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Statistical Analysis | cancer, death | CANCER, RELAPSE | Patients who underwent randomization and for whom residual serum samples were available for measurement of p53-Ab levels were included in this analysis. Relapse and death-related outcomes were assessed according to randomization group (ie, whether supplements were taken) as an intention-to-treat analysis. Effects of vitamin D and placebo on the risk of relapse or death were estimated using Nelson-Aalen cumulative hazard curves. A Cox proportional hazards model was used to determine hazard ratios (HRs) and 95% CIs. The p53-immunoreactive subgroup included patients who were p53-Ab (+) in serum and p53-IHC [3+] in cancer cells. To clarify whether vitamin D supplementation affected outcomes in the p53-immunoreactive subgroup but not in the non–p53-immunoreactive subgroup, | PMC10445201 |
Results | PMC10445201 | |||
Study Population | The study population consisted of 392 patients with levels of p53-Ab measured (260 males [66.3%]; mean [SD; range] age, 66 [10.7; 35-90] years), including 241 patients in the vitamin D group and 151 patients in the placebo group ( | PMC10445201 | ||
Study Flowchart | PMC10445201 | |||
Patient Characteristics | There was detectable p53-Ab in the serum of 142 patients (36.2%), constituting the p53-Ab (+) group, with levels ranging from 0.4 to 1890.0 U/mL, but not in the remaining 250 patients (63.8%), constituting the p53-Ab (−) group. Characteristics of participants in these groups are shown in the | PMC10445201 | ||
Patient Characteristics | Abbreviations: 25(OH)D, 25-hydroxy vitamin D; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); p53-Ab, anti-p53 antibody.≥0.4 U/mL.<0.4 U/mL.Characteristics of patients who were p53-Ab (+) stratified by intervention are shown in the eTable in | PMC10445201 | ||
Subgroup Analysis of Patients With and Without p53-Ab in Serum | death | In the p53-Ab (+) group, relapse or death occurred in 17 of 86 patients (19.8%) in the vitamin D group and 19 of 56 patients (33.9%) in the placebo group; 5-year RFS was 26 patients (77.2%) in the vitamin D group and 10 patients (60.0%) in the placebo group, indicating no significant difference (HR, 0.57; 95% CI, 0.30-1.10) ( | PMC10445201 | |
Subgroup Analysis of Patients With and Without Serum Anti-p53 Antibody (p53-Ab) | death | Nelson-Aalen cumulative hazard curves for relapse or death in p53-Ab (+) and p53-Ab (−) groups, assessing patients taking vitamin D vs those taking placebo, were compared using hazard ratios and 95% CIs. | PMC10445201 | |
Serum Anti-p53 Antibody (p53-Ab) Levels by p53 Immunohistochemistry (IHC) Protein Expression | IHC levels were 0 (<1% strong or faint p53 cells in cancerous region; p53-IHC [0]), 1 (1%-10% strong p53 cells in cancerous region; p53 IHC [1+]), 2 (>10%-99% strong p53 cells in cancerous region; p53 IHC [2+]), and 3 (>99% overexpressed p53 cells in cancerous region; p53 IHC [3+]). | PMC10445201 | ||
Analyses in p53-Immunoreactive Subgroup | death | In 80 patients in the p53-immunoreactive subgroup (ie, patients who were p53-Ab [+] and p53-IHC [3+]), relapse or death occurred in 9 of 54 patients (16.7%) in the vitamin D group and 14 of 26 patients (53.8%) in the placebo group; the 5-year RFS was significantly higher in the vitamin D group (13 patients [80.9%]) than the placebo group (1 patient [30.6%]; HR, 0.27; 95% CI, 0.11-0.61; | PMC10445201 | |
Analysis of p53-Immunoreactive Subgroup | death | IHC indicates immunohistochemistry; p53-Ab, anti-p53 antibody. Nelson-Aalen cumulative hazard curves are presented in the p53-Ab (+) and p53-IHC (3+) subgroup, p53-Ab (−) or p53-IHC (2+, 1+, or 0) subgroup, p53-Ab (+) and p53-IHC-positive subgroup, and p53-Ab (−) or p53-IHC-negative subgroup. Hazard curves for relapse or death in patients taking vitamin D were compared with those taking placebo.To conduct a sensitivity analysis using a cutoff p53-IHC value of 10% instead of 99%, patients were divided into 2 groups of p53-IHC (+), or greater than 10%, and p53-IHC (−), or 10% or less. In the p53-Ab (+) and p53-IHC (+) group, relapse or death occurred in 10 of 59 patients (16.9%) in the vitamin D group and 17 of 34 patients (50.0%) in the placebo group; the 5-year RFS was significantly higher in the vitamin D group (14 patients [80.7%]) than the placebo group (2 patients [37.4%]; HR, 0.31; 95% CI, 0.14-0.67; estimated power > 0.99) ( | PMC10445201 | |
Discussion | cancer, death | MUTANT, CANCER | In this post hoc analysis of the AMATERASU RCT, p53-IHC grades showed a positive association with serum p53-Ab levels. Additionally, p53-Ab levels were significantly higher in patients who were p53-IHC (+) than those who were p53-IHC (−). Previously, mutant p53 protein that accumulated in cancer cells was reported to be immunogenic and to lead to the generation of p53-Ab in vitro.The main findings of this study were that daily supplementation of 2000 IU of vitamin D reduced the risk of relapse or death by 27% compared with placebo in the p53-immunoreactive subgroup, defined by positivity for p53-Ab in serum and p53 protein in more than 99% of cancer cells. Results indicated interactions between vitamin D supplementation and p53-immunoreactive subgroup even after Bonferroni correction. Moreover, results showed a similar risk reduction by shifting the cutoff point of p53-IHC from more than 99% to 10% for sensitivity analysis. As preliminary evidence, we previously reported that vitamin D reduced the risk of relapse or death by 52% compared with the placebo group in the p53-IHC (+) cancer subgroup in a post hoc analysis of the same AMATERASU trial. | PMC10445201 |
Limitations | This study has several limitations. First, this was a post hoc analysis of the AMATERASU RCT, and the number of patients in the p53-immunoreactive subgroup was very small. Second, because analyses assessed a post hoc hypothesis, observer error or bias could have influenced results. Thus, the findings must be considered exploratory and interpreted with caution, although the main results of this study were remarkable and remained significant even after Bonferroni correction. Third, mutations of the | PMC10445201 | ||
Conclusions | digestive tract cancer, cancer, death | CANCER | This post hoc analysis of an RCT found that vitamin D supplementation reduced the risk of relapse or death in the subgroup of patients with digestive tract cancer who were p53 immunoreactive. Findings suggest the importance of developing cancer immunotherapy targeting mutated p53 proteins. | PMC10445201 |
Background | infection, sepsis, Sepsis | INFECTION, SEPSIS, SEPSIS, DYSREGULATED HOST RESPONSE | Sepsis occurs as a result of dysregulated host response to infection. However, cytokine adsorption therapy may restore the balance of proinflammatory and anti-inflammatory mediator responses in patients with sepsis. This study aimed to determine the cytokine adsorption ability of two different types of continuous renal replacement therapy (CRRT) hemofilters for polyethyleneimine-coated polyacrylonitrile (AN69ST) (surface-treated) and polymethylmethacrylate (PMMA) CRRT. | PMC10314474 |
Methods | sepsis | SEPSIS, SECONDARY | We performed a randomized controlled trial among sepsis patients undergoing CRRT, who were randomly assigned (1:1) to receive either AN69ST or PMMA-CRRT. The primary outcome was cytokine clearance of hemofilter adsorption (CHA). The secondary endpoints were the intensive care unit (ICU) and 28-day mortalities. | PMC10314474 |
Results | tumor necrosis | TUMOR NECROSIS | We randomly selected 52 patients. Primary outcome data were available for 26 patients each in the AN69ST-CRRT and PMMA-CRRT arms. The CHA of high-mobility group box 1, tumor necrosis factor, interleukin (IL)-8, monokine induced by interferon-γ, and macrophage inflammatory protein were significantly higher in the AN69ST-CRRT group than in the PMMA-CRRT group ( | PMC10314474 |
Supplementary Information | The online version contains supplementary material available at 10.1186/s40001-023-01184-6. | PMC10314474 | ||
Keywords | PMC10314474 | |||
Background | Sepsis, critically ill, organ dysfunction, infection, AKI | SEPSIS, CRITICALLY ILL, INFECTION, DYSREGULATED HOST RESPONSE, ACUTE KIDNEY INJURY | Sepsis, which is a life-threatening organ dysfunction, is caused by the dysregulated host response to infection [Acute kidney injury (AKI) is significantly associated with a high mortality rate in critically ill patients [ | PMC10314474 |
Methods | PMC10314474 | |||
Trial design and patients | EMERGENCY | This pilot open-label RCT was conducted at the Tertiary Emergency and Critical Care Center of Fukuoka University Hospital (Fukuoka, Japan) according to the Declaration of Helsinki. Our emergency and closed ICU has 32 beds. The trial was registered at the University Hospital Medical Information Network (UMIN000029450), and its protocol was previously published ( | PMC10314474 | |
Interventions and procedures | ® | The PMMA and AN69ST groups were defined based on the PMMA and AN69ST membranes. CRRT was initiated immediately after ICU admission. The patients were randomly assigned (1:1) to the AN69ST or PMMA groups. Random numbers were generated using the Excel random function (Microsoft Japan Co., Ltd., Tokyo, Japan), and patients were then randomly assigned to groups according to the hemofilter used.All patients were randomized immediately following ICU admission, and CRRT was initiated in the ICU. CRRT was performed using ACH-10® or ACH-Σ® (Asahi Kasei Medical Co., Ltd., Tokyo, Japan). The hemofilters used were an AN69ST (sepXiris150; Baxter Co. Ltd., Tokyo, Japan) or a PMMA membrane (Hemofeel CH1.5N; Toray Medical Co., Ltd., Tokyo, Japan). All CRRT modes were continuous hemodiafiltration. The operating conditions were as follows: quantity of blood flow (QB), 80–100 mL/min; dialysate flow rate, 500 mL/h; and filtration flow rate (QF), 300 mL/h. Sublood-BS (Fuso Pharmaceutical Industries, Osaka, Japan) was used as both the dialysate and replacement fluid. Nafamostat mesylate (NM) (Asahi Kasei Pharma Corp., Tokyo, Japan) was administered as an anticoagulant, and the dose was maintained in the range of 0–30 mg/h. The activated clotting time after hemofiltration was maintained at > 180 s, and it was measured using the Hemochron Response (Heiwa Bussan, Co. Ltd., Tokyo, Japan). NM is a protease inhibitor that strongly inhibits the activity of various coagulation enzymes [ | PMC10314474 | |
Data and sample collection | AKI, infection, septic shock | INFECTION, SEPTIC SHOCK, CORONAVIRUS, BLOOD, SEVERE ACUTE RESPIRATORY SYNDROME | The baseline data, including patients’ characteristics such as age, sex, comorbidities, AKI stage on admission, source of infection, and detected microorganism (COVID-19 was diagnosed based on the detection of severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] on reverse transcription-polymerase chain reaction or SARS-CoV-2 antigen from a nasopharyngeal swab sample), diagnosed septic shock [Blood samples and filtrates were drawn from the extracorporeal circuit at the inlet and outlet of the hemofilter 2–6 and 12–24 h after initiating CRRT (circuit schema is shown in Additional file TNF-α, IL-1β, IL-6, IL-8, IL-10, MIG, and MIP-1α levels were measured using a HISCL-5000 (Sysmex Co., Kobe, Japan) [ | PMC10314474 |
Outcomes | ADVERSE EVENTS | The primary outcome was cytokine CHA. Plasma cytokine clearance was calculated according to the following formula [Secondary endpoints included blood cytokine levels upon admission to the ICU and on days 2–4 and 5–7 after ICU admission, ICU mortality, 28-day all-cause mortality, VAI [Safety and feasibility outcomes included the number of patients with serious adverse events and reactions in both arms. | PMC10314474 | |
Statistical analyses | REGRESSION | Data are presented as medians (interquartile ranges) for continuous variables and percentages for categorical variables. We used the Wilcoxon, Steel–Dwass, and Chi-square or Fisher’s exact tests for comparing two groups of continuous variables, multiple comparisons between continuous variables, and comparing categorical variables, respectively. Furthermore, ICU and 28-day mortality rates were analyzed using multivariate logistic regression, and the explanatory variables were age and SOFA score. The data were analyzed using the statistical software JMP12 for Windows (SAS Institute Japan, Tokyo, Japan). Results were considered statistically significant at P-values less than 0.05. Since this was a pilot study, a sample size estimation was not conducted. | PMC10314474 | |
Results | PMC10314474 | |||
Safety and feasibility outcomes | ADVERSE EVENTS | No serious adverse events were observed in either group (Additional file | PMC10314474 | |
Discussion | sepsis | SEPSIS | To the best of our knowledge, this study is the first RCT to evaluate the difference in cytokine CHA between the AN69ST and PMMA hemofilters in a clinical setting. We found that AN69ST and PMMA membranes had significantly different cytokine CHA in patients with sepsis in different time points at 2–4 h and 12–24 h after CRRT initiation (Table The AN69 membrane is an electronegative copolymer of acrylonitrile and sodium methanesulfonate. AN69 can undergo adsorption in the membrane bulk through electrostatic interaction. In contrast, AN69ST was achieved by neutralizing the surface in contact with blood by ionic grafting of a polycationic polymer in AN69; however, AN69ST can also be adsorbed in the membrane bulk through electrostatic interaction [An in vitro closed-loop circulation system study showed that time-dependent changes of transmembrane pressure (TMP) were not observed but time-dependent superiority for CHA ability was observed in AN69ST membrane in comparison with PMMA for HMGB1 [In contrast, PMMA membranes have a higher CHA ability for IL-6 than for AN69ST membranes (Table Cytokine levels were significantly decreased in both the AN69ST and PMMA groups (Fig. Some observational studies [ | PMC10314474 |
Strengths and limitations | septic | The obvious strength of our study is the use of randomization to minimize selection bias. However, this study has some limitations. First, blinding of the interventions was not performed. Second, because this was a pilot, single-center study, generalizability is insufficient. Third, the present study did not have a control group that was not treated with CRRT. Therefore, this study did not provide information about endogenous clearance rates in septic patients, indicating that part of the decreased cytokine levels in blood may not depend on CRRT. Fourth, the sampling time windows (2–6 h and 12–24 h) were relatively wide. However, no significant differences in sampling time windows were observed between the two groups (Table | PMC10314474 | |
Conclusions | sepsis | SEPSIS | Our first pilot RCT showed that AN69ST and PMMA hemofilters have different cytokine CHA ability in patients with sepsis. However, no significant difference was observed in the present pilot clinical study. Therefore, these two hemofilters may have to be used depending on the target cytokine. | PMC10314474 |
Acknowledgements | We would like to thank Editage ( | PMC10314474 | ||
Author contributions | YN, FK, TH, and HI designed the study. HH, SY, KY, KH, and YK performed sample collection and input data. FK advised on statistical findings. YN generated the random allocation sequence; YN, KH, and YK enrolled participants; and HH, SY, and KY assigned participants to interventions. YN, HH, and SY contributed to the statistical analysis. YN, HH, and SY wrote the first draft. All authors critically revised the report, commented on the drafts of the manuscript, and approved the final report. | PMC10314474 | ||
Funding | Part of this study was supported by Sysmex Corp, which played no role in the study and measured cytokine and HMGB1 concentrations. Furthermore, this study was supported by a grant from the Clinical Research Promotion Foundation (2021). | PMC10314474 | ||
Availability of data and materials | The data that support the findings of this study are available from authors, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Medical Ethics Review Board of Fukuoka University. | PMC10314474 | ||
Declarations | PMC10314474 | |||
Ethics approval and consent to participate | This study was approved by the Medical Ethics Review Board of Fukuoka University (approval number: 2017M089) and was performed in line with the principles of the Declaration of Helsinki. All patients or legal representatives provided informed consent. | PMC10314474 | ||
Consent for publication | All patients or legal representatives approved this publication. | PMC10314474 | ||
Competing interests | The authors declare that they have no competing interests. | PMC10314474 | ||
References | PMC10314474 | |||
1. Introduction | ’ | Cities attracting large numbers of tourists increasingly face crowding and public resistance to tourism growth. As a result, governments strive to spread tourists from the best-known attractions to less-visited locations to improve both residents’ and tourists’ quality of life. Evidence of success and best practices herein is largely anecdotal, and the effects on tourist experience are also unknown. Thus, we undertook a randomized 2 × 2 experiment in the province of Overijssel (The Netherlands), wherein tourists staying at vacation parks near small and mid-sized cities were exposed to information which emphasized attractions in either heavily visited or less-visited areas. Participants were also assigned to receive the information in either a passive or a conversational form. Location and daily emotion, as well as experience evaluation on the last day of the vacation, were recorded via mobile platforms. We found that tourists receiving information on attractions in less-visited areas engaged in significantly more movements around these attractions, and significantly less around heavily visited areas. The conversational form of information delivery was more positively evaluated than information delivered passively. Furthermore, vacation experience emotions and evaluations were largely unaffected. Thus, it is clearly possible to direct tourists to less-crowded locations without negatively affecting their vacation experiences.Cities that attract large numbers of tourists increasingly face crowding and public resistance to tourism growth. This phenomenon is best-documented, though not limited to, cities, and has been termed An alleged mechanism of tourist spatial behavior is information that tourists consume. This information includes guidebooks, review sites, and social media. It is believed that the most-used information sources tend to echo one another, steering tourists to a relatively small number of attractions. At the same time, other places where tourist could have enjoyable experiences, remain undiscovered and suffer, to some degree, from undertourism. Important in existing visions for resetting tourism after the pandemic is bringing these under-/and over-visited places more into balance. For example, the Destination Management Organization (DMO) of The Netherlands, called The Netherlands Board of Tourism and Conventions (NBTC), wrote in 2019, thus based on pre-pandemic tourism levels, that “Heading up to 2030, we expect a 50 percent increase in the number of international tourists. This requires a new approach that prioritises the common interests of visitors, companies, and residents. The goal is for every Dutch person to benefit from tourism. Five priorities are central for achieving this ambition, [including to] put more cities and regions on the map as attractive destinations” [Therefore, in the present study, we aimed to test an intervention to inform tourists about attractions in less-visited areas, in contrast to attractions in heavily visited areas. How information provided by DMOs affects tourist flows is generally unknown, and no studies exist as far as we could find that have manipulated experimentally and then tracked spatial behavior by tracking tourists’ locations. We carried this intervention out over two distinct information channels to uncover how information | PMC10138512 | |
2. Literature Review | PMC10138512 | |||
2.1. Tourism Flows and Overtourism | Knowledge about the impacts of tourism flows on host resident communities is nearly as old as academic research of tourism. In fact, publications such as Turner and Ash’s Golden Hordes [At the same time, the term “overtourism” appeared increasingly often in academic journals [Previous research thus demonstrates that economic motivations to increase tourist flows and societal motivations to reduce or redirect them have long been in conflict. Unfortunately, academic research on overtourism has mostly extended the tradition in tourism impacts research of precisely describing and explaining problems rather than proposing and testing solutions. Most proposed solutions from tourism scholars, such as community-based creative tourism, have merely niche appeal [Potential solutions have emerged from both destination management as well as technological arenas. As an example of destination management, Venice has decided to charge an admission fee to enter the city. Recent visual scenario-based research of crowding in Amsterdam showed wide-ranging support for an entry fee to that city [The pandemic restrictions of 2020 and 2021 highlighted the importance of information as a possible method of altering tourist flows. While tourism demand in urban environments declined, social distancing meant that any existing visitor flows needed significantly more physical space. Once again, solutions emerged from both destination management as well as technological arenas. Several destinations such as Texel, an island in The Netherlands, created apps which not only provided information about possible attractions, but also indicated how busy they were, with the goal of spreading tourist flows away from the busiest locations. Google Maps has also provided this functionality across an increasing variety of location types (trains, cafes, public spaces). Furthermore, new communication channels such as short-format video and chatbots have been adopted by destinations. Among the most promising of these have been conversational recommender systems, a technical innovation which has been applied to tourist information [ | PMC10138512 | ||
2.2. Conversational Recommender Systems | A conversational recommender system occupies the space between a completely personal conversation with a human being and a completely automated conversation with a chatbot. While personal conversations have long been a staple of DMOs via brick-and-mortar offices, phone lines, and direct messaging on social media, they comprise an expensive information method. Chatbots, on the other hand, quickly fail and frustrate recipients when questions become intricate or unusual. A recommender system is defined by [Recent improvements in recommender systems have come from artificial intelligence. Recommender systems become more complete, accurate and user friendly [The content from which Travel with Zoey generates responses is stored in a digital catalogue. Each attraction in this catalogue is tagged with identifiers such as ‘restaurant’, ‘vegetarian’, and ‘family’, facilitating an optimal match between tourist and content. An unsolicited message with attraction suggestions is sent every morning, but Travel with Zoey also responds to request by the customers themselves. Behind the scenes, the delivery of the content is mostly but not fully automated, so that human employees of Travel with Zoey are involved in making each interaction more personalized than a chatbot could. There is an emphasis on empathy, wherein Travel with Zoey aims to make customers feel heard and attended to. Finally, any experiences at attractions that customers talk about in their conversation with Zoey are taken by Travel with Zoey employees as input for optimizing future recommendations. As for other recommender systems, the goal is to not only provide a quality experience in consuming destination information, but to improve the tourist experience as a whole [ | PMC10138512 | ||
2.3. Tourist Experience | While a comprehensive review of tourist experience is beyond the scope of this article see [There is remarkably little known about how | PMC10138512 | ||
2.4. Our Study—Justification and Approach | ’ emotions | While the technical and user experience value of a recommender system such as Zoey have been well documented in previous research, the application of this technology to overtourism issues has not been studied. If information interventions from recommender systems or more conventional sources actually causes tourists to travel elsewhere, it is likely to affect their emotional and evaluated experiences, but this assumption remains untested. Tourists’ emotions drive their evaluations, however, which in turn drive important destination outcomes such as behavioral intentions [In this project we utilized Travel with Zoey as a conversational recommender system to deliver destination information to a group of visitors to Overijssel, a province in the east of The Netherlands. We compared its effectiveness to that of a default, passive map-based mobile application. Furthermore, we investigated the effectiveness of prioritizing attractions based on the policy of the DMO to direct visitors to certain places while reducing the pressure on others. We were guided by the following question:Does a conversational recommender system, as compared to passive map-based mobile applications, spatially direct tourists to less-visited areas, and how are their vacation experiences and evaluations changed as a result?More specifically, we address the following sub-questions in our study:To what extent does tourists receiving different attraction information (attractions in heavily-visited vs. less-visited areas), from different sources (conversational vs. passive),differ in their spatial movements?visit different types of attractions?experience different self-reported emotions day-to-day?visit different proportions of urban and rural destinations?evaluate their experiences differently? | PMC10138512 | |
3. Materials and Methods | PMC10138512 | |||
3.1. Study Design | 342,273 | As we wanted to measure the specific and direct effects of conversational recommender systems and the information therein on vacation behaviors and experiences, we used a an experimental design with random assignment to intervention conditions, which is the only research design that supports conclusions about an intervention Popularity-driven information via a conventional passive map app;Policy-driven information via a conventional passive map app;Popularity-driven information via a personalized conversation on WhatsApp;Policy-driven information via a personalized conversation over WhatsApp;Given that DMOs have traditionally informed tourists about attractions based on feedback about attraction popularity, and that numerous larger DMOs boast a passive map-based app as one of their information channels, the popularity-driven passive condition approximates a control condition in our design. It is challenging to study interventions on tourism experiences in the field with meaningful control conditions, as the experience must remain intact. As tourist experiences without information interventions, such as the use of a conversational recommender or purely policy-driven attraction information, still often feature passive map-based apps with destination information, we argue that this condition comprises a plausible control condition.Information about attractions in the region were based on a database of approximately 400 attractions provided by Marketing Oost, the DMO for the province of Overijssel. Marketing Oost categorized the attractions based on priority. The best attractions were to be highlighted as having priority, while other attractions were displayed to participants without distinction. Which attractions were considered ‘best’, however, differed in location based on experimental condition. A different, mutually exclusive set of attractions was given priority in the popularity-driven conditions and in the policy-driven conditions. For popularity-driven participants, attractions judged by Marketing Oost as relatively higher quality, and located in the As a region benefiting from tourism, but also facing intense tourist flows in a handful of heavily visited areas, Overijssel is an ideal context for the present study. This province of 1.16 million inhabitants is located in the northeast of The Netherlands, and borders Germany to the east and other Dutch provinces to the north, south, and west. The province covers a total of 342,273 hectares, of which 6773 (1.98%) are covered by recreational areas and 34,474 (10.07%) by forest. While existing discourse about overtourism largely concerns cities, especially large, internationally famous historic city centers such as Venice or Amsterdam, the situation is somewhat inverse in Overijssel. While there are several Hanseatic historic city centers which attract tourists, such as Zwolle (2021 population 129,840) and Deventer (2021 population 101,236), these cities are considered relatively less-visited areas by Marketing Oost for the purpose of the present study. In contrast, the town of Ootmarsum (2021 population 4460), the village of Giethoorn (2021 population 2805), and several natural and agricultural landscapes such as the Vecht valley and the Sallandse Heuvelrug National Park comprise the heavily visited areas of Overijssel. Thus, the policy to spread tourists away from heavily visited areas in Overijssel corresponds with the goal to increase visits to urban areas. In contrast, a region such as Amsterdam might indicate fewer tourists in urban areas as a policy goal to reduce crowding.The experiment featured a during- and post-intervention measurement design. Participants were asked to fill in an intake questionnaire, including demographics and informed consent, approximately one month before their vacation. Location and emotions were measured daily during the vacation. Finally, on the last day of their vacation, participants were asked to evaluate the information source to which they were assigned (passive app or conversational recommender) and their vacation as a whole. | PMC10138512 | |
3.2. Sample | MAY | We used an availability sampling approach during two waves of data collection in May 2021 and late July/early August 2021. For field research in tourism contexts, availability samples being used as probability samples is practically impossible due to the lack of a sampling frame. Even if a sampling frame such as all visitors entering a region were practically possible, not only do tourist behaviors and experiences differ season to season, but also year to year. That makes a true probability sample of tourist experiences not only practically, but also conceptually unattainable.Participants were approached based on the criterion of booking a vacation at one of 10 (May) or 8 (August) vacation parks that chose to cooperate with the project. These parks are commercial operations which offer varying mixes of camping and bungalow accommodations, occasionally with dining, basic shopping, and activity facilities. A lottery to win back the cost of one’s vacation accommodation was offered as an incentive. If would-be visitors to these parks agreed to participate, we sent them an intake questionnaire including a statement of informed consent. The intake survey assessed demographics. Five days ahead of the beginning of their vacation, they received instructions for connecting with their assigned information source, comprising of either adding a WhatsApp contact with Travel with Zoey (conversational conditions), or downloading the appropriate mobile application, either Nienke’s Tips (passive popularity-driven condition) or Saar’s Tips (passive policy-driven condition). They were also instructed to download and install a GPS tracking application, Sesamo, which additionally notified them every evening to fill out a daily self-response questionnaire. Besides measuring self-reported emotions, this questionnaire asked them if the current day was the last day of their vacation. If so, experience evaluation measures were displayed as well. The final sample size varied by the data sources used in each analysis; 268 participants filled in at least one daily questionnaire, while 155 of these provided GPS data and 132 responded to the last-day questionnaire including experience evaluations. | PMC10138512 | |
3.3. Measures | PMC10138512 | |||
3.3.1. Location | We tracked location using GPS via the Mobidot mobile application Sesamo. Sesamo passively tracks location in the background using variable frequency based on speed of movement, with a granularity of one second. Accordingly, the application has a low burden on battery and CPU usage of mobile devices. After installing the application, it required no further input from the participants. Data were processed by matching with street and transportation network where possible. Resulting data comprised over 800,000 geographic coordinate pairs.Due to the variable frequency of recording, it is important to keep in mind that the data as analyzed are representative of participant location in terms of their | PMC10138512 | ||
3.3.2. Self-Reported Emotion | POSITIVE | We asked participants to report on their daily emotions using a modification of the Scale of Positive and Negative Experience SPANE, [While various emotion scales differ in the specific list of emotions presented, the SPANE is brief and well-balanced between positive and negative emotions. There are four general emotion terms in the SPANE which may not be strictly measuring emotion (“Good”, “Bad”, “Pleasant”, and “Unpleasant”) and were thus omitted. An emotion known to be important in tourism experiences, “positively surprised”, was added. We averaged the five positive and four negative emotion items together into positive and negative emotion indices. Positive emotion items were internally consistent (Cronbach alpha = 0.86; Revelle’s omega = 0.88). Negative emotion items were less internally consistent, in line with the very low variation normally present on negative emotion items in many tourism experiences (Cronbach alpha = 0.62; Revelle’s omega = 0.69). The low internal consistency in this case can specifically be attributed to the item “afraid”. We chose to retain this item as removing it improved the internal consistency only somewhat, and it is a conceptually important negative emotion. | PMC10138512 | |
3.3.3. Experience Evaluation | We measured four dimensions of experience evaluation using single-item measures based on the Net Promotor Score item on intent to recommend [While these item address participants’ evaluations of specific dimensions of their vacation, they do not cover the experience evaluation of the vacation as a goal. To that end, we used a single item with the same 0-to-10 Likert-type response scale, this time asking participants to give their vacation as a whole a grade, with 0 being the worst possible, and 10 being the best possible. This scale is familiar to Dutch participants as the grading scale used for school exams, and has been successfully validated in measuring experience evaluations in The Netherlands in previous research [ | PMC10138512 | ||
3.4. Analyses | We analyzed the data in five stages. First, we described the experience and evaluation variables for the sample as a whole. Then, we examined differences on experience and evaluation between the four conditions (popularity-driven/passive, policy-driven/passive, popularity-driven/conversational, policy-driven/conversational) using conditional group means and one-way analysis of variance. The third and fourth stage of data analysis aimed to assess if participants in different conditions visited different locations.In the third stage, we processed GPS data by first eliminating any data points which were not between date of arrival and date of departure or located outside of Overijssel. To analyze spatial patterns of participant distribution we transformed the participant point locations captured by GPS to continuous density representation using bivariate kernel density estimates [The last stage of data analysis involved modeling spatial movements of participants within spatial buffers generated around the attractions (20 m for point attractions, such as museums, restaurants, and monuments and 100 m for area-type attractions, such as parks or playgrounds) as a function of experimental condition. In other words, we modeled the odds that each datapoint collected came from a participant in one of the experimental conditions. Data were nested within participants, as both emotions and spatial behavior tend to be autocorrelated within participants. A multilevel logit model was used. We ran three models with different types of attractions as the outcomes: non-priority attractions, attractions which had popularity-driven priority, and attractions which had policy-driven priority. Finally, we also used these attraction movement variables as predictors of emotions to explore if participants enjoyed their vacation more on days when they engaged in more movements at a specific type of attraction.The popularity-driven passive condition was the reference group for all analyses, meaning all other conditions were always compared to this one, because a popularity-driven passive map-based application is the current default option used by many DMOs, and is thus analogous to a control condition in the present study. For smaller DMOs which do not have their own app, printed materials and conversations in tourist information centers also tend to focus on maps with certain attractions highlighted. Thus, the coefficients of models explaining participant movements based on experimental condition express the differences in movements between participants in the popularity-driven passive condition and each other condition in turn. | PMC10138512 | ||
4. Results | PMC10138512 | |||
4.1. Descriptive Statistics | fits | RECRUITMENT | An initial group of 269 participants filled in the recruitment form and intake questionnaire. Random assignment led to a relatively even division across the four experimental groups (popularity-driven passive n = 71; policy-driven passive n = 65; popularity-driven conversational n = 61; policy-driven conversational n = 72). Of these, 268 filled in at least one daily questionnaire and 132 filled in a daily questionnaire on the last day of their vacation. The responses between the different questionnaires presented to each participant do not overlap fully. The sample as measured by the intake questionnaire was over three-quarters female (76%) with a mean age of 44 years (sd = 11 years). About two-thirds (65%) of participants possessed bachelor or higher degrees of education, while the remaining 35% had vocational degrees. A large majority went on vacation with either their partner (14%) or family (79%). The average age fits almost exactly with previous national research on visitors to Overijssel (M. Kompanje, personal communication, date). Participants had booked their stay for an average of 9.33 days (sd = 5.54).Participants generally enjoyed their vacations, the destination, and the destination information source to which they were assigned. On average participants graded their vacation with a 7.77 (sd = 1.25) and were quite likely to recommend their accommodation (mean = 8.10, sd = 1.67) and Overijssel (mean = 8.38, sd = 1.15). They were also mildly positive about the information source to which they were assigned (mean = 6.08, sd = 2.71). Average daily positive emotions were approximately normally distributed, with a mean of 3.19 on a 5-point scale (sd = 0.58). Negative emotions were extremely positively skewed, as usual for tourism datasets, with very few participants reporting much of any negative emotion at all (mean = 1.31, sd = 0.28). | PMC10138512 |
4.2. Differences between Groups | There were remarkably few differences in experiences and outcomes between groups. The groups were statistically similar in positive emotions on vacation (F = 1.028, There were large differences between groups in evaluations of destination information sources. The passive app with either kind of tips earned about a 5 on an 11 point scale ranging from 0 to 10. The conversational recommender, on the other hand, earned a 6.7 (popularity-driven) to 7.3 (policy-driven) on the last day of vacation (F = 10.11, | PMC10138512 | ||
4.3. Spatial Distribution between Groups | As a whole, participants moved around the most near their accommodations, but spread out over the entire province of Overijssel, including all its major cities and motorways as well as side roads. On a map showing locations visited by at least one participant from each group (Unlike differences in reported experience and evaluations, the differences between groups in terms of where they went were clear and striking. We created maps showing locations where only one of the four groups was present (Maps of kernel density differences showed that policy-driven participants made more movements west of Ommen and in the municipalities of Rijssen-Holten and Hardenberg. Popularity-driven participants, on the other hand, moved around more just east of Ommen, just west of Almelo, and on the north side of Zwolle. (Dividing popularity-driven and policy-driven participants into two separate maps, we examine differences between the recommender systems. Popularity-driven participants using the passive app were more present in Enschede, Raalte, and Staphorst, comprising 57% of the visited locations on this map, while they were more present around Deventer, Rijssen-Holten and south of Hardenberg if used the conversational recommender, comprising 43% of the visited locations on this map (Policy-driven participants, on the other hand, showed almost the opposite pattern. They were present south of Hardenberg and around Tubbergen if using the passive app, comprising just 21% of the movement locations on this map, but more present around Raalte, Rijssen-Holten, and Ommen if using the conversational recommender, an overwhelming 79% of movement locations (Statistical models assessing number of points at attractions of various types (non-priority, priority policy-driven, and priority popularity-driven) as a function of group confirms that tourists in different groups not only went to different places but went to the At policy-driven attractions, participants getting policy-driven information were 1.8 (passive) to 2.0 (conversational) times as likely to be recorded at policy-driven attractions, as participants getting popularity-driven information by passive app. Interestingly, conversational recommender users getting popularity-driven information were | PMC10138512 | ||
4.4. Experience over Space | SAID | There were no significant effects of aggregated daily movements at different attraction types on daily positive emotions, within participants. There were modest connections between spatial behavior and negative emotions, however. Days when participants spent relatively more movements at non-priority attractions featured more negative emotions. In contrast, days with more movements at popularity-driven locations featured fewer negative emotions. Thus, it could be said that the most negative days had the most movement at non-priority attractions, while the least negative days had the most movement at popularity-driven attractions, on average within participants ( | PMC10138512 | |
5. Discussion | PMC10138512 | |||
5.1. Theoretical Implications | Our findings show that intervening on information changes tourists’ spatial behavior substantially, but has only limited implications for their experiences, extending existing knowledge in several ways. First, we assert that information provided to tourists firmly belongs in the theoretical discussion about causes and consequences of overtourism. While much has been written about the macro-level social forces that cause a destination to be crowded and bring its local residents into conflict with tourists [Second, we show that addressing crowding using an information intervention is moderately more effective with a conversational recommender, which is also much more positively evaluated, than with a passive map-based app. While positive experiences of personalized recommender systems in tourism were previously documented [Third, our study contributes evidence of modest variation in tourist experience over space on the temporal scale of differences between days over an entire vacation. Previous studies showed that single tourist experience episodes (e.g., city walks; guided tours) vary over urban space [Correspondingly, vacations were evaluated equally no matter of which attractions tourists were informed. Thus, our findings dispel the persistent myth in destination management that promoting only lesser-visited attractions will ‘ruin’ a vacation. We surmise that this may be due to novelty, the experience that vacation surroundings are new and different from everyday life. This explains a substantial proportion of the positive emotion boost people experience during vacationing [ | PMC10138512 | ||
5.2. Professional Implications | Our findings suggest that the current trend to shift destination management policy from attracting as many tourists as possible, to spreading tourists away from crowded attractions to less-popular ones, can be implemented by intervening on information delivery. Specifically, at least those tourists who are open to destination information can be led to greatly reduce their visits to crowded attractions while potentially almost doubling their visits to lesser-known places. Destination Management Organizations (DMOs) should therefore critically examine where tourists obtain information. Tourists respond to digital recommender systems, whether passive or active, by visiting different attraction locations. Herein the most advanced conversational systems are likely to be the most powerful, because they personalize information more effectively and are more positively evaluated. In our findings, a highly personalized recommender was evaluated much more passively than a default information source, a passive map-based app, and was also mildly more effective in redirecting tourist flows. Thus, we encourage municipalities, regional governments, and DMOs to actively communicate where they wish tourists to go, and deactivate promotion of locations where they would like to reduce crowding. In our findings, such an intervention was not associated with lower experience evaluation. An information intervention is likely to be effective within a passive recommender system. It may be more somewhat effective with a conversational one, as it was in our data. Furthermore, considering that many destinations now lend their brand to passive map-based apps, our findings would suggest that a more personalized and conversational channel for delivering information may reflect better on the destination brand. The extent to which information source evaluations reflect back on destination brands is an interesting and relevant question for future research.In terms of the connection between location and experience, popularity-driven priority attractions performed better in our study, with slightly lower negative emotions, than policy-driven attractions. Attraction reputation is very dynamic and difficult to grasp reasons in contemporary social milieus, where experience quality is quickly communicated between tourists on review sites and social media, a process which could be further explored in future research. Based on the difference between popularity-driven and policy-driven attractions we found in terms of emotions, we urge DMOs to critically evaluate the quality of attractions they recommend, and to implement marketing policies while keeping tourists’ experiences in mind.Finally, we recommend regional governments and other destination stakeholders to make decisions based on data, such as those in the present project. Collecting and analyzing these data requires investment in an appropriate data software infrastructure, but it is possible to start small, scale up to projects such as this one, and further yet to a reciprocal process which connects management decision cycles to recurring data collections on tourist experience and behavior. Certainly, | PMC10138512 | ||
5.3. Limitations and Future Directions | crowding, COVID-19-related lockdowns, ’ behavior | MAY, COLD | During the first wave of data collection (May 2021), museums and restaurants were closed due to COVID-19-related lockdowns. Many people were hoping for some perspective on the situation before booking a vacation. At the last minute, when they might still have booked, the weather turned out to be cold and very rainy. Thus, it was difficult to collect a large number of participants, and it was difficult to deliver information about attractions that they would actually be able to use. We initiated a second data wave of collection at the end of July and beginning of August, during much more favorable conditions. The situation in domestic tourism in The Netherlands is once again different, however, with all pandemic measures having been lifted at the time of this writing (early 2023), international visitation climbing toward pre-pandemic levels, and domestic tourism at vacation parks remaining popular. Thus, replicating the present study under current conditions could uncover different results, as tourists today face different levels of crowding than in 2021.The accommodations at which we sampled do not comprise a probability sample of accommodation bookings or of vacation parks in Overijssel. More generally, one might ask how representative our sample was of tourists that marketers are hoping to reach with information. Not every tourist will seek out or pay attention to destination information from organizational channels. In that sense, it is likely that the same tourists which downloaded information for an experiment such as this one would also download information purely for use during their vacation. Thus, our participants maybe have been different in ways that made them more susceptible to change their behavior in response to the information provided. It also raises the question if a passive map-based app was an optimal control condition in our experiment. Certainly, more experimental conditions with different channels for the provision of information, as well as a condition with no information at all provided, could lead to deeper insights. In future research we advocate deriving sources of information to be tested from the bottom up, based on observations of or interviews about tourists’ behavior. This could increase the validity and usefulness of the conclusions.Between-individual differences in response to the experimental intervention were controlled for by the random assignment used in our experimental design. However, we also analyzed the within-individual effect of location on experiences, which could be moderated by between-individual variables such as length of stay or travel motivations. A limitation is that we did not include these variables in our analysis. We also did not collect extensive demographic information, such as household composition or place of residence, which can lead to appreciable differences in how vacations are experienced; for example in how far away, new, or different the vacation setting feels. Controlling these variables or modeling these moderations in future research could lead more precise insights into the relationship between location and experience.Analyzing a contextual variable as complicated as location also brings limitations. We chose to use a buffer approach to measure participants’ spatial behavior in relation to attraction locations, meaning when data points were within a certain minimum distance of an attraction, we counted them as being “at” that attraction, as a reasonably simple and accurate estimate. As with all measures of social behavior, it was imperfect, however. Every single data point within a buffer does not mean that the participants visited the attraction, just that they were nearby. Combining buffers with kernel densities or per-participant time data would address this issue. For area-type attractions the boundary polygons or trails would be needed to get more accurate estimate of the visitors. | PMC10138512 |
6. Conclusions | The findings of the present study demonstrate that giving tourists policy-driven information, especially by conversational recommender system, has the potential to spread them away from heavily visited areas to a wider geographic area and specifically to attractions in less-visited areas. Their visitation of urban areas also shifts. Furthermore, giving tourists different information via different channels may not change their experience of their vacation, though it is likely to change their experience of receiving information. In sum, the findings show promise for informing tourists about lesser-known attractions to reduce crowding without degrading the quality of their experiences.The findings situate actionable and affordable interventions in the often negative discussion about overtourism and increasing crowding in areas with vigorous tourist flows. We highlight the importance of attending to information that tourists use to make spatial behavior decisions at the destination. The present study shows that information interventions may carry the potential to improve quality of life for all destination stakeholders, including both tourists and local residents. The promise of intervening on information delivered to tourists is particularly substantial if the effects of these interventions are measured, and further decisions are made based on evidence. | PMC10138512 | ||
Author Contributions | W.W. | Conceptualization, O.M., R.B., J.K. and W.W.; methodology, O.M. and J.K.; software, R.B., H.M. and O.M.; formal analysis, O.M., M.V., L.d.G. and K.V., resources, W.W. and R.B.; data curation, O.M., H.M. and W.W.; writing—original draft preparation, O.M.; writing—review and editing, O.M., L.d.G., K.V. and H.M.; visualization, H.M. and O.M.; supervision, O.M.; project administration, O.M.; funding acquisition, O.M., J.K. and R.B. All authors have read and agreed to the published version of the manuscript. | PMC10138512 | |
Institutional Review Board Statement | BREDA | IThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Breda University of Applied Sciences with number 2103-01-ADV-02-CC-ZOEY. | PMC10138512 | |
Informed Consent Statement | Informed consent was obtained from all participants. | PMC10138512 | ||
Data Availability Statement | The data used in this study are unavailable for public use due to the privacy sensitivity of location data that may inadvertently occasionally include participants’ travel from their home to their vacation location. | PMC10138512 | ||
Conflicts of Interest | Author 2 (R.B.) is the CEO of Zoey, the recommender system studied in some of the experimental conditions. He contributed to the conceptualization of the study, structuring destination information and delivering the experimental intervention, and to factual description of technology underlying recommender systems to the manuscript. He did not have access to the design of measurement instruments or study data, and did not participate in the data analysis or interpretation of results. The remaining authors have no conflicts of interest to declare. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. | PMC10138512 | ||
References | POSITIVE | Screenshots of conversational (Self-reported differences between groups on (Locations visited by at least one participant from all four groups.Most visited locations.Locations visited exclusively by each of the 2 popularity-driven groups.Locations visited exclusively by each of the 2 policy-driven groups.Difference between popularity-driven and policy-driven participants.Difference between passive and conversational popularity-driven participants.Difference between passive and conversational policy-driven participants.Self-reported differences between groups including inferential comparisons to passive popularity-driven group.Note: Significant differences compared to passive popularity-driven group marked as * Coverage of Overijssel and its urban areas by each group.Effect of recommender systems and destination information on spatial movement, expressed as odds ratios in reference to popularity-driven passive group.Note: Popularity-driven attraction model AIC = 10,848.9; BIC = 10,904.4; Policy-driven attraction model AIC = 103,548.9; BIC = 103,604.4; Non-priority attraction model AIC = 41,765.2; BIC = 41,820.7; Urban area model AIC = 272,864.75; BIC = 272,920.26; * Effects of movement at attractions compared to movement at non-attraction locations on daily emotions.Note: Positive emotion model AIC = 574,279.4; BIC = 574,346.0; Negative emotion model AIC = −916,767.6; BIC = −916,701.0; * | PMC10138512 | |
1. Introduction | CVDs, death, premature atherosclerosis, hypercholesterolemia, Hypercholesterolemia, atherosclerosis, fatty deposits | HYPERCHOLESTEROLEMIA, INFLAMMATORY RESPONSES, CARDIOVASCULAR DISEASES, CVD, HYPERCHOLESTEROLEMIA, OXIDATIVE STRESS, PREMATURE ATHEROSCLEROSIS, ATHEROSCLEROSIS, DYSLIPIDEMIA | Probiotics have the potential as a multi-target approach to modulate hypercholesterolemia associated with premature atherosclerosis. Various strains of Hypercholesterolemia is a major risk factor leading to cardiovascular diseases (CVDs), a major cause of death worldwide. High levels of total cholesterol, low-density lipoprotein-cholesterol (LDL-C), and triglyceride result in a build-up of fatty deposits inside the arteries leading to atherosclerosis [Accumulating evidence shows that the elevated levels of LDL-C predominantly induce oxidative modification of LDL-C in arterial walls through enzymatic and nonenzymatic oxidation of lipid molecules, leading to aggravation of oxidative stress and inflammatory responses in the vascular endothelium cell resulting in the emergence of oxidized low-density lipoprotein (oxLDL) [Substantial evidence from clinical and experimental studies has demonstrated that alterations in cholesterol and lipoprotein metabolism [In recent years, several food supplements have emerged as potentially effective for dyslipidemia, lowered LDL-C levels, and CVD prevention, such as garlic [Therefore, the objective of this study was to investigate the effect of | PMC9921995 |
2. Materials and Methods | PMC9921995 | |||
2.1. Subjects | TCTR | This study was conducted following the rules of the Declaration of Helsinki. The protocol was approved by the Ethical Committee of the Human Experimentation Committee, Research Institute for Health Science (RIHES), Chiang Mai University, Thailand (Project No. 11/64), and the Thai Clinical Trials Registry (TCTR) is TCTR20220917002 (Subjects were randomly distributed into two groups: placebo or In addition, subjects in this study underwent a general characteristics examination, including age, sex, BMI, systolic blood pressure, diastolic blood pressure, and heart rate, and were interviewed using a questionnaire to obtain information on their dietary status. | PMC9921995 | |
2.2. Study Design and Treatment | BLIND | We conducted a single-center, prospective, randomized, double-blind, placebo-controlled, parallel-group trial. Subjects were kept blind regarding treatments. Blinded investigators performed data interpretation and analysis.Subjects were randomly assigned using block randomization into two groups as follows: (1) | PMC9921995 | |
2.3. Blood Sampling and Biochemical Measurements | inflammation, TG, TNF-α, tumor necrosis | CLOT, TUMOR NECROSIS, INFLAMMATION, BLOOD, OXIDATIVE STRESS | Blood samples were collected in the morning after 12 h fasting at each visit and kept in a test tube to analyze for blood lipid profiles (TG, TC, LDL-C, and HDL-C), serum biochemistry (FBG, blood urea nitrogen (BUN), creatinine, and alanine transaminase (ALT), aspartate transaminase (AST) and alkaline phosphatase (ALP), blood electrolytes (sodium, potassium, and chloride), and complete blood count examinations as the following parameters: white blood cell count (WBC), hemoglobin concentration, hematocrit, platelet count (PLT), neutrophil, lymphocyte, monocyte, eosinophil, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC). The tests were done in the Laboratory Unit of Chiang Mai Medical Lab, Chiang Mai, Thailand. Additionally, other blood samples were allowed to clot and were spun at 1450 RCF or g force for 10 min at 4 °C. Then, the serum was transferred into dry well-labeled specimen plastic tubes and stored at −80 °C until analysis. Serum analysis was performed on a spectrophotometry (SPECTROstarNano, BMG LABTECH, Biotechnology division, Scientific Promotion Ltd., Ortenberg, Germany) analyzer to study a change of oxidative stress status, an inflammation marker, lipid metabolism enzymes, and MCP-1 parameter. The oxidative stress and antioxidant markers were used to evaluate the oxidative stress status. The lipid peroxidation level, excess production of reactive oxygen species (ROS), was investigated using the thiobarbituric acid reactive substances (TBARS) method according to Bhutia et al. [Inflammatory markers, including interleukin-10 (IL-10), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α), were measured by enzyme-linked immunosorbent assay kits using paired antibodies (SigmaThe levels of total bile acid (TBA), adiponectin, apolipoprotein E (APOE), and monocyte chemoattractant protein-1 (MCP-1) were also evaluated via enzyme-linked immunosorbent assay kits with the intra- and interassay coefficient of variations (CVs) <12%, <10%, <10%, and <10%, respectively. (Elabscience | PMC9921995 |
2.4. Statistical Analysis | The sample size of fifty subjects was calculated using the superiority design method (calculating sample size when the outcome measure is a continuous variable) [Statistical analysis was performed using SPSS software version 22 (SPSS Inc., Chicago, IL, USA). Data were ensured using the normal distribution of variables by the Kolmogorov–Smirnov test, and variables that were non-normally distributed were log-transformed before statistical analysis.The descriptive analysis was expressed as an absolute number and percentage. Continuous variables were expressed using means and standard deviations (SDs) and associated two-tailed 95% confidence intervals (CIs). We applied the means substitution method to handle missing values. The sensitivity analysis in which the missing values were not imputed was performed. Outcomes were compared to the baseline during a study period, and a paired | PMC9921995 | ||
3. Results | PMC9921995 | |||
3.1. Characteristics of the Subjects | dyslipidemia | SECONDARY, DYSLIPIDEMIA | A total of 50 hypercholesterolemic subjects were assessed for eligibility, and 4 subjects were excluded due to the presence of secondary dyslipidemia (n = 3) and relocation (n = 1). Subjects were randomly allocated into the Subjects in the The within-group and between-group analysis showed that general characteristics of BMI, systolic blood pressure, diastolic blood pressure, heart rate, and calorie intake had no statistically significant difference at baseline and the end of the study. The general characteristics data of all subjects at baseline and the end of the study were presented in | PMC9921995 |
3.2. Effect of L. paracasei TISTR 2593 Supplementation on Blood Safety Parameters | BLOOD | Blood samples were tested for FBG, BUN, creatinine, ALT, AST, ALP, blood electrolytes, and hematology representing safety parameters when repetitively consuming A comparison of within-group and between-group was analyzed, and results are shown in | PMC9921995 | |
3.3. Effect of L. paracasei TISTR 2593 Supplementation on Blood Lipid Profiles | To investigate the effect of | PMC9921995 | ||
3.4. Effect of L. paracasei TISTR 2593 Supplement on Oxidative Stress and Inflammation | inflammation, atherosclerosis, Hypercholesterolemia | INFLAMMATION, ATHEROSCLEROSIS, HYPERCHOLESTEROLEMIA, OXIDATIVE STRESS, PATHOGENESIS | Hypercholesterolemia alters vascular endothelial cell membranes, enhancing oxidative stress and inflammation mediators implicated in the pathogenesis of atherosclerosis [In current results, it was found that subjects who consumed Additionally, a significant difference in the reduction in MDA ( | PMC9921995 |
3.5. Effect of L. paracasei TISTR 2593 Supplement on Adiponectin, apolipoprotein E, and Total Bile acid Level | Regulating glucose and lipid metabolism, cholesterol transportation, and cholesterol execration play an important role in disrupting cholesterol metabolism. Thus, the serum level of adiponectin, apolipoprotein E, and TBA was evaluated to observe the possible effect of Interestingly, the results showed that subjects who consumed When compared between groups, we found that the placebo group’s serum adiponectin level decreased significantly than the | PMC9921995 | ||
4. Discussion | adiponectin deficiency, atherosclerosis, hypercholesterolemia, endothelial dysfunction | HYPERCHOLESTEROLEMIA, INFLAMMATION, ATHEROSCLEROSIS, INSULIN RESISTANCE, DYSLIPIDEMIA, OXIDATIVE STRESS, ENDOTHELIAL DYSFUNCTION | This present study demonstrated the benefit of Recently, it has been suggested that the gut microbiota and their metabolites play an important role in regulating lipid metabolism and glucose homeostasis [This observation may be attributed to the anti-inflammatory effects of probiotics by inhibiting the release of pro-inflammatory cytokines. The reduced inflammatory state in the body could be a potential metabolic process for the LDL-C lowering effect. A significant reduction in serum TNF-α without any changes in IL-6 was observed in participants receiving Our results showed that plasma APOE levels were significantly increased in participants with According to the emerging evidence, it has been suggested that elevated oxidative stress is involved with endothelial dysfunction induced by hypercholesterolemia [Adiponectin plays an important role in glucose and lipid metabolism, and adiponectin deficiency is related to insulin resistance, inflammation, dyslipidemia, and the risk of atherosclerosis [In addition to the above remarks, bile salt hydrolase (BSH) enzyme activity was also increased with probiotic supplementation. Co-precipitation between intestinal cholesterol and deconjugated bile salts could reduce the absorption of cholesterol and bile salts, consequently lowering blood cholesterol [Even though there were no differences in baseline characteristics between groups, some limitations were still found in the study. Our study did not analyze changes in the gut microbiota community in feces, which is an essential indicator to confirm the colonization of the probiotic | PMC9921995 |
5. Conclusions | In summary, the current study findings demonstrated that supplementation with | PMC9921995 | ||
Author Contributions | W.W. | Conceptualization, J.K. and P.C.; methodology, J.K. and P.C.; formal analysis, J.K., P.C. and A.P.; investigation, J.K., P.Y., K.B., P.A., S.T. and W.W.; resources, J.S., K.N., P.T., P.P. and P.C.; writing—original draft, J.K. and A.P.; writing—review and editing, J.K., A.P., W.W. and P.C.; supervision, J.K. and P.C.; project administration, J.K.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript. | PMC9921995 | |
Institutional Review Board Statement | The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethical Committee of the Human Experimentation Committee, Research Institute for Health Science (RIHES), Chiang Mai University, Chiang Mai, Thailand (Project No. 11/64). | PMC9921995 | ||
Informed Consent Statement | All study participants provided informed consent. | PMC9921995 | ||
Data Availability Statement | The data supporting the findings of this study are included in this article. | PMC9921995 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC9921995 | ||
References | BLOOD | Consort flow diagram.The general characteristics of the subjects. Data were presented as ± standard deviations.Effect of Data expressed as mean ± standard deviations. FBG; Fasting blood glucose, BUN; Blood Urea Nitrogen, ALT; Alanine aminotransferase, AST; Aspartate aminotransferase, ALP; Alkaline phosphatase, WBC; white blood cell, MCV; mean corpuscular volume, MCH; mean corpuscular hemoglobin, MCHC; mean corpuscular hemoglobin concentration.Effect of Data expressed as mean ± standard deviations. * Significant difference from baseline within each group (Effect of Data expressed as mean ± standard deviations. *, **, *** Significant difference from baseline within each group ( | PMC9921995 | |
Background | colorectal cancer, trauma | COLORECTAL CANCER | During the perioperative period, the surgical stress response induced by surgical trauma tends to cause a decrease in peripheral lymphocytes. Anesthetics could reduce the stress response during surgery and prevent sympathetic nerve overexcitation. The goal of this study was to investigate how BIS-guided anesthetic depth affected peripheral T lymphocytes in patients undergoing laparoscopic colorectal cancer surgery. | PMC10184475 |
Methods | colorectal cancer | BLOOD, COLORECTAL CANCER | A total of 60 patients having elective laparoscopic colorectal cancer surgery were randomly assigned and analyzed (n = 30 for deep general anesthesia, BIS 35, n = 30 for light general anesthesia, BIS 55). Blood samples were collected immediately before anesthesia induction and immediately after operation, 24 h and 5 days postoperatively. The CD4+/CD8 + ratio, T lymphocyte subsets (including CD3 + T cells, CD4 + T cells, and CD8 + T cells), and natural killer (NK) cells were analyzed by flow cytometry. Serum interleukin-6 (IL-6), interferon -ɣ (IFN-ɣ), and vascular endothelial growth factor-α (VEGF-α) were also measured. | PMC10184475 |
Results | The CD4+/CD8 + ratio decreased 24 h after surgery in two groups, but the reduction did not differ between the two groups ( | PMC10184475 | ||
Conclusions | colorectal cancer, peripheral T lymphocyte subsets | COLORECTAL CANCER | Despite the fact that patients in deep general anesthesia group had low levels of the IL-6 24 h after surgery, the deep general anesthesia was not associated to a positive effect on patients’ peripheral T lymphocytes during colorectal cancer surgery. We found no evidence that peripheral T lymphocyte subsets and natural killer cells were affected by the targeting a BIS of either 55 or 35 in patients undergoing laparoscopic colorectal cancer surgery in this trial. | PMC10184475 |
Trial registration | ChiCTR2200056624 ( | PMC10184475 | ||
Keywords | PMC10184475 | |||
Introduction | cancer, colorectal cancer, CRC, trauma | FUNCTIONAL DISTURBANCE, CANCER, COLORECTAL CANCER, IMMUNOSUPPRESSION | Worldwide, colorectal cancer (CRC) is the third most commonly diagnosed cancer, and the second for mortality [Immunosuppression is commonly observed in patients who underwent trauma and major surgery, which could be characterized by functional disturbances and quantity decline in lymphocytes [Therefore, we tested the hypothesis that deep anesthesia (BIS 35) could control surgical stress response effectively, hence CD4+/CD8 + ratio in the deep general anesthesia group is higher than that in the light general anesthesia group (BIS 55). In addition, it was previously reported that high CD4+/CD8 + ratio correlated with stronger cellular immunity [ | PMC10184475 |
Methods and materials | PMC10184475 | |||
Ethics and registration | PMC10184475 | |||
Ethical approval | for this study (ethics: KY-20211130002-02) was provided by the Ethics Committee of the affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China on 29 January 2022. The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2200056624) on 09/02/2022 before enrollment. The study protocol followed the CONSORT guidelines. All participants signed written informed consent. | PMC10184475 |
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