotated for 7 expressions: neutral, fear, happiness, sadness, surprise, anger, and disgust. We tested 3 copies for each of 5 different convolutional neural network architectures: MobileNetV3-Small 1.0x, MobileNetV2 1.0x, EfficientNetB0, MobileNetV3-Large 1.0x, and NASNetMobile. We trained the first copy on images of children, second copy on images of adults, and third copy on all data sets. We evaluated each model against the entire Child Affective Facial Expression (CAFE) set and by ethnicity. We performed weight pruning, weight clustering, and quantize-aware training when possible and profiled each model's performance on the Moto G6. RESULTS: Our best model, a MobileNetV3-Large network pretrained on ImageNet, achieved 65.78% accuracy and 65.31% F1-score on the CAFE and a 90-millisecond inference latency on a Moto G6 phone when trained on all data. This accuracy is only 1.12% lower than the current state of the art for CAFE, a model with 13.91x more parameters that was unable to run on the Moto G6 due to its size, even when fully optimized. When trained solely on children, this model achieved 60.57% accuracy and 60.29% F1-score. When trained only on adults, the model received 53.36% accuracy and 53.10% F1-score. Although the MobileNetV3-Large trained on all data sets achieved nearly a 60% F1-score across all ethnicities, the data sets for South Asian and African American children achieved lower accuracy (as much as 11.56%) and F1-score (as much as 11.25%) than other groups. CONCLUSIONS: With specialized design and optimization techniques, facial expression classifiers can become lightweight enough to run on mobile devices and achieve state-of-the-art performance. There is potentially a "data shift" phenomenon between facial expressions of children compared with adults; our classifiers performed much better when trained on children. Certain underrepresented ethnic groups (e.g., South Asian and African American) also perform significantly worse than groups such as European Caucasian despite similar data quality. Our models can be integrated into mobile health therapies to help diagnose autism spectrum disorder and provide targeted therapeutic treatment to children.

A Scoring System to Determine the Risk Factors Causing Recurrent Pulmonary Thromboembolism.

BACKGROUND: The risk of recurrence in pulmonary embolism is the highest in the first week after the acute event. Although it decreases over time, it may remain high for months depending on compliance with treatment and the nature of the underlying risk factor. Our study aimed to identify risk factors that lead to recurrence in pulmonary thromboembolism (PTE) patients and establish an easy-to-use scoring system that determines the risk of recurrence after the first embolism. METHODS: We retrospectively evaluated 1452 patients who were diagnosed with acute PTE between 7/1/2014 and 7/1/2019. Demographic data, comorbidities and clinical data of the patients, and risk factors were recorded. The relationship of the examined parameters with recurrent PTE was evaluated. RESULTS: Diabetes mellitus (DM), hypertension, obesity, and the presence of at least one hereditary risk factor were found to be associated with recurrence. The sensitivity of our score was 66.9%, the specificity was 63.2%, the positive predictive value was 19%, and the negative predictive value was 93.7%. The risk of recurrence in the patients identified as high-risk in the scoring system was 3.47 times higher than those identified as low-risk. CONCLUSION: In terms of risk of recurrence, special attention should be paid to patients with diabetes, HT, obesity and any of the hereditary risk factors. Using scoring systems to determine the risk of recurrence will be valuable and interesting as it is easy-to-use, gives quick results and provides quantitative results.

From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures.

We now need more than ever to make genome analysis more intelligent. We need to read, analyze, and interpret our genomes not only quickly, but also accurately and efficiently enough to scale the analysis to population level. There currently exist major computational bottlenecks and inefficiencies throughout the entire genome analysis pipeline, because state-of-the-art genome sequencing technologies are still not able to read a genome in its entirety. We describe the ongoing journey in significantly improving the performance, accuracy, and efficiency of genome analysis using intelligent algorithms and hardware architectures. We explain state-of-the-art algorithmic methods and hardware-based acceleration approaches for each step of the genome analysis pipeline and provide experimental evaluations. Algorithmic approaches exploit the structure of the genome as well as the structure of the underlying hardware. Hardware-based acceleration approaches exploit specialized microarchitectures or various execution paradigms (e.g., processing inside or near memory) along with algorithmic changes, leading to new hardware/software co-designed systems. We conclude with a foreshadowing of future challenges, benefits, and research directions triggered by the development of both very low cost yet highly error prone new sequencing technologies and specialized hardware chips for genomics. We hope that these efforts and the challenges we discuss provide a foundation for future work in making genome analysis more intelligent.

FastRemap: a tool for quickly remapping reads between genome assemblies.

MOTIVATION: A genome read dataset can be quickly and efficiently remapped from one reference to another similar reference (e.g., between two reference versions or two similar species) using a variety of tools, e.g., the commonly used CrossMap tool. With the explosion of available genomic datasets and references, high-performance remapping tools will be even more important for keeping up with the computational demands of genome assembly and analysis. RESULTS: We provide FastRemap, a fast and efficient tool for remapping reads between genome assemblies. FastRemap provides up to a 7.82× speedup (6.47×, on average) and uses as low as 61.7% (80.7%, on average) of the peak memory consumption compared to the state-of-the-art remapping tool, CrossMap. AVAILABILITY AND IMPLEMENTATION: FastRemap is written in C++. Source code and user manual are freely available at: github.com/CMU-SAFARI/FastRemap. Docker image available at: https://hub.docker.com/r/alkanlab/fastremap. Also available in Bioconda at: https://anaconda.org/bioconda/fastremap-bio.

Machine learning models using mobile game play accurately classify children with autism.

Digitally-delivered healthcare is well suited to address current inequities in the delivery of care due to barriers of access to healthcare facilities. As the COVID-19 pandemic phases out, we have a unique opportunity to capitalize on the current familiarity with telemedicine approaches and continue to advocate for mainstream adoption of remote care delivery. In this paper, we specifically focus on the ability of GuessWhat? a smartphone-based charades-style gamified therapeutic intervention for autism spectrum disorder (ASD) to generate a signal that distinguishes children with ASD from neurotypical (NT) children. We demonstrate the feasibility of using "in-the-wild", naturalistic gameplay data to distinguish between ASD and NT by children by training a random forest classifier to discern the two classes (AU-ROC = 0.745, recall = 0.769). This performance demonstrates the potential for GuessWhat? to facilitate screening for ASD in historically difficult-to-reach communities. To further examine this potential, future work should expand the size of the training sample and interrogate differences in predictive ability by demographic.

The comparison of exacerbation and pneumonia before and after conjugated pneumococcal vaccination in patients with chronic obstructive pulmonary disease, and the effect of inhaled corticosteroid use on results.

Introduction: Pneumococcal infections and exacerbations are important causes of mortality and morbidity in chronic obstructive pulmonary disease (COPD). The use of inhaled corticosteroids and pneumococcal vaccination are suggested for the control of the disease progression and exacerbations. The aim of this study is to assess the effect of pneumococcal conjugate vaccine on pneumonia and exacerbation in COPD patients using inhaled corticosteroids (ICSs). The secondary aim is to analyze the effect of ICS use and different ICS types, if administered, on exacerbation and pneumonia incidence in the study population. Materials and Methods: Medical records of 108 adult patients with COPD who were vaccinated with the pneumococcal conjugate vaccine (PCV13) were retrospectively evaluated. The number of acute exacerbations and pneumonia within one year before and after vaccination were evaluated in all included COPD patients. The comparison analysis was also performed based on the ICS types. Result: There were statistically significant differences between the mean numbers of pneumonia and exacerbations before and after vaccination (p<0.05). There were no significant differences in the mean pneumonia attacks and acute exacerbations between patients using ICS and not using ICS (p> 0.05). Conclusions: This study revealed that PCV13 provides a significant decrease in both exacerbation and pneumonia episodes in COPD patients. On the other hand, the use of ICSs and the types of ICSs were not found to have adverse effects on pneumonia and acute exacerbations in vaccinated COPD patients.