API Test Research Articles

Effect of dusty clinker and alite crystal size on the properties of class G oil well cement slurries

AbstractHoning the quality of cement plays a pivotal role in the petroleum industry throughout drilling operations, undoubtedly. In cement plants, producing high-quality clinker is essential for improving oil well maintenance, reducing expenditures, enhancing safety, and more, demonstrating its importance in the long term. Despite tight control of clinker operational conditions, cement kiln operators can still produce dusty clinker. This work uniquely establishes the previously unexplored relationship between dusty clinker and the qualitative characteristics of class G oil well cement slurries, offering a practical solution to prevent the production of poor-quality cement in factories. All API tests were done according to API Specification 10 A. Furthermore, a thorough microscopic analysis was conducted on both standard and dusty clinkers to establish a connection between the properties of class G oil well cement slurries and the changes in crystal size observed in the minerals of dusty clinker. According to the studies conducted in this regard, changing the size of the cement particles has a considerable influence on the cement performance. Here, results revealed that increasing the alite (C3S) crystal size to 60–70 microns in dusty clinker led to a roughly 20-minute increase in the average thickening time. In addition, the compressive strength cured at 38 and 60 °C for 8 h, decreased by 1.47 and 2.89 MPa, respectively. More importantly, it was found that the average maximum consistency in dusty clinker oil well cement increased by almost 6.4 Bc over a 15 to 30-minute period.

Microbiological studies on Escherichia coli and Klebsiella pneumoniae causing vaginal and urinary tract inflammation; prevalence, antibiotics-resistance and natural products susceptibility

Great efforts have been made for controlling multidrug-resistant bacteria (MDR). The antibacterial activity of natural products is an effective strategy due to its beneficial effects on human health. This study focused on the isolation, identification, studying the prevalence and the susceptibility of Escherichia coli and Klebsiella spp. to some natural products. After isolation, the isolates primarily identified using biochemical and API tests. Then, the antibiotic susceptibility test was done to reveal the most antibiotics resistant isolates before further identification of Escherichia coli and Klebsiella pneumoniae using phoA and gyrA genes, respectively. Furthermore, the sensitivity of the most antibiotics resistant isolates to apple cider vinegar (ACV), garlic oil (GO), coconut oil (CNO), tea tree oil (TTO), and lavender oil (LO) was estimated. All tested extracts especially ACV and TTO showed good antibacterial activity against MRD selected isolates. Statistical analysis indicated that there was significant difference in ACV and TTO antibacterial response between Escherichia coli and Klebsiella pneumoniae whereas there was no significant difference between natural products activity when tested against Escherichia coli and Klebsiella pneumoniae seperatly. GC-MS spectroscopy analysis revealed the most prominent active constituents present in tested ACV and TTO. Analyzing the 16S rRNA sequence confirmed the two most MRD pathogenic isolates that showed sensitivity to ACV and TTO were Escherichia coli MS1 and Klebsiella pneumoniae MS47, respectively. Essential oils tea tree and apple cider vinegar showed good antibacterial activities against antibiotics-resistant Escherichia coli and Klebsiella pneumoniae causing vaginal and urinary tract inflammation.

Crabtree: Rust API Test Synthesis Guided by Coverage and Type

Rust type system constrains pointer operations, preventing bugs such as use-after-free. However, these constraints may be too strict for programming tasks such as implementing cyclic data structures. For such tasks, programmers can temporarily suspend checks using the unsafe keyword. Rust libraries wrap unsafe code blocks and expose higher-level APIs. They need to be extensively tested to uncover memory-safety bugs that can only be triggered by unexpected API call sequences or inputs. While prior works have attempted to automatically test Rust library APIs, they fail to test APIs with common Rust features, such as polymorphism, traits, and higher-order functions, or they have scalability issues and can only generate tests for a small number of combined APIs. We propose Crabtree, a testing tool for Rust library APIs that can automatically synthesize test cases with native support for Rust traits and higher-order functions. Our tool improves upon the test synthesis algorithms of prior works by combining synthesis and fuzzing through a coverage- and type-guided search algorithm that intelligently grows test programs and input corpus towards testing more code. To the best of our knowledge, our tool is the first to generate well-typed tests for libraries that make use of higher-order trait functions. Evaluation of Crabtree on 30 libraries found four previously unreported memory-safety bugs, all of which were accepted by the respective authors.

Advanced White-Box Heuristics for Search-Based Fuzzing of REST APIs

Due to its importance and widespread use in industry, automated testing of REST APIs has attracted major interest from the research community in the last few years. However, most of the work in the literature has been focused on black-box fuzzing. Although existing fuzzers have been used to automatically find many faults in existing APIs, there are still several open research challenges that hinder the achievement of better results (e.g., in terms of code coverage and fault finding). For example, under-specified schemas are a major issue for black-box fuzzers. Currently, EvoMaster is the only existing tool that supports white-box fuzzing of REST APIs. In this paper, we provide a series of novel white-box heuristics, including for example how to deal with under-specified constrains in API schemas, as well as under-specified schemas in SQL databases. Our novel techniques are implemented as an extension to our open-source, search-based fuzzer EvoMaster . An empirical study on 14 APIs from the EMB corpus, plus one industrial API, shows clear improvements of the results in some of these APIs.

Test Coverage in Microservice Systems: An Automated Approach to E2E and API Test Coverage Metrics

Test coverage is a critical aspect of the software development process, aiming for overall confidence in the product. When considering cloud-native systems, testing becomes complex, as it becomes necessary to deal with multiple distributed microservices that are developed by different teams and may change quite rapidly. In such a dynamic environment, it is important to track test coverage. This is especially relevant for end-to-end (E2E) and API testing, as these might be developed by teams distinct from microservice developers. Moreover, indirection exists in E2E, where the testers may see the user interface but not know how comprehensive the test suits are. To ensure confidence in health checks in the system, mechanisms and instruments are needed to indicate the test coverage level. Unfortunately, there is a lack of such mechanisms for cloud-native systems. This manuscript introduces test coverage metrics for evaluating the extent of E2E and API test suite coverage for microservice endpoints. It elaborates on automating the calculation of these metrics with access to microservice codebases and system testing traces, delves into the process, and offers feedback with a visual perspective, emphasizing test coverage across microservices. To demonstrate the viability of the proposed approach, we implement a proof-of-concept tool and perform a case study on a well-established system benchmark assessing existing E2E and API test suites with regard to test coverage using the proposed endpoint metrics. The results of endpoint coverage reflect the diverse perspectives of both testing approaches. API testing achieved 91.98% coverage in the benchmark, whereas E2E testing achieved 45.42%. Combining both coverage results yielded a slight increase to approximately 92.36%, attributed to a few endpoints tested exclusively through one testing approach, not covered by the other.

Backend Development of a Microservice-Based Website Application for Public Issue Reporting: Case Studyn in People Representative Council

This research introduces a web application aimed at advancing public issue reporting, promoting civic engagement, and expediting government responses. In collaboration with DPRD Jawa Barat, the research leverages Scrum methodology, social network features, and a microservice architecture to create an efficient communication platform between citizens and governmental bodies. The backend of the application, developed using the Go programming language, adopts a microservice architecture to enhance scalability and maintainability. The Scrum methodology facilitates an agile development process, ensuring adaptability to changing requirements and fostering continuous improvement throughout the project lifecycle. Additionally, the study explores the incorporation of social network features to encourage public engagement within the application. This integration allows citizens to connect, share, and discuss public issues, further enhancing the collaborative nature of the reporting platform. To ensure the seamless functionality of the microservices, API testing is employed, validating the reliability and consistency of the application's interfaces. Stress testing is also conducted to assess scalability and performance capabilities, identifying potential optimizations for the system's responsiveness under varying levels of load. In conclusion, this research presents an innovative solution for public issue reporting that combines microservice architecture, Scrum methodology, and social network features. The application's integration of these elements aims to not only streamline citizen-government communication but also create a dynamic platform that encourages active public involvement and collaboration.

Integrating Manual Insight in an Automated World of Human Expertise in API Testing

Integrating Manual Insight in an Automated World of Human Expertise in API Testing

Application of ultra-fine particles of potato as eco-friendly green additives for drilling a borehole: A filtration, rheological and morphological evaluation

Drilling fluid is crucial for oil and gas well drilling operations, serving key functions such as facilitating the removal of drill cuttings, maintaining borehole stability and controlling formation pressures. When the drilling fluid is lost into formations, it can alter the formation's integrity, contaminate groundwater and cause permeability damage. In addition, environmental concerns arise from the waste produced during drilling activities, particularly when discarded drilling fluids contain hazardous substances like heavy metals. This study explores the potential of integrating ultrafine potato powder (PP) into water-based drilling fluids (WBDFs) as environmentally friendly additives. Several analytical techniques including X-ray fluorescence (XRF), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electronic microscope (SEM) and differential scanning calorimetry (DSC) were used to comprehensively characterize the prepared PPs. The study included API and HPHT filtration test, permeability plugging test and rheological evaluations of drilling fluids, assessing parameters such as filter loss, filter cake, apparent viscosity, plastic viscosity, yield point and gel strength under varied conditions of different particle sizes of PP, concentrations of PP, and temperature and pressure measurements. The obtaining results emphasize PP's potential to enhance the wellbore stability and reduce the fluid loss, the filtration of water or oil into the permeable formation, achieving 43% reduction in the filtration rate and 70% reduction in the filter cake thickness. Adding 0.5 wt% ultrafine PP improved the maximum gel strength to 32.2 lb/100 ft2, while the same concentration and particle size raised the plastic viscosity from 3 to 6.8 cP, which subsequently dropped to 6 cP in high temperature conditions. PP performed better compared with the reference fluid in improving the thinning behavior of the drilling fluids. Moreover, permeability plugging tests confirm that adding PP effectively lowered the filtration rate, with higher concentrations achieving greater reductions over time. These findings suggest that PP holds promise as an effective additive for drilling fluids, contributing to enhanced drilling efficiency, improved wellbore stability and a reduced likelihood of instability and lost circulation. The characterization and rheological analysis of PP biodegradable drilling fluids provide valuable insights for optimizing fluid formulations, tailoring them to specific operational conditions, and achieving a balance between fluidity, wellbore stability, and cuttings transport. This research highlights the potential of PP as a sustainable and efficient solution in the realm of drilling fluid additives.

The influence of nanoparticle size, concentration, and functionalization on drilling fluid filtration properties

This research investigates the effect of the surface properties of silicon dioxide (SiO2) nanoparticles on the filtration properties of water-based drilling fluid. The nanoparticles were synthesized in-house using a one-step sol-gel method and functionalized into different charges. Different sizes, concentrations, and functionalization of water-based drilling fluid were subjected to standard API and HPHT filtration tests. Regression analysis and ANOVA were carried out to develop a model for future prediction and classify each independent variable's significance. The result shows that the positively charged nanoparticles had better filtration properties, as evidenced by the lower filtrate volume. Functionalization levels had minimal impact on filtration properties, and the effect of concentration was insignificant above 0.4 v/v%. In terms of nanoparticle size, 120 nm showed a more consistent result despite varying concentration and functionalization. In contrast, smaller nanoparticle sizes are more susceptible to changes in concentration and functionalization but could produce the least filtrate volume with the appropriate combination of concentration and functionalization. Filtrate analysis shows that about 25–28 % of the drilling fluid nanoparticles can infiltrate the porous media. Furthermore, this study challenges existing models on the improvement of the filtration properties by using nanoparticles. While previous hypothesis relied on physical bridging mechanism, new evidence from this research suggests that the enhancement is primarily based on the surface properties of the nanoparticles. Specifically, the nanoparticles envelop the barite through monolayer adsorption, reducing the angularity and increasing the reaction sites with water molecules. This inhibits the mobility of the water molecules across the filter cakes, providing a novel understanding of the mechanisms underlying nanoparticle-enhanced filtration properties of drilling fluids. In summary, the filtration properties of water-based drilling fluid with nanoparticles are significantly influenced by the surface properties of the nanoparticles, with positively charged nanoparticles demonstrating superior performance in stable conditions.

Model extraction via active learning by fusing prior and posterior knowledge from unlabeled data

As machine learning models become increasingly integrated into practical applications and are made accessible via public APIs, the risk of model extraction attacks has gained prominence. This study presents an innovative and efficient approach to model extraction attacks, aimed at reducing query costs and enhancing attack effectiveness. The method begins by leveraging a pre-trained model to identify high-confidence samples from unlabeled datasets. It then employs unsupervised contrastive learning to thoroughly dissect the structural nuances of these samples, constructing a dataset of high quality that precisely mirrors a variety of features. A mixed information confidence strategy is employed to refine the query set, effectively probing the decision boundaries of the target model. By integrating consistency regularization and pseudo-labeling techniques, reliance on authentic labels is minimized, thus improving the feature extraction capabilities and predictive precision of the surrogate models. Evaluation on four major datasets reveals that the models crafted through this method bear a close functional resemblance to the original models, with a real-world API test success rate of 62.35%, which vouches for the method’s validity.

Bacteriological Analysis of Street Food to Support the Availability of Hygienic Functional Foods in Kendari City

Food contaminated by pathogenic bacteria can cause various diseases such as diarrhea and typhoid. Bacterial growth in food can also result in unwanted physical or chemical changes, making the food unfit for consumption. The purpose of this study was to determine the number and type of bacteria in some snacks around health facilities in Kendari City. This type of research is descriptive research, the samples in this study are types of snacks around health facilities in Kendari City with testing methods in the form of Total Plate Numbers, API Test, and Confirmation test. The results showed that the total plate number contamination of 2 out of 5 food samples amounted to 3x103 - 4x103 colonies/g, namely in samples 2 and 3. These results indicate that the total plate number value in these samples is higher than SNI 7388: 2009 which is < 1x102 colonies/g. The number of colonies in sample 1, namely fried sweet potato, amounted to 0, in sample 2, namely fried banana, amounted to 4x103, in sample 3, namely sweet potato, amounted to 3x103, in sample 4, namely tempeh, amounted to 0 and sample 5, namely bakwan, amounted to 0. The type of bacteria in the results of bacterial identification testing using the API method in sample 2 is Staphylococcus aureus and sample 3 is E.coli.

Testing RESTful APIs: A Survey

In industry, RESTful APIs are widely used to build modern Cloud Applications. Testing them is challenging, because not only do they rely on network communications, but also they deal with external services like databases. Therefore, there has been a large amount of research sprout in recent years on how to automatically verify this kind of web services. In this article, we present a comprehensive review of the current state-of-the-art in testing RESTful APIs based on the analysis of 92 scientific articles. These articles were gathered by utilizing search queries formulated around the concept of RESTful API testing on seven popular databases. We eliminated irrelevant articles based on our predefined criteria and conducted a snowballing phase to minimize the possibility of missing any relevant paper. This survey categorizes and summarizes the existing scientific work on testing RESTful APIs and discusses the current challenges in the verification of RESTful APIs. This survey clearly shows an increasing interest among researchers in this field, from 2017 onward. However, there are still a lot of open research challenges to overcome.

URLGEN—Toward Automatic URL Generation Using GANs

URLs play an essential role on the Internet, allowing access to Web resources. Automatically generating URLs is helpful in various tasks, such as application debugging, API testing, and blocklist creation for security applications. Current testing suites deeply embed experts’ domain knowledge to generate suitable URLs, resulting in an ad-hoc solution for each given application. These tools thus require heavy manual intervention, with the expensive coding of rules that are hard to maintain. We here introduce URLGEN, a system that uses Generative Adversarial Networks (GANs) to tackle the automatic URL generation problem. URLGEN is designed for web API testing and generates URL samples for an application without any system expertise, complementing the existing tools. It leverages Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures, augmented by an embedding layer that simplifies the URL learning and generation process. We show that URLGEN learns to generate new valid URLs from samples of real URLs without requiring any domain knowledge and following a purely data-driven approach. We compare the GAN architecture of URLGEN against other design options and show that the LSTM architecture can better capture the correlation among URL characters, outperforming previously proposed solutions. Finally, we show that the URLGEN approach can be extended to other scenarios, which we illustrate with two use cases, i.e., cybersquatting domain prediction and URL classification.

ANALYTICAL STUDY OF SAPTAMRITA LAUHA: AN AYURVEDIC HERBO-MINERAL FORMULATION

Saptamrita lauha is one of the herbo-mineral formulations. In Ayurveda classics, there are so many references available for Saptamrita lauha. The formulation distinctively improves eyesight and haemoglobin by its properties chaksushya (good for eyesight) and raktavardhak (haematinic). Standardization of formulation is essential in order to evaluate the quality of formulation. Making tablets from herbo-mineral drugs will aid in uniform dosage form, better palatability and easy acceptability in children. The pharmaceutical analysis method of Saptamrita lauha formulation was done according to the API and drug testing protocol of the Pharmacopoeial laboratory for Indian Medicine (PLIM). Materials and Methods: The prepared drugs were evaluated for organoleptic study, physicochemical study, pH value and microbiological studies for developing standards. Result: Due to low levels of heavy metals and lack of pathogenic bacteria, the formulation is safe for use. Conclusion: The results of pharmacognostical and pharmaceutical parameters have generated data that could be used as a fingerprint for future research. This paper presents the analytical study of the formulation.

Characterisation of Pectinolytic Bacillus pumilus and Paenibacillus amyloliticus Strains, New Pathogens of Potato in Tunisia

Soft rot disease in potato is a major problem in fields and warehouses all over the world. Although it is known that bacteria from the genera Pectobacterium and Dickeya are the main causative agents of soft rot diseases, recent studies indicate the involvement of pectinolytic Bacillus and Paenibacillus in this disease. In the present research, samples of potato with soft rot symptoms were collected from eight governorates of Tunisia. Two hundred and seventy bacterial isolates were acquired from tubers. Twenty of the isolated strains indicted pectinolytic activity by forming deep cavities on crystal violet pectate medium. All pectinolytic isolates were able to macerate potato tuber tissue. Phenotypic characterisation showed that these isolates were Gram-positive bacilli, exhibiting pectinolytic, cellulolytic, proteolytic and amylolytic activity. The majority of the isolates indicated swimming and swarming motility. The application of API test, MALDI-TOF MS and 16S rDNA sequencing allowed for the assignment of nineteen of the tested isolates to the species Bacillus pumilus and one to the species Paenibacillus amyloliticus. To the best of our knowledge, this is the first report of soft rot in potato caused by pectinolytic B. pumilus in Tunisia.

Characterization of chitinolytic bacteria newly isolated from the termite Microcerotermes sp. and their biocontrol potential against plant pathogenic fungi.

Chitinolytic bacteria were isolated from guts and shells of the termite Microcerotermes sp. Among the nineteen morphologically different chitinolytic isolates, three isolates with highest extracellular chitinase production ratio (≥ 2.26) were selected. Based on molecular identification of 16S rRNA gene sequences and biochemical characterizations using API test kits and MALDI-TOF MS, these isolates were closely related to Bacillus thuringiensis (Mc_E02) and Paenibacillus species (Mc_E07 and Mc_G06). Isolate Mc_E02 exhibited the highest chitinase specific activity (2.45 U/mg protein) at 96 h of cultivation and the enzyme activity was optimized at pH 7.0 and 45°C. The isolate showed highest and broad-spectrum inhibitory effect against three phytopathogenic fungi (Curvularia lunata, Colletotrichum capsici, and Fusarium oxysporum). Its 36-kDa chitinase exhibited the biomass reduction and mycelium inhibition against all fungi, with highest effects to Curvularia lunata. This research provides novel information about termite chitinolytic bacteria and their effective chitinase, with potential use as biocontrol tool.

Isolation and Identification of Lactic Acid Bacteria from Homemade Natural Pickles

Pickles are both a traditional and commercial product type formed as a result of fermentation of fruits or vegetables. Many types of pickles prepared by traditional methods can be produced in different ways in terms of raw materials and chemical properties such as salt ratio and acidity. The aim of this study was to determine the existing natural microflora in home pickles and to identify the dominant species. 15 traditional home pickles were used as material for microbiological and some chemical analyses. Isolation of LABs (Lactic acid bacteria) from pickle samples and identification of the isolates by classical methods and API test kits were carried out. In the study, a total of 130 pickle-derived strains were isolated and 88 of them were grouped as Lactobacillus and 42 as Pedicoccus. In addition, total aerobic mesophilic bacteria, yeast and mould, lactobacillus lactococcus counts and pH, acidity and salt content were determined from microbiological parameters and chemical analyses. As a result, it was determined that the dominant flora present in the home pickle samples belonged to L. plantarum.

Collinsella urealyticum sp. nov., a urease-positive bacterial strain isolated from swine faeces

A novel actinobacterial strain, designated AGMB00827T, was isolated from swine faeces. Strain AGMB00827T was obligately anaerobic, Gram-stain-positive, non-motile, non-spore-forming and rod-shaped bacterium. Comparative analyses based on the 16S rRNA gene and whole genome sequence revealed that strain AGMB00827T was affiliated to the genus Collinsella, and was most closely related to Collinsella vaginalis Marseille-P2666T (= KCTC 25056T). Biochemical analysis showed strain AGMB00827T was negative for catalase and oxidase. Interestingly, strain AGMB00827T possessed urease activity, which was determined by traditional methods (API test and Christensen's urea medium), unlike related strains. Furthermore, the major cellular fatty acids (> 10%) of the isolate were C18:1 ω9c, C16:0, C16:0 DMA and C18:2 ω9,12c DMA. Based on the whole genome sequence analysis, the DNA G + C content of strain AGMB00827T was 52.3%, and the genome size and numbers of rRNA and tRNA genes were 1,945,251bp, 3 and 46, respectively. The average nucleotide identity and digital DNA-DNA hybridization values between strain AGMB00827T and C. vaginalis KCTC 25056T were 71.0 and 23.2%, respectively. Additionally, the genome analysis revealed that strain AGMB00827T possesses urease gene cluster including ureABC and ureDEFG while the related strains do not have those genes, which is consistent with the urease activity. On the basis of polyphasic taxonomic approach, strain AGMB00827T represents a novel species within the genus Collinsella, for which the name Collinsella urealyticum sp. nov. is proposed. The type strain is AGMB00827T (= KCTC 25287T = GDMCC 1.2724T).

SMART CANTEEN MANAGEMENT SYSTEM

The introduction of the Internet enables financial transactions through digital payments, supporting the vision of a cashless society. The appearance of the corona virus (COVID-19) had a significant impact on how we handled currency in the past and marked a turning point in the shift from cash to contactless digital payments. By enabling devices to provide goods and services without requiring human involvement, Internet of Things (IoT) technology also advances digital payments. This study presented an Internet of Things (IoT)-enabled cashless vending machine that combines cloud computing and payment gateway for placing orders and making purchases via mobile applications. The system enables a pre-installed mobile application to scan a Quick Response (QR) code attached to a vending machine's body, opens a web-based virtual machine through the code, enables users to select and order items from the virtual vending, initiates and authorizes a digital payment through an IoT gateway installed inside the physical vending machine by connecting users' and vendors' financial entities, and then disburses the purchased goods. It uses an ATmega 2560 Microcontroller, a Esp8266 Wi-Fi module, mobile application software, and payment gateway API to run on the Arduino platform. After 150 consecutive API test calls, the system selected a product with an average response time of 14500 milliseconds. This outcome demonstrates a successful time for enhancing customer purchasing experiences with digital payment systems and an adaptable and reasonably priced intelligent vending machine based on the Internet of Things to be introduced for mass production. Key Words: smart canteen, ordering system, blynk, android phone, vending machine,

The Role of IL-6 and IL-17 in SARS CoV-2 Patients with Secondary Bacterial Pneumonia

The current study was conducted in Kirkuk city at Al-Shifaa 14 hospital from November 2021 to March 2022, indicated the bacteria causing secondary pneumonia isolated from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients and the role of IL-6 and IL-17 in these infection.
 Sputum samples were used to obtain the bacterial isolates, and API testing was used to confirm the species level identification. Using an enzyme-linked immunosorbent assay (sandwich ELISA), the levels of IL-6 and IL-17 in the blood were evaluated. The study documented several bacterial species in a single infection (56/87.5%) or mixed bacterial infection (8/12.5%). The most common isolated bacteria species was Klebsiella pneumoniae (35.95%) followed by Staphylococcus aureus (31.25%), E. coli (17.19%), Pseudomonas aeruginosa (10.94%), and (1.56%) for each of Klebsiella oxytoca, Acinetobacter baumannii and Cronobacter sakazakii. The study recorded a high significant difference (P <0.01) between the patients (22.2±6.82) pg/mL and the control group (58.39±11.15) pg/ mL concerning IL-6 also a high significant difference (P <0.01) between the patients (101.79±27.13) pg/mL and the control group (58.39±11.15) pg/mL concerning IL-17.
 In conclusion, K. pneumoniae and S. aureus were the predominant isolated bacteria from COVID-19 patient's lung and there was a highly significant increase in IL-6 and IL-17 levels in secondary bacterial pneumonia in COVID-19 patients.