Quantitative Analysis Research Articles

Optimizing MR-based attenuation correction in hybrid PET/MR using deep learning: validation with a flatbed insert and consistent patient positioning.

To address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve precise MR-CT matching for accurate MRAC evaluation. A validation dataset of 21 patients underwent whole-body [18F]FDG PET/CT followed by [18F]FDG PET/MR. A flatbed insert ensured consistent positioning, allowing direct comparison of four MRAC methods-four-tissue and five-tissue models with discrete and continuous μ-maps-against CT-based attenuation correction (CTAC). A deep learning-based framework, trained on a dataset of 300 patients, was used to generate synthesized-CTs from MR images, forming the basis for all MRAC methods. Quantitative analyses were conducted at the whole-body, region of interest, and lesion levels, with lesion-distance analysis evaluating the impact of bone proximity on standardized uptake value (SUV) quantification. Distinct differences were observed among MRAC methods in spine and femur regions. Joint histogram analysis showed MRAC-4 (continuous μ-map) closely aligned with CTAC. Lesion-distance analysis revealed MRAC-4 minimized bone-induced SUV interference (r = 0.01, p = 0.8643). However, tissues prone to bone segmentation interference, such as the spine and liver, exhibited greater SUV variability and lower reproducibility in MRAC-4 compared to MRAC-2 (2D bone segmentation, discrete μ-map) and MRAC-3 (3D bone segmentation, discrete μ-map). Using a flatbed insert, this study validated MRAC with high precision. Continuous μ-value MRAC method (MRAC-4) demonstrated superior accuracy and minimized bone-related SUV errors but faced challenges in reproducibility, particularly in bone-rich regions.

Insights into the role of the D-cluster in [NiFe]-CODH from Rhodospirillum rubrum.

The [NiFe]-CODH from Rhodospirillum rubrum contains [4Fe4S] clusters that allow electron transfer from the buried active sites to the protein surface. Among them, the role of the D-cluster, located at the dimer interface is still not fully understood. In this study, the removal of the D-cluster by site-directed mutagenesis revealed remarkable features in the behavior of the enzyme. Quantitative analysis and spectroscopic studies unveiled the suppression of D-cluster in the mutants and the influence on other metal cofactors. Furthermore, the CO oxidation activity in solution measured in the presence of methylviologen is almost completely abolished in the mutants. Conversely, direct electrochemistry at a functionalized carbon nanotube electrode shows that the mutants are still catalytically active reaching reduced but significant current densities of 0.7 mA cm-2. Moreover, the electroenzymatic activity towards oxygen is not affected by the removal of the D cluster. EPR studies reveal a remarkable change in the magnetic coupling between FeS clusters upon removal of the D-cluster, highlighting the effect of the location of this D-cluster at the dimer interface. It is noteworthy that in addition to its role as electron relay, the D-cluster appears to play an important role in the biosynthesis of the active site.

Impact of Automation on Retail Logistics: AI-Powered Solutions for Efficient Supply Chains

This research explores the transformative impact of automation on retail logistics, focusing specifically on AI-powered solutions that enhance supply chain efficiency. As e-commerce continues to grow, retailers face increasing pressure to streamline logistics processes to meet rising consumer expectations for fast, reliable deliveries and cost-effective operations. AI technologies, such as machine learning, predictive analytics, and autonomous systems, have emerged as critical tools in addressing these challenges by improving various logistics functions, including demand forecasting, inventory management, route planning, and warehouse automation. This study investigates the role of AI-powered solutions in enhancing operational performance, reducing costs, and boosting customer satisfaction across retail logistics networks. A mixed-methods approach is employed, combining quantitative analysis of logistics performance data and qualitative case studies from retail companies that have integrated AI technologies into their supply chains. The quantitative analysis examines key performance indicators such as delivery time, cost per order, inventory turnover, and customer satisfaction before and after the adoption of AI solutions. The qualitative analysis draws on case studies from companies like Amazon, Walmart, and JD.com to explore the real-world applications and challenges of AI in retail logistics. The results indicate that AI automation improves forecasting accuracy, reduces operational costs, enhances inventory management, and improves delivery efficiency. However, challenges such as system integration, data quality, and workforce adaptation are also identified. This study contributes to understanding the current state of AI applications in retail logistics and offers practical insights for industry leaders aiming to optimize their supply chains. Keywords: Automation, Artificial Intelligence, Retail Logistics, Supply Chain Efficiency, Machine Learning, Inventory Management, Predictive Analytics, E-commerce, Cost Reduction, Customer Satisfaction

Dipicolylaminofluorene Derivatives for Fluorescent Sensing of Copper(II) Ion and Glyphosate

AbstractThe contamination of water by toxic herbicides poses a significant environmental challenge, necessitating the development of reliable detection tools. In this study, novel dipicolylamino derivatives of fluorene and fluorenone were synthesized via Sonogashira cross‐coupling reactions with high yields. These compounds demonstrated exceptional selectivity for fluorescence turn‐off by Cu(II) ions, with detection limits of 0.99 μM and 1.11 μM in a DMSO‐HEPES buffer system. Investigations using Job's plot, fluorescence recovery with EDTA, and mass spectrometry revealed that the sensing mechanism involves complexation between the compounds and Cu(II) ions. Notably, the Cu(II)‐fluorene complex exhibited a selective fluorescence turn‐on response to glyphosate, achieving a detection limit of 0.93 μM. Quantitative analysis in real water samples demonstrated good recovery rates, underscoring the practical utility of these fluorene and fluorenone derivatives in environmental monitoring.

Analysis of Causes for Extension of Time in Construction Projects: A Case Study of Kageshwori Manohara Municipality

Extension of Time (EoT) claims are a common occurrence in construction projects worldwide, often leading to disputes due to insufficient attention to delay causes during the contract initiation phase. However, in the context of Kageshwori Manohara Municipality, these challenges are particularly pronounced due to localized factors. This study identifies key causes of EoT through qualitative and quantitative analyses, utilizing Principal Component Analysis (PCA), Kaiser-Meyer-Olkin (KMO) testing, Bartlett's Test of Sphericity, Eigenvalue and variance analysis, and reliability assessments using Cronbach's Alpha. Principal Component Analysis (PCA) helped to categorize twelve major EoT causes into three components: Design and Consultant-Related Issues (Eigenvalue: 13.892, Variance: 55.57%), Project Management and Execution Challenges (Eigenvalue: 1.146, Variance: 4.58%), and External and Unforeseen Conditions (Eigenvalue: 1.000, Variance: 4.00%). Significant causes include inadequate design information, poor project management, and unforeseen challenges like land acquisition disputes. These causes were ranked using the Relative Importance Index (RII) to prioritize their impacts on project time extensions. The authors believe that present findings illuminate the need for improved design and planning processes, along with adaptive project management practices to mitigate EoT claims and enhance project performance within the municipality.

Experiences and Lessons Learned from the Measles-Rubella Vaccination Campaign 2023 Implemented in Private Schools of Kolkata

Abstract Background: Measles-Rubella vaccination campaign (MRVC) was conducted in West Bengal with a target to achieve more than 95% vaccination among children aged 9 months to 15 years. Materials and Methods: A concurrent mixed-method study was undertaken in Kolkata from June 2023 to July 2023 with the objectives to explore strategies adopted in MRVC to achieve target, and lessons learned, and to identify barriers to implement MRVC, particularly in elite private schools. Thirty private schools and three medical officers of the borough health office were interviewed. Both quantitative and qualitative analyses were conducted. Results: The vaccination coverage was 70.16%. Sensitization meeting with parents before MRVC was not conducted in eight schools; key messages communicated were on the safety and benefit of the measles and rubella (MR) vaccine. The main pre-implementation barriers identified were the short notice period, sessions conflicting with pre-planned school activities, and hesitancy of parents. Frequently reported barriers during the sessions were delays in starting sessions, 30-minute waiting period after vaccination, and the involvement of teachers in the documentation process. Missed children were immunized in local health facilities and community-based sessions. Conclusions: With one of every four students not getting the vaccine in the school campaign, the progress toward MR elimination will halt. The main reasons for poor performance were deficient coordination between the health and education department, lack of ownership by schools, and low visibility of the campaign. Observations from this study highlight the need for effective planning, better advocacy, judicious use of communication strategy, and school-led promotion of vaccination.

Law and Economics in Japan’s Courts: Emerging Trends and Developments

Abstract This article briefly reviews the emerging trends of incorporating law and economics perspectives within the Japanese judiciary. In the first part, I review opinions of Justice Koichi Kusano, the first justice in the history of the Supreme Court to adopt law and economics approaches. In particular, I discuss his two key approaches: the maximization of well-being and the use of quantitative empirical analysis. In the second part, I explore the broader judiciary context, including transformations in the Supreme Court’s majority opinions and the practices of lower courts and lawyers. Specifically, I highlight the Supreme Court’s growing emphasis on empirical data, particularly concerning social circumstances and public attitudes. Additionally, I analyze the subsequent shift in lawyers’ practices, which increasingly include the submission of survey data and statistical evidence. I also present emerging trends in the annual number of court decisions that reference regression analysis.

Implementasi Kurikulum Sekolah Alam dalam Pembentukan Pendidikan Karakter

The purpose of this study is to determine how the Nature School curriculum is used to build student character. This research method is a literature study method. The literature study method is a research method used in this study. Data collection techniques are carried out by studying literature from written sources, such as books, journals, and articles to strengthen arguments. The data sources used are scientific journals with quantitative data analysis techniques. The results of the study are that the nature school curriculum does not only focus on cognitive aspects, but also on students' social, emotional, and spiritual development. In facing climate change and other environmental issues, nature-based character education is very relevant to prepare future generations. Examples of nature school activities that can shape students' character are planting trees, cleaning rivers, watering plants, gardening, raising livestock, disposing of garbage in its place, and market day activities.

Knowledge, Attitudes, Beliefs, and Practices Regarding Maternal, Infant, and Young Child Feeding (MIYCF) among Women Living in Tea Estates of Dibrugarh District: A Mixed-Method Study

Abstract Background: Despite global efforts, malnutrition remains a significant contributor to under-five mortality. Current study, aimed to assess knowledge, attitudes, beliefs, and practices related to maternal, infant, and young child feeding (MIYCF) among women with children aged 0-23 months, working in tea estates of the Dibrugarh district. Methods: Community-based cross-sectional study using mixed-method design conducted in 2022. The quantitative study involved 198 women selected through multistage sampling, while the qualitative segment included focus group discussions and in-depth interviews with healthcare providers. SPSS version 25 and Atlas Ti software are used for quantitative and qualitative data analysis, respectively. Results: A discernible gap between knowledge and practices highlighted the necessity for targeted behavioral interventions. Cultural norms, dietary restrictions, and traditional practices affecting MIYCF were documented in a qualitative study. Service providers identified economic conditions, ignorance, and reliance on traditional healers as contributing factors to malnutrition. Conclusion: Holistic, culturally sensitive interventions are pivotal to addressing the intricate interplay between knowledge, societal norms, and MIYCF practices in the tea estates of Dibrugarh.

Quantitative Proteomic Analysis of Lysine Malonylation in Response to Salicylic Acid in the Roots of Platycodon grandiflorus

Salicylic acid, as a plant hormone, significantly affects the physiological and biochemical indexes of soluble sugar, malondialdehyde content, peroxidase, and superoxide dismutase enzyme activity in Platycodon grandiflorus. Lysine malonylation is a post-translational modification that involves various cellular functions in plants, though it is rarely studied, especially in medicinal plants. In this study, the aim was to perform a comparative quantitative proteomic study of malonylation modification on P. grandiflorus root proteins after salicylic acid treatment using Western blot with specific antibodies, affinity enrichment and LC-MS/MS analysis methods. The analysis identified 1907 malonyl sites for 809 proteins, with 414 proteins and 798 modification sites quantified with high confidence. Post-treatment, 361 proteins were upregulated, and 310 were downregulated. Bioinformatics analysis revealed that malonylation in P. grandiflorus is primarily involved in photosynthesis and carbon metabolism. Physiological and biochemical analysis showed that salicylic acid treatment increased the malondialdehyde levels, soluble protein, superoxide dismutase, and peroxidase activity but did not significantly affect the total saponins content in P. grandiflorus. These findings provide an important basis for exploring the molecular mechanisms of P. grandiflorus following salicylic acid treatment and enhance understanding of the biological function of protein lysine malonylation in plants.

Transformer-based fusion model for mild depression recognition with EEG and pupil area signals.

Early detection and treatment are crucial for the prevention and treatment of depression; compared with major depression, current researches pay less attention to mild depression. Meanwhile, analysis of multimodal biosignals such as EEG, eye movement data, and magnetic resonance imaging provides reliable technical means for the quantitative analysis of depression. However, how to effectively capture relevant and complementary information between multimodal data so as to achieve efficient and accurate depression recognition remains a challenge. This paper proposes a novel Transformer-based fusion model using EEG and pupil area signals for mild depression recognition. We first introduce CSP into the Transformer to construct single-modal models of EEG and pupil data and then utilize attention bottleneck to construct a mid-fusion model to facilitate information exchange between the two modalities; this strategy enables the model to learn the most relevant and complementary information for each modality and only share the necessary information, which improves the model accuracy while reducing the computational cost. Experimental results show that the accuracy of the EEG and pupil area signals of single-modal models we constructed is 89.75% and 84.17%, the precision is 92.04% and 95.21%, the recall is 89.5% and 71%, the specificity is 90% and 97.33%, the F1 score is 89.41% and 78.44%, respectively, and the accuracy of mid-fusion model can reach 93.25%. Our study demonstrates that the Transformer model can learn the long-term time-dependent relationship between EEG and pupil area signals, providing an idea for designing a reliable multimodal fusion model for mild depression recognition based on EEG and pupil area signals.

Diagnosis Documentation Done Right: Cross-Specialty Standard for the Diagnosis Section in German Discharge Summaries — A Mixed-Methods Study

Abstract Background The diagnosis section in hospital discharge summaries is critical for continuity of care and patient safety, yet it varies widely in quality, format, and content due to a lack of standards. Objective This study aims to develop a cross-specialty standard for the structure and content of the diagnosis section, based on the preferences of German physicians. The study examines physicians’ satisfaction with the diagnosis section, their rating of its importance, and their preferences for its specific elements, comparing perspectives between inpatient and outpatient physicians. Design, Participants, Approach This mixed-methods study integrated a scoping review, focus group discussion, and a nationwide survey of 602 physicians (317 outpatient primary care and 285 inpatient physicians; 4.1% response rate), most trained in internal medicine. Quantitative analyses evaluated physician satisfaction and preferences, while qualitative feedback provided deeper insights regarding preferred content and format. Key Results Although 95.7% of physicians considered the diagnosis section crucial for follow-up care, only 36.9% were satisfied with its current content and format. 91.2% supported standardizing the diagnosis section, identifying 18 content elements to be included for every current treatment diagnosis. Strong consensus (> 95.0% agreement) was reached for “name of the diagnosis,” “severity/stage/classification/TNM,” “localization/extent/pattern of involvement,” “course e.g., acute, chronic, recurrent,” “expression,” “complications,” “date of initial diagnosis,” and “etiology/cause.” 86.4% preferred separating current and chronic/prior diagnoses with headings. Outpatient physicians were more likely than inpatient physicians to rate “ICD-10 codes” as mandatory (46.2% vs. 14.8%, p < 0.001) and to consider “recommendations for further procedures” (76.6% vs. 63.6%, p < 0.001) and “follow-up appointments” (77.3% vs. 63.5%, p < 0.001) as necessary. Additionally, a list of practical recommendations for clinicians to better document diagnoses was derived. Conclusions This study proposes a cross-specialty standard for the diagnosis section based on physician preferences for a clearly structured format and 18 key content elements.

Words of well-being: language reveals differences in the relational nature of five components of subjective well-being

ABSTRACT This paper explores the differences between five components of subjective well-being: happiness, meaning, harmony, spirituality, and religiosity. This is a pressing question, as growing research suggests that a good life encompasses more than just happiness, underscoring the need to understand the distinctions between subjective well-being components. Quantitative approaches often reveal high correlations between them, making differentiation difficult. To address this, we employed a mixed-methods design, including a quantitative analysis of open-ended responses from 1,084 British participants. Our findings indicate that happiness is primarily oriented toward small communities, while the other components are more broader communities-oriented. These results contribute to a nuanced understanding of human flourishing and lay the groundwork for further research into the distinctions between well-being components.

Impact of Artistic Expression on the Development of Entrepreneurial Skills in Secondary Education

This article analyzes the impact of artistic expression on the development of entrepreneurial skills in high school students through an investigation comparing two groups, experimental and control group, in the development of an entrepreneurship project, in which students could incorporate artistic expression tools for the development and design of their business plan. Through a qualitative and quantitative analysis of the scores obtained from different perspectives (Idea, Design, Presentation, Feasibility and Rating), significant differences have been observed in different parameters between the groups. The results show how the use of artistic expression favors key competencies such as critical thinking, creativity and problem solving, reflected in better ratings in aspects such as feasibility and presentation of the project. The study, carried out over three academic years, shows how the academic grades obtained are slightly higher in the experimental groups than in the control groups. As an analysis methodology, statistical tests such as Student’s t-test and Mann-Whitney U-test as well as an ANOVA analysis have been carried out to reflect the significant differences between the different parameters. The main conclusion is that the incorporation of artistic expression in entrepreneurship education not only enriches the learning process, but also enhances entrepreneurial skills.

Research on Rapid and Non-Destructive Detection of Coffee Powder Adulteration Based on Portable Near-Infrared Spectroscopy Technology

This study explores the feasibility of using portable near-infrared spectroscopy for the rapid and non-destructive detection of coffee adulteration. Spectral data from adulterated coffee samples in the 900–1700 nm range were collected and processed using five preprocessing methods. For qualitative detection, the Support Vector Machine (SVM) algorithm was applied. For quantitative detection, two optimization algorithms, Invasive Weed Optimization (IWO) and Binary Chimp Optimization Algorithm (BChOA), were used for the feature wavelength selection. The results showed that convolution smoothing combined with multiple scattering correction effectively improved the signal-to-noise ratio. SVM achieved 96.88% accuracy for qualitative detection. For the quantitative analysis, the IWO algorithm identified key wavelengths, reducing data dimensionality by 82.46% and improving accuracy by 10.96%, reaching 92.25% accuracy. In conclusion, portable near-infrared spectroscopy technology can be used for the rapid and non-destructive qualitative and quantitative detection of coffee adulteration and can serve as a foundation for the further development of rapid, non-destructive testing devices. At the same time, this method has broad application potential and can be extended to various food products such as dairy, juice, grains, and meat for quality control, traceability, and adulteration detection. Through the feature wavelength selection method, it can effectively identify and extract spectral features associated with these food components (such as fat, protein, or characteristic compounds), thereby improving the accuracy and efficiency of detection, further ensuring food safety and enhancing the level of food quality control.

Modernizing a giant: assessing the impact of military-civil fusion on innovation in China’s defence-technological industry

ABSTRACT In light of China’s objective of achieving complete military modernization by 2035 and becoming a world-class military by 2049, whether it will successfully modernize its military or not is a crucial question for the 21st century. Historically reliant on foreign technologies, China now faces challenges in developing indigenous innovation capabilities. Utilizing a mixed-methods approach, combining quantitative analysis of financial data from Chinese listed companies with case studies, this paper assesses the impact of military-civil fusion (MCF) on innovation in China’s defence-technological industry. Findings indicate that MCF pushed state-owned enterprises (SOE) to be more efficient and to recentre on core businesses, while encouraging the gradual entry of private firms into the industry. Yet, several limitations exist. SOEs continue to enjoy greater subsidies and better integration into the national innovation system, cementing their advantage over their private counterparts. Private companies function as suppliers of large conglomerates, which remain the only ones capable of assembling advanced weapon systems. By examining the interaction between market dynamics and state-driven strategies, this paper explores the evolving role of China’s private sector in military modernization and underscores the synergies between government support and market interests in fostering innovation.

Impact of User Perceptions on Sharing Intentions in Sri Lanka's Transportation Sector: An Analysis of Risk, Convenience, Practicality, and Value

This research examines how perceived risk, convenience, practicality, and value impact ride-sharing intention in Sri Lanka's Western Province. It employs a deductive approach, utilizing surveys and quantitative analysis. The study focuses on ride-sharing service users in Sri Lanka, with a sample size of 884 determined through simple random sampling. Data was collected via self-administered printed questionnaires, with 900 responses received. However, only 884 responses were considered for analysis due to incomplete or biased questionnaires. SPSS version 26.0 was used to conduct the statistical analysis. The results highlight the significant influence of sharing intention on users' perceptions, emphasizing that user perceptions are pivotal in shaping ride-sharing intention. Hypothesis testing revealed a negative correlation between perceived risk and sharing intention. Parametric tests, including correlation and multiple linear regressions, upheld these findings. Furthermore, there exists a statistically significant positive relationship between perceived convenience, practicality, value, and sharing intention. These findings deepen our understanding of the determinants of ride-sharing intention in Sri Lanka, providing valuable insights for policymakers and industry players to bolster the adoption of ride-sharing services and nurture the sharing economy's growth. Additionally, this study enriches academic comprehension of how the sharing economy can address economic challenges and enhance the quality of life in developing nations like Sri Lanka. Future research endeavors should broaden their scope to encompass all regions of Sri Lanka for enhanced generalizability.

From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability

The widespread use of electronic health records (EHRs) and healthcare information systems (HISs) has led to isolated data silos across healthcare providers, and current interoperability standards like FHIR cannot address some scenarios. For instance, it cannot retrieve patients’ health records if they are stored by multiple healthcare providers with diverse interoperability standards or the same standard but different implementation guides. FHIR and similar standards prioritize institutional interoperability rather than patient-centered interoperability. We explored the challenges in transforming fragmented data silos into patient-centered data interoperability. This research comprehensively reviewed 56 notable studies to analyze the challenges and approaches in patient-centered interoperability through qualitative and quantitative analyses. We classified the challenges into four domains and categorized common features of the propositions to patient-centered interoperability into six categories: EMR integration, EHR usage, FHIR adaptation, blockchain application, semantic interoperability, and personal data retrieval. Our results indicated that “using blockchain” (48%) and “personal data retrieval” (41%) emerged as the most cited features. The Jaccard similarity analysis revealed a strong synergy between blockchain and personal data retrieval (0.47) and recommends their integration as a robust approach to achieving patient-centered interoperability. Conversely, gaps exist between semantic interoperability and personal data retrieval (0.06) and between FHIR adaptation and personal data retrieval (0.08), depicting research opportunities to develop unique contributions for both combinations. Our data-driven insights provide a roadmap for future research and innovation.

Impact of Climate Change on Informal Street Vendors: A Systematic Review to Help South Africa and Other Nations (2015–2024)

Climate change poses significant challenges to informal street vendors, particularly in urban settings where they operate in vulnerable environments. These challenges include economic instability, health risks, and sociopolitical exclusion, which are further exacerbated by extreme weather events and inadequate policy support. This review focuses on understanding these impacts by synthesizing lessons and exploring potential solutions from South Africa and other regions that could be implemented by governments and street vendors. A systematic review was conducted, following PRISMA guidelines, to synthesize findings from 48 studies published between 2015 and 2024. This review employed qualitative and quantitative analysis using thematic coding in ATLAS.ti and Microsoft Excel version 2024. Comparative analyses across regions and time periods were conducted to identify differences in impacts and adaptation strategies. Among the 48 studies, 52% were conducted in Africa and 50% in Asia, making these the most frequently represented regions in research on climate change and informal street vendors. Most of the publications were between 2021 and 2024 (52%). This review further highlights that climate change has led to significant economic losses, reduced working hours, and increased operational costs for informal vendors. Health impacts, such as respiratory illnesses and heat stress, are prevalent, particularly among vendors exposed to air pollution and extreme temperatures. Gender-specific vulnerabilities were noted, with women facing compounded challenges due to caregiving responsibilities and inadequate access to sanitation facilities. While Asian vendors have adopted technological solutions like mobile payment systems and cooling devices, these may not be currently feasible for South African vendors. Instead, tailored interventions that consider the local context and available resources are necessary to effectively support South African street vendors. Key recommendations include integrating vendors into urban resilience planning such as encouraging the use of cleaner and more sustainable transportation, improving access to healthcare, and providing financial support. Additionally, governments and communities should pilot the solutions identified in this review and publish their findings to inform future policies and practices.

Effects of Tocilizumab on mortality and hospital length of stay in patients with COVID-19: a systematic review and meta-analysis

Interleukin 6 (IL-6) plays an important role in the progression of COVID-19, reinforcing the possible efficacy of treatment with Tocilizumab (TCZ). However, there is no scientific confirmation that proves the real effectiveness of TCZ on mortality or length of hospital stay in patients with SARS-CoV-2. To update the evidence on TCZ treatment in patients with COVID-19 based on a systematic review and meta-analysis. We carried out searches based on the acronym PICOS in 5 databases (PubMed, Scopus, EMBASE, Cochrane and Web of Science). The assessment of methodological quality was carried out using the Cochrane Collaboration’s tool Risk of Bias 2 (RoB-2). The certainty of the evidence was assessed by the Grading of Recommendations Assessment Development and Evaluation (GRADE approach). The quantitative analysis was conducted using the software Review Manager 5.4. Of the 327 articles identified, 12 were included. Mortality was not influenced by treatment with TCZ compared to placebo or standard treatment (RR: 0.92; 95% CI: 0.82-1.04; p=0.17; I2=0%, p=0.63), and the certainty of the evidence was low. Five studies were considered for length of hospital stay (mean difference: -2.61; 95% CI from -5.41 to 0.19; p=0.07; I2=99%, p<0.001), the cumulative evidence it was very low. The risk of bias was low in 10 included studies. TCZ was not efficient in reducing mortality in patients with COVID-19 during hospital admission, but the true effect may be substantially different from the estimated effect. For length of hospital stay, the use of TCZ in patients with COVID-19 did not show a significant reduction in length of stay.