Active Research Research Articles

Inside Production Data Science: Exploring the Main Tasks of Data Scientists in Production Environments

Modern production relies on data-based analytics for the prediction and optimization of production processes. Specialized data scientists perform tasks at companies and research institutions, dealing with real data from actual production environments. The roles of data preprocessing and data quality are crucial in data science, and an active research field deals with methodologies and technologies for this. While anecdotes and generalized surveys indicate preprocessing is the major operational task for data scientists, a detailed view of the subtasks and the domain of production data is missing. In this paper, we present a multi-stage survey on data science tasks in practice in the field of production. Using expert knowledge and insights, we found data preprocessing to be the major part of the tasks of data scientists. In detail, we found that tackling missing values, finding data point meanings, and synchronization of multiple time-series were often the most time-consuming preprocessing tasks.

Development of DNA aptamers targeting B7H3 by hybrid-SELEX: an alternative to antibodies for immuno-assays

Antibodies have been extensively used in numerous applications within proteomics-based technologies, requiring high sensitivity, specificity, a broad dynamic range for detection, and precise, reproducible quantification. Seeking alternatives to antibodies due to several inherent limitations of antibodies is an area of active research of tremendous importance. Recently, aptamers have been receiving increasing attention, because they not only have all of the advantages of antibodies, but also have unique advantages, such as thermal stability, low cost, and unlimited applications. Aptamers are gaining importance in immunological studies and can potentially replace antibodies in immunoassays. B7H3, an immunoregulatory protein belonging to the B7 family, is an attractive and promising target due to its overexpression in several tumor tissues while exhibiting limited expression in normal tissues. This study employed hybrid-SELEX with next-generation sequencing to select ssDNA aptamers specifically binding to the B7H3 protein. These aptamers demonstrated versatility across various assays, including flow cytometry, dot-blot, and immunohistochemistry. Effective performance in sandwich dot-blot assays and western blot analysis suggests their potential for diagnostic applications and demonstrates their adaptability and cost-effectiveness in diverse protein detection techniques.

Assessment of the Daily Living Activities of Older People (2004-2023): A Bibliometric and Visual Analysis.

With a rapidly aging global population, comprehending the risks associated with older people's activities of daily living is increasingly important; yet, interdisciplinary analyses remain rare. By providing a bibliometric overview of the capability risks associated with older people's activities of daily living, in order to identify prevailing trends and future directions in the field, the study aims to fill this gap. Using CiteSpace software to analyze data from 928 articles published between 2004 and 2023, the study results demonstrate the growing interest in the capability risks of older people's activities of daily living, with the United States leading in the number of publications, and geriatrics emerging as the dominant discipline. Notably, Institut National de la Sante et de la Recherche Medicale (Inserm) in France emerges as a pivotal contributor in the field. Key research topics encompass risk factors associated with a decline in daily activities and disease-related studies, with emerging trends in cognitive function and instrumental activity research. Future research should prioritize the development of predictive mechanisms for daily living trends, exploration of caregiving solutions, and promotion of interdisciplinary collaboration. This study highlights promising avenues for further research, emphasizing the importance of predictive modeling, innovative caregiving strategies, and interdisciplinary cooperation in addressing capability risks in the activities of daily living of older people.

An effective approach for reducing data redundancy in multi-agent system communication

The redundancy of the data is an active research topic. While an agent works in a multi-agent system, the number of messages between them increases. This is due to the fact that the functionalities data depends on other agents in terms of functional requirements. Typically, only one agent in a multi-agent system is responsible for accessing a database instead of replicating the database on each agent. A database is stored on multiple agents rather than a single agent to avoid a single point of failure. In this approach, the system has a higher load because one agent is responsible for all agent queries and must send duplicate messages to multiple agents, resulting in redundant data. In this research, we present Multi-Agent System for Commodity Data (MASCD) framework, the multi-agent system based communication using the distributed hash system, to reduce data redundancy in multi-agent system communication. Our anticipated method demonstrated how we divided the database names and efficiently distributed data to each agent. The database splitting is based on manufacturer names or product names. We utilize a table based on prime numbers. Through the hash function, we ascertain the index of the agent granted access to the relevant data. Each agent is accountable for its data. We use a Distributed Hash Table for efficient querying that stores data as key-value pairs. Each agent maintains a Finger Table containing the next and previous nodes for agent communication purposes. Using FIPA messages, we demonstrated how an agent could interact optimally. In conclusion, we illustrate the application of the proposed approach through a case study of mobile phones and university information systems.

Separate and Integrated Data Processing for the 3D Reconstruction of a Complex Architecture

Abstract. In the last few years, data fusion has been an active research topic for the expected advantages of exploiting and combining different but complementary techniques for 3D documentation. The data fusion process consists of merging data coming from different sensors and platforms, intrinsically different, to produce complete, coherent, and precise 3D reconstructions. Although extensive research has been dedicated to this task, we still have many gaps in the integration process, and the quality of the results is hardly sufficient in several cases. This is especially evident when the integration occurs in a later stage, e.g., merging the results of separate data processing. New opportunities are emerging, with the possibility offered by some proprietary tools to jointly process heterogeneous data, particularly image and range-based data. The article investigates the benefits of data integration at different processing levels: raw, middle, and high levels. The experiments are targeted to explore, in particular, the results of the integration on large and complex architectures.

ANALYSIS OF THE PROBLEM OF PROTECTION OF INFORMATION IN THE CORPORATE INFORMATION SYSTEMS SEGMENT

Successful use of modern information technologies is impossible without effective management of not only the computer network, but also the IT process. Improvement of information security management is possible both by using new methods for solving the management problem and by increasing the quality of the management effect by reducing the duration of these control periods. Therefore, a reasonable approach to increasing the effectiveness of information security measures can be the development of intelligent decision-making tools related to information management issues. Although active research is currently being conducted on the development of IT methods and systems, there are still many unanswered questions about the creation of methods for creating intelligent PPR tools related to IT management, which indicates a need. Complex solutions to scientific problems aimed at developing not only scientifically based, but also practically applicable models and methods for intellectual support of IT process management.

Aminopyridines in the development of drug candidates against protozoan neglected tropical diseases.

Neglected tropical diseases (NTDs) pose a major threat in tropical zones for impoverished populations. Difficulty of access, adverse effects or low efficacy limit the use of current therapeutic options. Therefore, development of new drugs against NTDs is a necessity. Compounds containing an aminopyridine (AP) moiety are of great interest for the design of new anti-NTD drugs due to their intrinsic properties compared with their closest chemical structures. Currently, over 40 compounds with an AP moiety are on the market, but none is used against NTDs despite active research on APs. The aim of this review is to present the medicinal chemistry work carried out with these scaffolds, against protozoan NTDs: Trypanosoma cruzi, Trypanosoma brucei or Leishmania spp.

ChoreoVis: Planning and Assessing Formations in Dance Choreographies

AbstractSports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real‐world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.

An effective nonlinear dynamic formulation to analyze grasping capability of soft pneumatic robotic gripper

ABSTRACT Soft pneumatic robotic grippers have found extensive applications across various engineering domains, which prompts active research due to their splendid compliance, high flexibility, and safe human-robot interaction over conventional stiff counterparts. Previously simplified rod-based models principally focused on clarifying overall large deformation and bending postures of soft grippers from static or quasi-static perspectives, whereas it is challenging to elaborate grasping characteristics of soft grippers without considering contact interaction and nonlinear large deformation behaviors. To address this, based on absolute nodal coordinate formulation (ANCF), comprehensively allowing for structural complexity, geometric, material and boundary nonlinearities, and incorporating Coulomb’ friction law with a multiple-point contact method, we put forward an effective nonlinear dynamic modeling approach for exploring grasping capability of soft gripper. Moreover, we solved the established dynamic equations using Generalized-α scheme, and conducted thorough numerical simulation analysis on a three-jaw soft pneumatic gripper (SPG) in terms of grasping configurations, displacements and contact forces. The proposed dynamic approach can accurately both describe complicated deformed configurations along with stress distribution and provide a feasible solution to simulate grasping targets, whose effectiveness and precision were analyzed theoretically and verified experimentally, which may shed new light on devising and optimizing other multifunctional SPGs.

An eQTL-based approach reveals candidate regulators of LINE-1 RNA levels in lymphoblastoid cells.

Long interspersed element 1 (LINE-1; L1) are a family of transposons that occupy ~17% of the human genome. Though a small number of L1 copies remain capable of autonomous transposition, the overwhelming majority of copies are degenerate and immobile. Nevertheless, both mobile and immobile L1s can exert pleiotropic effects (promoting genome instability, inflammation, or cellular senescence) on their hosts, and L1's contributions to aging and aging diseases is an area of active research. However, because of the cell type-specific nature of transposon control, the catalogue of L1 regulators remains incomplete. Here, we employ an eQTL approach leveraging transcriptomic and genomic data from the GEUVADIS and 1000Genomes projects to computationally identify new candidate regulators of L1 RNA levels in lymphoblastoid cell lines. To cement the role of candidate genes in L1 regulation, we experimentally modulate the levels of top candidates in vitro, including IL16, STARD5, HSD17B12, and RNF5, and assess changes in TE family expression by Gene Set Enrichment Analysis (GSEA). Remarkably, we observe subtle but widespread upregulation of TE family expression following IL16 and STARD5 overexpression. Moreover, a short-term 24-hour exposure to recombinant human IL16 was sufficient to transiently induce subtle, but widespread, upregulation of L1 subfamilies. Finally, we find that many L1 expression-associated genetic variants are co-associated with aging traits across genome-wide association study databases. Our results expand the catalogue of genes implicated in L1 RNA control and further suggest that L1-derived RNA contributes to aging processes. Given the ever-increasing availability of paired genomic and transcriptomic data, we anticipate this new approach to be a starting point for more comprehensive computational scans for regulators of transposon RNA levels.

Use of Circulating Tumor DNA to Guide Decision-making in Adjuvant Colon Cancer.

The use of circulating tumor DNA (ctDNA) assays to guide clinical decision-making in early-stage colon cancer is an area of rapidly advancing active research. With assays clinically available, clinicians must be informed how to best use this novel tool to treat patients. Recent observational and prospective studies have suggested that ctDNA has potential to guide clinical decision-making in early-stage colon cancer by detecting minimal residual disease (MRD) and predicting recurrence risks. MRD-negative patients may be able to de-escalate or forgo adjuvant chemotherapy (ACT) without compromising disease-free survival or overall survival, while MRD-positive patients may benefit significantly from ACT. Recent and ongoing studies have given reason for optimism about the future of ctDNA as a useful biomarker for clinicians treating early-stage colon cancer. Data thus far are mostly limited to observational studies; inconsistent results highlight the need for caution. As more evidence emerges, ctDNA may become standard of care for colon cancer patients.

Two Decades of Publications in Journals Dedicated to Autoimmunity: A Bibliometric Analysis of the Autoimmunity Field from 2004 to 2023

To carry out an in-depth analysis of the scientific research on autoimmunity, we performed the first bibliometric analysis focusing on publications in journals dedicated to autoimmunity (JDTA) indexed by science citation index during the period 2004–2023. Using bibliometric analysis, we quantitatively and qualitatively analyzed the country, institution, author, reference and keywords information of publications in JDTA, so as to understand the quantity, publication pattern and publication characteristics of these publications. The co-occurrence networks, clustering map and timeline map were created by CiteSpace and VOSviewer software to visualize the results. The CiteSpace was also used to analyze the strongest citation burst of keywords, which could describe the frequency, intensity and time period of high-frequency keywords, and indicate the research hotspots in the field. A total of 5 710 publications were analyzed, and their annual distribution number was basically stable from 2004 to 2023, fluctuating around 300. The United States and Italy led the way in terms of the number of publications, followed by France and China. For international cooperation, the developed countries represented by the United States cooperate more closely, but the cooperation was localized, reflecting that there was no unified model of autoimmunity among countries. UDICE-French Research Universities had the greatest number of publications. Subsequently, the number of publications decreased slowly with the ranking, and the gradient was not large. Eric Gershwin and Yehuda Shoenfeld stood out among the authors. They had an excellent academic reputation and great influence in the field of autoimmunity. The results of keyword analysis showed that JDTA publications mainly studied a variety of autoimmune diseases, especially SLE and RA. At the same time, JDTA publications also paid special attention to the research of cell function, autoantibody expression, animal experiments, disease activity, pathogenesis and treatment. This study is the first to analyze the publications in JDTA from multiple indicators by bibliometrics, thus providing new insights into the research hotspots and development trends in the field of autoimmunity.

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

Image detection technology is of paramount importance across various fields. This significance is not only seen in general images with everyday scenes but also holds substantial research value in the field of remote sensing. Remote sensing images involve capturing images from aircraft or satellites. These images typically feature diverse scenes, large image formats, and varying imaging heights, thus leading to numerous small-sized targets in the captured images. Accurately identifying these small targets, which may occupy only a few pixels, is a challenging and active research area. Current methods mainly fall into two categories: enhancing small target features by improving resolution and increasing the number of small targets to bolster training datasets. However, these approaches often fail to address the core distinguishing features of small targets in the original images, thus resulting in suboptimal performance in fine-grained classification tasks. To address this situation, we propose a new network structure DDU (Downsample Difference Upsample), which is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. At the same time, in order to take into account the recognition effect of targets of other sizes in the image, a new attention mechanism called PNOC (protecting the number of channels) is proposed, which integrates small target features and universal object features without losing the number of channels, thereby increasing the accuracy of recognition. And experimental verification was conducted on the PASCAL-VOC dataset. At the same time, it was applied to the testing of the fine-grained MAR20 dataset and found that the performance was better than other classic algorithms. At the same time, because the proposed framework belongs to a one-stage detection method, it has good engineering applicability and scalability, and universality in scientific research applications are good. Through comparative experiments, it was found that our algorithm improved the performance of the mAP by 0.7% compared to the original YOLOv8 algorithm.

Introducing Isotòpia: A stable isotope database for Classical Antiquity.

We present Isotòpia, an open-access database compiling over 36,000 stable isotope measurements (δ13C, δ15N, δ18O, δ34S, 87Sr/86Sr, 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, 207Pb/206Pb, and 208Pb/206Pb) on human, animal, and plant bioarchaeological remains dating to Classical Antiquity (approximately 800 BCE - 500 CE). These were recovered from different European regions, particularly from the Mediterranean. Isotòpia provides a comprehensive characterisation of the isotopic data, encompassing various historical, archaeological, biological, and environmental variables. Isotòpia is a resource for meta-analytical research of past human activities and paleoenvironments. The database highlights data gaps in isotopic classical archaeology, such as the limited number of isotopic measurements available for plants and animals, limited number of studies on spatial mobility, and spatial heterogeneity of isotopic research. As such, we emphasise the necessity to address and fill these gaps in order to unlock the reuse potential of this database.

Progressive prediction: Video anomaly detection via multi‐grained prediction

AbstractVideo Anomaly Detection (VAD) has been an active research field for several decades. However, most existing approaches merely extract a single type of feature from videos and define a single paradigm to indicate the extent of abnormalities. A coarse‐to‐fine three‐level prediction is built by integrating different levels of spatio‐temporal representations, better highlighting the difference between normal and abnormal behaviors. First, an object‐level trajectory prediction is proposed to model human historical position using a graph transformer network. Subsequently, skeleton‐level prediction is achieved by incorporating the positional information from the trajectory prediction. More importantly, based on the predicted skeleton, a skeleton‐guided pixel‐level region prediction is performed. A novel Skeleton Conditioned Generative Adversarial Network (SCGAN) is designed to explore the correlation between skeleton‐level and pixel‐level motion prediction. Benefiting from SCGAN, the prediction of human regions is contributed by both coarse‐grained and fine‐grained motion features. This three‐level prediction, namely Progressive Prediction Video Anomaly Detection (P3VAD), enlarges the prediction error on irregular motion patterns. Besides, a pixel‐level analysis method is proposed to achieve Background‐bias Elimination (BE) and denoise the predicted region. Experimental results validate the effectiveness of P3VAD on the four benchmark datasets (ShanghaiTech, CUHK Avenue, IITB‐Corridor, and ADOC).

Facial expression recognition (FER) survey: a vision, architectural elements, and future directions

With the cutting-edge advancements in computer vision, facial expression recognition (FER) is an active research area due to its broad practical applications. It has been utilized in various fields, including education, advertising and marketing, entertainment and gaming, health, and transportation. The facial expression recognition-based systems are rapidly evolving due to new challenges, and significant research studies have been conducted on both basic and compound facial expressions of emotions; however, measuring emotions is challenging. Fueled by the recent advancements and challenges to the FER systems, in this article, we have discussed the basics of FER and architectural elements, FER applications and use-cases, FER-based global leading companies, interconnection between FER, Internet of Things (IoT) and Cloud computing, summarize open challenges in-depth to FER technologies, and future directions through utilizing Preferred Reporting Items for Systematic reviews and Meta Analyses Method (PRISMA). In the end, the conclusion and future thoughts are discussed. By overcoming the identified challenges and future directions in this research study, researchers will revolutionize the discipline of facial expression recognition in the future.

Are we bridging the gap? A ten-year probe into NIH grants for early-career and independent investigators in oncology.

9010 Background: Ongoing gender disparities persist within National Institutes of Health (NIH) funding, specifically evident in NIH R01 grants within Oncology. A crucial aspect of addressing this issue involves a comprehensive investigation of discrepancies between early-career and independent principal investigators (PI). Here we present a ten-year analysis of gender distribution of early career vs. independent grants in the NIH under the field of Oncology. Methods: We implemented an R script utilizing NIH rePORTER, tidyverse, and janitor libraries for data retrieval. This script defined fields for retrieval which looped over fiscal years 2012 to 2022. The script executed API requests with criteria that included fiscal years, activity codes, and search terms that are relevant to oncology to extract grants from NIH rePORTER. We defined the early-career grant category to encompass the top 3 most active NIH K and F grants (K01,K08,K23, F31,F32,F33). In contrast, the independent research grant category included the top 3 most active NIH R grants (R01,R21,R35). The gender library was used to predict the gender of each principal investigator (PI) based on their first name. Ggplot and gtsummary packages were employed for data visualization and statistical analysis, automatically selecting appropriate tests based on data type. Results: Over the fiscal years 2012 to 2022, a total of 44,545 active early-career grants were funded by the NIH. Females (n = 24,243: 54.4% [95% CI:53.7%,55.0%]) surpassed their male counterparts (n = 20,302: 45.6% [95% CI:44.9%, 46.3%]) by holding more active early-career research grants. From 2012 to 2022, there was a significant upward trend that was noted in active early-career grants funded to females (1,818 to 2,969, p < 0.01). Similarly, males exhibited a noteworthy increase in these grants when comparing 2012 to 2022 (1,780 to 1,986, p < 0.001). Despite both genders experiencing an uptrend in active early-career NIH grants, females demonstrated a 48.3% increase, while males experienced a more modest 11.5% increase. In comparison, a total of 236,804 active independent research NIH grants were disbursed in the NIH R category from 2012 to 2022. Females (78,439: 33.1% [95% CI:32.7%,33.4%]) had fewer active independent NIH research grants compared to males (158,365: 66.9% [95% CI:66.6%,67.1%]). Males observed a 6.4% increase in independent NIH grants, rising from 14,419 to 15,343, while females exhibited a notable 48% increase, expanding from 6,193 to 9,195, from 2012 to 2022. Conclusions: In the realm of oncology, gender disparities are not evident among early career investigators as there were 53.7% of early career grants funded to females over the decade. Conversely, females accounted for only 33.3% of independent research grants. These observations emphasize the importance for targeted interventions to foster equal opportunities within the field of Oncology.

A Bright Future for Electrodeposition

The Electrodeposition Division, which was founded in 1922 as the second ECS division, celebrated their centennial anniversary at the 242nd ECS Fall meeting in Atlanta. For the occasion, the division organized several sessions with invited contributions to honor the achievements of 100 years of electrodeposition, but also to take a closer look at the present trendsetters and give a perspective on future challenges and opportunities in this thriving field. Our then newly established Electrodeposition Early Career Forum (ECF, founded in Spring 2022) organized a full day symposium with contributions from outstanding early-career researchers involved in cutting-edge research across a broad range of areas of active electrodeposition research. It turned out to be a fantastic day with invigorating talks full of ideas. The ELDP division decided to share some of the excitement with the ECS community and asked our ECF members to suggest topics and participate in the articles for this summer edition of Interface dedicated to Electrochemical and Electroless deposition. Dr. Kent Zheng, assistant professor at the University of Texas and Dr. Martin Leimbach, postdoc at TU Ilmenau, Germany, took up the challenge. Together with us, humble guest editors, four articles have been selected centred around three important topics: (1) electrodeposition for manufacturing and sustainability, edited by Prof. Luca Magagnin; (2) electrodeposition for energy applications, edited by Prof. Natasa Vasiljevic; and (3) new electrodeposition approaches extending the material library, edited by Prof. Philippe Vereecken.

Clinical, histopathological, and molecular biomarkers in a multicentric cohort of Mexican women with triple-negative breast cancer.

e13152 Background: Triple negative breast cancer (TNBC) is a heterogeneous entity with a marked variation in biological behavior, treatment responses, and patient outcomes. Thus, the investigation of clinical, histopathological, and molecular biomarkers is essential to understand this variability and potentially inform more effective management approaches across different patient profiles. Methods: We conducted a retrospective multicenter study of women >18 years diagnosed with TNBC between 2006 and 2021 in two Mexican centers. We systematically gathered clinical and histopathological data, including an assessment of H&E-stained slides for histopathological features and tumor-infiltrating lymphocytes (TILs), alongside immunohistochemistry analyses for androgen receptors (AR), CD8, and doublecortin-like kinase 1 (DCLK1). Statistical analyses were conducted using R version 4.3.2. Results: In our cohort, comprising 289 patients, the average age at diagnosis was 49.13 years, with a median overall survival (OS) of 63 months and a 5-year OS rate of 55%. The distribution of clinical stages at diagnosis was as follows: I (n=12, 4.2%), II (n=94, 32.5%), III (n=130, 45.0%), and IV (n=53, 18.3%). CD8 expression was higher in patients with no recurrence compared to patients with stage IV at diagnosis (p<0.001), and it decreased with advancing clinical stages. We also found statistically significant differences in OS between negative and positive CD8 expression, favoring the latter (5-year OS 51.8% vs. 72.2%, p=0.032). Disease-free survival (DFS) and OS also differed significantly between low (≤10%) and high (>10%) stromal TILs, favoring the high expression group (5-year DFS 51.9% vs. 70.8%, p=0.0066; 5-year OS 50.1% vs. 70.3%, p=0.0018). CD8 levels positively correlated with stromal and intratumoral TILs. Positive AR expression (>10%) was seen in 8.9% of cases, with no observed differences in DFS or OS according to negative or positive status (5-year DFS 56.0% vs. 72.2%, p=0.42; 5-year OS 55.0% vs. 60.3%, p=0.68). Finally, DCLK1 positivity (≥10%) was seen in 23% of cases, with no influence on survival (negative vs. positive status, 5-year DFS 58.5% vs. 53.8%, p=0.63; 5-year OS 54.6% vs. 58.1%, p=0.60). Conclusions: Our study underscores the prognostic significance of CD8 and TILs in predicting DFS and OS among TNBC patients. Notably, the expressions of AR and DCLK1 were not independently associated with survival, although their prognostic impact in combination with other biomarkers is still an area of active research. Overall, our findings advocate for a more detailed examination of the molecular characteristics of TNBC within our demographic, encompassing an expanded array of biomarkers and a thorough investigation of the tumor microenvironment.

Immunotherapy plus chemotherapy versus chemotherapy alone in perioperative treatment for locally advanced gastric cancer: A systematic review and meta-analysis.

e16083 Background: The standard care for locally advanced gastric (LAGC) or gastroesophageal junction (GEJ) cancer is perioperative chemotherapy (CTX) with surgery. The therapeutic landscape has evolved with PD-1 and PD-L1 inhibitors, showing promise in advanced or metastatic disease. Investigating their role in locally advanced disease is an active research area. In response, we conducted a meta-analysis to evaluate the effectiveness of perioperative immunotherapy with CTX in LAGC/GEJ patients. Methods: We systematically searched PubMed, Embase, and Cochrane for randomized controlled trials (RCTs) comparing perioperative PD-1 and PD-L1 inhibitors in combination with CTX versus CTX alone for LAGC/GEJ. Evaluated outcomes included pathological complete response (pCR), R0 resection rate, downstaging rate, and treatment-related adverse events (TRAE) with a grade ≥ 3. Heterogeneity was assessed using I2 statistics, employing a random-effects model for analysis. Statistical analyses were performed using R software version 4.3.2. Results: Among 2,452 patients from six RCTs, 1,228 (50.08%) received immunotherapy. Combining immunotherapy with CTX showed substantial improvements in pCR (RR 2.83; 95% CI 1.78-4.50; p < 0.001). This effect persisted in the FLOT CTX subgroup (RR 2.15; 95% CI 1.37-3.38; p < 0.001). Additionally, downstaging outcomes were superior in the immunotherapy group, with ypT0 (RR 1.96; 95% CI 1.50-2.55; p < 0.001) and ypN0 (RR 1.36; 95% CI 1.19-1.54; p < 0.001). However, no significant differences were observed in the R0 resection rate (RR 1.01; 95% CI 0.98-1.04; p = 0.603) and TRAE grade 3 or higher (RR 1.05; 95% CI 0.98-1.13; p = 0.177). Conclusions: These findings emphasize the effectiveness of combining PD-1 and PD-L1 inhibitors with CTX, indicating an improved pathological response compared to CTX alone. As expected, these positive outcomes persisted in the FLOT regimen. Our results provide evidence supporting the combination of immunotherapy and CTX as a perioperative treatment option for LAGC/GEJ patients.