This study aimed to assess the cost-effectiveness of aQIV compared to SD-QIV and HD-QIV in Denmark, Norway, and Sweden for adults aged ≥65 years. A static decision tree model was used to evaluate costs and outcomes of different vaccination strategies from healthcare payer and societal perspectives. This model projects that compared to SD-QIV, vaccination with aQIV could prevent a combined total of 18,772 symptomatic influenza infections, 925 hospitalizations, and 161 deaths in one influenza season across the three countries. From a healthcare payer perspective, the incremental costs per quality adjusted life year (QALY) gained with aQIV versus SD-QIV were EUR 10,170/QALY in Denmark, EUR 12,515/QALY in Norway, and EUR 9894/QALY in Sweden. The aQIV was cost saving compared with HD-QIV. This study found that introducing aQIV to the entire population aged ≥65 years may contribute to reducing the disease and economic burden associated with influenza in these countries.Individuals aged 65 years and above are at increased risk of complications and death from influenza compared with any other age group. Enhanced vaccines, as the MF59 Seasonal influenza is an acute respiratory infection caused by influenza viruses . Apart f®, a squalene oil-in-water emulsion that is designed to induce a more robust, durable, and broader immune response [Vaccination is the most effective measure to prevent influenza. Health agencies in several European countries support free seasonal influenza programs for high-risk populations, including adults aged ≥65 years and above . Quadrivresponse . Enhanceresponse ,13,14,15response .As a result, enhanced vaccines have been recommended and/or reimbursed for seasonal influenza vaccination programs for this population in multiple countries ,18,19,20There is no published evidence on the cost-effectiveness of aQIV in comparison to SD-QIV or HD-QIV in the Nordic countries. Recently, aQIV was shown to be a cost-saving option compared with HD-QIV and a cost-effective option compared with SD-QIV for adults over 65 years of age in Spain ,25. SimiOur study aimed to assess the health economic impact of adopting aQIV compared to SD-QIV and HD-QIV in the seasonal influenza vaccination programs for adults aged ≥65 years in Denmark, Norway, and Sweden.A decision tree model was developed in MS Excel 365 and built on a model structure used in two published cost-effectiveness analyses for influenza vaccines ,30. The To allow comparison of different vaccination strategies, the same decision tree pathways starting from the infection stage were replicated for each vaccine , allowing outcomes from the populations immunized with each vaccine (and without immunization in the “no vaccine” arm) to be estimated separately and combined based on vaccination coverage. Two comparisons were conducted: aQIV vs. SD-QIV (strategy 1 vs. strategy 2) and aQIV vs. HD-QIV (strategy 1 vs. strategy 3). Considering all clinical events captured in the model were conditional on an influenza infection, the impact of a vaccination strategy on the population  was driven by VE against influenza infection.This study focused on adults aged ≥65 years in Denmark, Norway, and Sweden. Modeled populations were further divided into two subgroups (65–74 years and ≥75 years) to account for certain age-specific model inputs. The model projected the health (life years (LYs) and quality adjusted life years (QALYs)) and cost outcomes during one influenza season (assumed to last 6 months), as well as LYs, QALYs, and productivity lost due to premature death resulting from IRCs. Country-specific discount rates for health and cost outcomes  are difficult to obtain due to limited testing, especially in asymptomatic patients. Somes et al. have conducted a multi-country systematic review and meta-analysis of vaccine randomized controlled trials (RCT) that reported on laboratory-confirmed seasonal influenza in the placebo arm . They esThe population aged ≥65 years were vaccinated with aQIV, SD-QIV, or HD-QIV. Vaccine coverage rates were assumed to be the same for the three strategies and varied by country. Non-vaccinated patients  were included in the “No vaccine” arm of the model. In the base case, vaccine coverage rates for the 2020/21 season were used for three countries . These wThe overall effectiveness of SD-QIV was calculated as a weighted average of strain-specific VE and influenza strain circulation information . The VE Clinical inputs were sourced from published literature. When available, we sourced evidence originating from Denmark, Norway, and Sweden. In the absence of data from these countries, we used evidence from other countries, giving preference to large studies used to inform previous influenza cost-effectiveness models reported in peer-reviewed publications. All clinical inputs were validated based on the authors’ clinical experience to ensure they were applicable to Denmark, Norway, and Sweden . In the Patients with IRCs were assumed to require either hospitalization or treatment in outpatient settings. The probabilities of hospitalization for each complication were sourced from published literature ,46,47,48The model applied general population utilities and utility decrements for influenza symptomatic infection and IRCs to estimate quality adjusted life years (QALYS) for the different vaccination strategies. Age-specific utility values for the general population in three countries were collected from published literature ,104. UtiA summary of costs used in the base case is provided in The base case analyses included two comparisons: aQIV vs. SD-QIV and aQIV vs. HD-QIV. The following outcomes were evaluated: numbers of clinical events , costs , life years (LY) and quality adjusted life years (QALY), ICERs, and net monetary benefit (NMB). In addition, the model estimated the maximum price ratio of aQIV (vs. SD-QIV) at which aQIV remained a cost-effective strategy.There are no official willingness to pay thresholds in Denmark, Norway, and Sweden. We identified economic evaluations in these countries that use different thresholds ranging from EUR 22,000 to EUR 55,000 per QALY ,119,120.Scenario analyses were conducted to evaluate the robustness of the model results by changing input parameters and/or assumptions using alternative data sources and hypotheses . UnivariThe base case results for the comparison between aQIV and SD-QIV are presented in When compared to HD-QIV, our analysis suggests that aQIV is a cost-saving vaccination strategy, resulting in savings of around EUR 7.1 M in Denmark, EUR 4.7 M in Norway, and EUR 8.5 M in Sweden . The cosResults from the scenario analyses conducted for the comparison of aQIV vs. SD-QIV  are presented in Variations in the vaccine coverage rate (scenario 1) had a negligible impact on incremental outcomes of the model (and consequently ICERs) since the variation in coverage rate was equally applied to both strategies. The analysis using influenza strain distribution from individual seasons (scenarios 2.1–2.5), yielded ICERs ranging between EUR 5650 and EUR 17,354 with aQIV remaining a cost-effective strategy in all scenarios across all countries. When using only observational data to estimate the rVE of aQIV (scenario 3), the ICERs increased in all countries, but aQIV remained a cost-effective strategy. When varying the strain-specific VE of SD-QIV (scenarios 4.1 and 4.2), results ranged from aQIV being a dominant strategy  to a maximum ICER of EUR 21,615/QALY. When varying rVEs for HD-QIV vs. SD-QIV and aQIV vs. HD-QIV (scenarios 5.1 and 5.2), we observed a significant impact on the ICERs for aQIV vs. SD-QIV, ranging from dominant to ICERs above the assumed cost-effectiveness threshold in all countries. The impact of the management costs of IRCs (scenarios 6.1 and 6.2) was moderate and ICERs varied between ±35% and ±43% from base case results when all complication-related costs were varied by ±30%. When excluding HF as an IRC (scenario 7), ICERs showed a small increase with aQIV remaining a cost-effective strategy in all countries.Results of the DSA are presented in Variations on the rVE of HD-QIV vs. SD-QIV (9.7–36.5%) and aQIV vs. HD-QIV (−2.5–8.9%), strain-specific VE of SD-QIV against H3N2 (−6–45%), influenza attack rate (5–10%), and acquisition costs of both vaccines were the major drivers of the ICER for the three countries. The remaining parameters in the model had a moderate to minor impact on the ICERs. The aQIV remained a cost-effective strategy in all but one of the scenarios tested in the DSA (lower bound of rVE between aQIV and HD-QIV for Norway).PSA results (scatter plots and cost-effectiveness acceptability curves) for aQIV vs. SD-QIV are presented in From a healthcare payer perspective, the cost-effectiveness acceptability curves (CEACs) showed that aQIV was more likely to be cost-effective than SD-QIV in Denmark (90%), Norway (77%), and Sweden (75%), at a threshold of EUR 30,000/QALY. Our results showed that aQIV was a dominant strategy in 23%, 32%, and 36% of the simulations for Denmark, Norway, and Sweden, respectively.Due to immunosenescence, SD-QIV provides limited protection for older adults. Lower vaccine effectiveness and higher prevalence of comorbidities, result in higher influenza-related morbidity and mortality among this age group (≥65 years). Enhanced vaccines such as the aQIV and HD-QIV elicit increased immune response among this population and have demonstrated improved VE compared to the SD-QIV ,40,41. AIn this study, we developed a static decision-tree model to estimate the influenza-related disease and economic burden based on different vaccination strategies in Denmark, Norway, and Sweden for adults ≥65 years. To our knowledge, this is the first cost-effectiveness study of aQIV in these countries. The model results indicate that aQIV can prevent a higher number of symptomatic influenza cases, influenza-related complications leading to hospitalizations, and deaths compared to SD-QIV. Vaccination with aQIV led to cost increases and QALY gains and aQIV was a cost-effective vaccination strategy for adults aged ≥65 years compared to SD-QIV. In comparison to HD-QIV, aQIV resulted in cost savings and comparable health benefits in the three countries.Our results align with previous modeling studies for influenza vaccination strategies with aQIV in other European countries. Fochesato et al. showed that replacing SD-QIV with aQIV in the Spanish adult population aged ≥65 years was a cost-effective strategy based on a compartmental disease transmission model and decision-tree model . Kohli eThe disease burden projected in our analysis is conservative when compared with the available evidence in Denmark, Norway, and Sweden. The Public Health Agency of Sweden reported that there were between 206 and 949 deaths from influenza each year between seasons 2015/16 and 2018/19 compared to 636 deaths projected in our study for Sweden (for the SD-QIV strategy) ,79,80,81Our base case model findings were robust based on several sensitivity and scenario analyses. However, model results were sensitive to the inputs and assumptions used to inform effectiveness of the vaccines included in the analyses. Strain-specific effectiveness for SD-QIV and rVEs for the enhanced vaccines were key drivers of the model results. This is a result of the wide confidence intervals in the estimates sourced from the literature. For instance, the rVE of aQIV vs. HD-QIV ranges from negative to positive values, indicating non-statistically significant estimates that suggest comparability. Nevertheless, DSA and scenario analyses on these model parameters showed that aQIV remained a cost-effective strategy in most alternative scenarios evaluated. The influenza attack rate was another driver of the model outcomes, showing an increase in the value of vaccination with aQIV during severe influenza seasons. PSA showed that aQIV has a high likelihood of being a cost-effective or dominant vaccination strategy in comparison with SD-QIV and HD-QIV.The findings of this study must be interpreted considering several limitations. There is no evidence from randomized controlled trials (RCT) or observational studies directly comparing the effectiveness of standard-dose quadrivalent vaccines with the respective enhanced formulations (aQIV and HD-QIV). Hence, we have used evidence from the trivalent formulations. It should be noted that effectiveness evidence from studies from the trivalent formulations are relevant for QIV vaccines as they have overlapping compositions and the same manufacturing process . FurtherCountry-specific evidence was limited on the incidence of IRCs and IRC-related hospitalization and mortality with the granularity required for our model. We have used inputs derived from other countries and validated their applicability to Denmark, Norway, and Sweden based on the authors’ clinical experience. We decided to include HF as an IRC in the model base case, as this is the “end disease” for all cardiovascular conditions. Some evidence suggests that influenza vaccination has associated benefits in terms of CV disease outcomes. A recent meta-analysis reported lower risks of all-cause and CV-related mortality for HF patients vaccinated against influenza . In addiAnother limitation is related to the net prices for the different vaccines in Denmark, Norway, and Sweden. Due to confidential commercial agreements and tenders negotiated directly with manufacturers, we could not accurately estimate net prices. Therefore, we used publicly available evidence from other European countries to inform our price assumptions. DSAs showed that vaccine prices were among the main drivers of model outcomes. Nevertheless, our findings remained consistent for variations of ±20% in the prices of all vaccines.Future vaccine coverage rates are an area of uncertainty. Vaccination rates in the three countries have been affected by the COVID-19 pandemic. Compared with previous years, higher coverage rates in the 2020/21 influenza season were reported likely due to increased public awareness for the consequences of respiratory infections. We used coverage rates from the 2020/21 season under the assumption that influenza vaccination rates will remain high for the following years . Nevertheless, to address the uncertainty around this parameter, we conducted a scenario analysis with lower vaccine coverage rates and aQIV remained a cost-effective (vs. SD-QIV) or cost-saving (vs. HD-QIV) strategy.Finally, our study used a static model that did not capture the dynamics of disease transmission, such as impact of seasonal variations, interactions between individuals, and herd immunity . Incorporating herd immunity would likely show additional benefits in favor of the most effective vaccines  .Our analyses indicated that, in an average influenza season, aQIV may be a cost-effective strategy compared to SD-QIV and may be cost-saving when compared to HD-QIV for preventing seasonal influenza among adults aged ≥65 years in Denmark, Norway, and Sweden. The introduction of aQIV may prevent a significant number of influenza cases and IRCs, leading to a lower disease burden for patients and reducing the economic burden for healthcare payers and society.


To describe the surgical technique and compare the operative outcomes of gasless and traditional robot-assisted transvaginal natural orifice transluminal endoscopic surgery (GR-vNOTES vs. TR-vNOTES) in hysterectomy.via GR-vNOTES or TR-vNOTES between February 2020 and January 2022 in our hospital were included. Clinical data regarding patient demographics, operative time, blood loss, complications, and postoperative hospital stays were collected and analyzed.The patients undergoing hysterectomy via GR-vNOTES, and nine cases via TR-vNOTES. The baseline demographics and operative outcomes were not significantly different in GR-vNOTES and TR-vNOTES groups. There was no conversion to multiport robotic laparoscopy, conventional laparoscopy or laparotomy. No complications were seen in both groups, except two cases had fever postoperatively in the TR-vNOTES group. For those with early stage cervical/endometrial cancer, no recurrence or metastasis was observed in the follow-up of six months.Five cases underwent hysterectomy Both GR-vNOTES and TR-vNOTES were feasible and safe for hysterectomy. GR-vNOTES was a promising alternative to TR-vNOTES in hysterectomy. Laparoscopy initiated the era of minimally invasive surgery. Natural orifice transluminal endoscopic surgery (NOTES) furtherly reduced the surgical trauma. The vagina is the most widely used natural channel because it provides safe access to the peritoneal cavity . AccordiLaparoscopy is standardly performed by achieving pneumoperitoneum. However, pneumoperitoneum could bring many issues such as increased discomfort, longer recovery time, and the potential of tumor metastasis . Particuvia GR-vNOTES and TR-vNOTES from May 2021 to July 2022 in our hospital were included. The operative approach was mainly based on the patients’ choice, after they were informed of the pros and cons of “GR-vNOTES and TR-vNOTES.” The exclusion criteria included patients with complete cul-de-sac obliteration, suspected severe endometriosis, late stage cervical/endometrial cancer，or a history of multiple prior open abdominal operations. Medical records were identified through our hospital’s database. The baseline demographics and operative outcomes were compared between the two groups. The study was approved by the Ethics Committee of Chengdu Women and Children’s Central Hospital (202315). All participants were given written informed consent.In this study, patients who underwent hysterectomy Patients were given the lithotomy position after administering general anesthesia. The cervical-vaginal junction was incised circumferentially and the posterior and anterior colpotomy was performed subsequently. For gasless surgery, one sterilized needle (1.2 mm) was inserted through the subcutaneous tissue 5 cm above symphysis pubis level, and lifted by abdominal wall retractors . If necep < 0.05 was considered statistically significant.Statistical analysis was performed using SPSS 19.0 software. Categorical variables were assessed using the chi-square test, and continuous variables were evaluated by the Student’s T-test or Mann–Whitney U test according to the data distribution. Baseline patient characteristics such as age, menopause or not, body mass index (BMI), gravidity, parity, previous abdominal surgeries did not differ between the two groups . There wvs 115 ± 23 min, p = 0.421). The amount of estimated blood loss (50 (50–75) vs. 50 (20–100) mL, p = 0.594), and postoperative hospital stays  were also comparable in both groups. There was no significant difference in terms of VAS at operative day and postoperative days. And there was no conversion to multiport robotic surgery, conventional laparoscopy or laparotomy. Only two patients in the TR-vNOTES group developed a postoperative fever but recovered quickly after administering antibiotics. No patients experienced damage to adjacent organs, hematoma or re-operation. For those with early stage of cervical cancer or endometrial cancer, no recurrence or metastasis were observed in the follow-up of six months. The baseline characteristics and operative outcomes is shown in The operative time was not noticeably different in both groups  mL, respectively, in our study. According to our review, there appeared to be only one literature which reported the operative outcomes of 13 patients who had underwent gasless robotic V-notes for hysterectomy . The litBased on our knowledge, this is the first study to compare the operative outcomes of GR-vNOTES and TR-vNOTES in hysterectomy. The main limitation of our study was the small sample size which reduced its statistical power. Secondly, there was some difference in the disease type in both groups, which may bring some bias.To summarize, GR-vNOTES and TR-vNOTES were both safe and effective in hysterectomy. Additionally, GR-vNOTES avoided the disadvantages of CO2 pneumoperitoneum and was a promising alternative to TR-vNOTES. However, to confirm the findings of this study, prospective studies with large sample sizes are required in the future.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Ethics Committee of Chengdu Women and Children’s Central Hospital. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.YM drafted the manuscript and participated in data collection and analysis. LH participated in the design of the study. QZ, YC, and JZ participated in the data collection and analysis. YL participated in the design of the study and coordination. All authors contributed to the article and approved the submitted version.This work was supported by Sichuan Provincial Medical Association Project: S19084.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


With the increasing demand for person re-identification (Re-ID) tasks, the need for all-day retrieval has become an inevitable trend. Nevertheless, single-modal Re-ID is no longer sufficient to meet this requirement, making Multi-Modal Data crucial in Re-ID. Consequently, a Visible-Infrared Person Re-Identification (VI Re-ID) task is proposed, which aims to match pairs of person images from the visible and infrared modalities. The significant modality discrepancy between the modalities poses a major challenge. Existing VI Re-ID methods focus on cross-modal feature learning and modal transformation to alleviate the discrepancy but overlook the impact of person contour information. Contours exhibit modality invariance, which is vital for learning effective identity representations and cross-modal matching. In addition, due to the low intra-modal diversity in the visible modality, it is difficult to distinguish the boundaries between some hard samples. To address these issues, we propose the Graph Sampling-based Multi-stream Enhancement Network (GSMEN). Firstly, the Contour Expansion Module (CEM) incorporates the contour information of a person into the original samples, further reducing the modality discrepancy and leading to improved matching stability between image pairs of different modalities. Additionally, to better distinguish cross-modal hard sample pairs during the training process, an innovative Cross-modality Graph Sampler (CGS) is designed for sample selection before training. The CGS calculates the feature distance between samples from different modalities and groups similar samples into the same batch during the training process, effectively exploring the boundary relationships between hard classes in the cross-modal setting. Some experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate the superiority of our proposed method. Specifically, in the VIS→IR task, the experimental results on the RegDB dataset achieve 93.69% for Rank-1 and 92.56% for mAP. Person re-identification (Re-ID) ,4,5,6,7 Besides the intra-modal variations already present in single-modal Re-ID, a key challenge in the VI Re-ID task is how to reduce the modality discrepancy between visible and infrared images of the same identity. Existing research approaches primarily rely on modal transformation methods. These methods generate cross-modal or intermediate-modal images corresponding to person images to convert heterogeneous modalities into a unified modality, thereby reducing the modal discrepancy. Specifically, Generative Adversarial Networks (GANs)  and encoTo enhance the method’s adaptability to cross-modal challenges, some recent researchers apply modal-shared feature learning to the VI Re-ID task, which projects visible and infrared images into a specific shared embedding space, achieving cross-modal feature alignment. These approaches can be further divided into global feature learning and local feature learning. Specifically, global feature learning represents a person image as a single feature vector, which is suitable for capturing overall person identity information. On the other hand, local feature learning uses a set of feature vectors based on parts or regions to represent the image, allowing for a more detailed capture of the local features of person images. In addition, the two-stream convolutional neural network architecture is commonly applied in such methods and combined with loss functions  for constraint.Although these methods have achieved good results in alleviating the modality discrepancy, they still have certain limitations: (1) Existing modal-shared feature learning methods typically focus on exploring either global or local feature representations and rarely combine the advantages of both features. Moreover, due to the nature of infrared images, which only contain a single channel reflecting objects’ thermal radiation, key features like color cannot be utilized for cross-modal matching. Directly extracting features from infrared images may also suffer from interference from identity-irrelevant information. (2) These methods all adopt some basic sampling techniques, such as random sampling and uniform sampling, which do not consider the relationships and similarities between samples with different modalities. Additionally, in VI Re-ID tasks, there are limited features that can be extracted when dealing with infrared modality retrieval, resulting in numerous similar features among samples from different classes. Consequently, conventional sampling methods struggle to capture the subtle differences between these similar features.To address the aforementioned two issues, we propose a novel method named the Graph Sampling-based Multi-stream Enhancement Network (GSMEN) in this paper. It is noteworthy that when humans engage in the visual judgment of infrared surveillance, they heavily rely on contour information. Despite the absence of color and texture features in infrared images, contour and shape information remains clear and visible, as depicted in k nearest neighboring samples of different classes, each class containing the same number of S instances, as illustrated in Then, to tackle the challenge of exploring the boundaries between hard classes in VI Re-ID task, an efficient batch sampling technique is introduced, known as the Cross-modality Graph Sampler (CGS). Specifically, CGS involves constructing nearest neighbor relationship graphs for all classes in the visible and infrared modalities at the beginning of each epoch and then combining them. Subsequently, CGS conducts batch sampling by randomly selecting a sample as the anchor and choosing its top-To enhance feature representation and reduce the cross-modal discrepancy between visible and infrared modalities, we propose the Contour Expansion Module (CEM), which combines the global features of contours with the local features obtained from modal-shared feature learning. To the best of our knowledge, this method represents the first attempt at tackling the VI Re-ID task.To explore the boundaries between hard classes, we introduce the Cross-modality Graph Sampler (CGS). The sampler constructs nearest neighbor relationship graphs separately for the visible and infrared modalities, and then combines them for batch sampling. This sampling strategy ensures that samples within a batch are mostly similar to each other, providing informative and challenging examples for discriminative learning.We conduct experiments on large-scale VI Re-ID datasets, SYSU-MM01, and RegDB. The results demonstrate that our method achieves significant improvements in matching performance and modal adaptability.In summary, our contributions in the paper are as follows:VI Re-ID aims to address not only the challenges of handling intra-modal differences but also the cross-modal disparities arising from heterogeneous images. Therefore, alleviating cross-modal disparities is crucial, as they can exacerbate existing intra-modal differences.To tackle these challenges, researchers attempt the modality-shared feature learning approach ,21,22,23In recent years, image generation-based methods ,25 are aFurthermore, it is worth noting that among various types of information in cross-modal images , contour information is crucial in cross-modal retrieval, as it exhibits strong modality invariance. However, previous researchers did not consider using it as auxiliary information to alleviate modality differences. Therefore, we attempt to introduce it as auxiliary enhancement information to improve the image-matching capability in VI Re-ID tasks.Our VI Re-ID framework is based on a two-stream convolutional neural network  but incoThe goal of contour detection is to identify pixels in the image that correspond to regions with significant changes in grayscale values. Recently, there have been studies ,25 apply methods ,31,32 van Element-wise addition. As shown in z using a two-dimensional scale with the height of x and width of y. p is the number of local body parts in visible image. Then, 1 × 1 convolutional layers are utilized to adjust the number of feature channels in C. Finally, by concatenating the local feature e and feature f. Considering the comparative experiments in Element-wise concatenation. As depicted in  Pooling  to z usiBoth DG Re-ID ,36,37,38zth dimension. Similarly, the process is applied to the infrared modality sample set:Before each epoch, we calculate the distances or similarities between classes using the latest trained model and then construct a graph encompassing all classes. This approach allows us to leverage the relationships between classes for informative sampling. To illustrate, one image per class is randomly chosen to form a smaller sub-dataset. Next, the feature S instances per class to create a mini-batch containing Later, the top Triplet loss Ltri  and Crososs Lssd  is a selSYSU-MM01  dataset RegDB  dataset To assess the performance of both datasets, we employ standard evaluation protocols, which incorporate Cumulative Matching Characteristics (CMC) , and meaThe proposed methodology utilizes the PyTorch deep learning framework and is implemented on an NVIDIA RTX 3090 GPU. Building upon existing VI Re-ID methods, a pretrained ResNet-50  is emplop of local body parts in Formula (3) is set to 6.The training process involves using the stochastic gradient descent (SGD) optimizer with a momentum value of 0.9. The initial learning rate is set to 0.01, and a warm-up strategy is employed to adjust the learning rate. Specifically, the learning rate is initialized to 0.01 and undergoes 10 decays, each occurring every twenty epochs. The training is stopped after 60 epochs. The number In this section, a comparison between the proposed method and state-of-the-art VI Re-ID approaches is conducted, including Cross-modal feature learning: DDAG , AGW 4]4], cm-SSEvaluations on SYSU-MM01. In comparison with the state-of-the-art Cross-modal feature learning method GLMC , our appFurthermore, when compared to the current best Cross-modal feature learning method AGMNet , our appEvaluations on RegDB. In comparison with the best-performing method in the VIS-to-IR mode, our approach demonstrates remarkable superiority. Specifically, our method achieves a higher mAP by 4.86% in this mode, showcasing its effectiveness in handling the cross-modal matching between visible and infrared images.Similarly, in the IR-to-VIS mode, our method outperforms the best-performing method with a significantly higher mAP by 10.67%. This result highlights the exceptional capability of our approach to effectively address the challenges of cross-modal matching between infrared and visible images.These impressive performance demonstrates the versatility and effectiveness of our proposed method in handling VI Re-ID task.In this section, a comprehensive ablation study is conducted to thoroughly evaluate the contributions of the Contour-oriented Enhancement Module (CEM) and the Cross-modality Graph Sampler (CGS) in our proposed approach. By systematically adding or removing these modules, we investigate their individual impacts on the performance of our model. The results are presented in First, we establish a baseline model  that comIn the next step, to assess the influence of the CEM module, we integrate it into the baseline model. The inclusion of the CEM module results in significant performance gains, with mAP and Rank-1 accuracy increasing by 7.74% and 8.51%, respectively. This demonstrates that the CEM module effectively enhances feature representation and reduces modality discrepancy, contributing to the overall improvement in VI Re-ID performance.Then, the impact of the CGS module is evaluated by incorporating it into the baseline model. The addition of the CGS module also leads to notable performance improvements, with mAP and Rank-1 accuracy increasing by 2.45% and 1.61%, respectively. The CGS module facilitates informative and challenging sample selection, effectively optimizing the training data and further enhancing the model’s discriminative capability.Finally, we examine the combined effect of both the CEM and CGS modules by integrating them into the baseline model simultaneously. Remarkably, this joint integration yields remarkable performance enhancements, with mAP and Rank-1 accuracy increasing by 10.24% and 9.73%, respectively. The synergistic interplay between CEM and CGS reinforces the feature representation and sample selection aspects, leading to substantial overall improvements in the VI Re-ID task.The ablation study demonstrates the effectiveness and significance of both the CEM and CGS modules in our proposed approach. The CEM module successfully leverages contour information to enhance feature representation, while the CGS module optimizes the sampling strategy for informative and challenging examples. By understanding the individual contributions of these modules, our study offers valuable insights into the design of a robust and efficient VI Re-ID task.In our comparative experiments on different sampling methods, namely Random Sampler, Uniform Sampler, and our proposed Cross-modality Graph Sampler (CGS), we observe significant differences in their performances from Besides, considering For the selection of contour extraction methods, two options are considered: Canny edge detection and Self-Correction Human Parsing (SCHP). Therefore, a comparative analysis is conducted, and the specific results are shown in Furthermore, before employing the Element-wise Concatenation fusion method to merge the contour global features with the local features obtained from modal-shared feature learning, the output channel values of the contour global features are also worth our attention. In In this section, we compare our proposed method with the AGW  approachUpon analyzing the results, we observe that our method, with the inclusion of contour information and the utilization of the CGS sampler, effectively improves the retrieval performance. Specifically, in the case of the AGW method, there are instances of two erroneous matches in the rearview image retrieval, where these two images belong to the same class. In contrast, our method achieves correct Rank-10 results for all samples, indicating the superiority of the CGS sampler in distinguishing between similar samples from different classes. Moreover, when using the frontal view as a query sample, the AGW method shows three incorrect matches. These errors can be attributed to the similarity in backgrounds between these samples and the query sample. However, there are noticeable differences in the contour information between them. After enhancing the contour information in our method, only one matching error was observed, showcasing the significant role of contour assistance in enhancing matching capability.Overall, these qualitative analyses demonstrate that the integration of contour information and the utilization of the CGS sampler effectively address the challenges posed by modal discrepancies and improve the precision and accuracy of the VI Re-ID task.In this paper, we propose the Graph Sampling-based Multi-stream Enhancement Network (GSMEN) for the VI Re-ID task. The GSMEN integrates contour information with the globally shared contour features obtained from modal-shared feature learning. This integration aims to enhance feature representation and reduce cross-modal discrepancy. Our approach introduces the Contour Expansion Module (CEM) for fusing contour-enhanced features with local features and the Cross-modality Graph Sampler (CGS) for effective batch sampling. Experimental results on large-scale datasets demonstrate significant improvements in matching performance and modal adaptability. Our contributions include the novel CEM approach and the efficient CGS sampler, which show promising potential for VI Re-ID in various applications.


Camu camu (CC) is a prebiotic that selectively stimulates growth and activity of beneficial gut microbiota. Work in murine models demonstrated that castalagin, the active compound in CC, preferentially binds to beneficial gut microbiome bacteria, promoting a stronger CD8+T cell anti-cancer response. We present two patients with metastatic melanoma whose cancer progressed on immune checkpoint inhibitors (ICIs) and developed clinically significant immune-related adverse events (irAEs). They were rechallenged with ICIs in combination with CC. The first patient is a 71-year-old woman with metastatic melanoma, whose ICI treatment was complicated by immune-related pneumonitis and colitis. Upon progression on maintenance nivolumab, CC was added to nivolumab, leading to a near complete response (CR). The second patient is a 90-year-old man with recurrent unresectable melanoma, treated with nivolumab, complicated by immune-related rash and diabetes. He developed new subcutaneous calf lesions and a metastatic popliteal lymph node. CC was added to nivolumab. One month later, the patient experienced a CR. Both patients have been on nivolumab and CC with durable responses for more than a year, with minimal irAEs. These two cases suggest that CC may modulate the microbiome, synergizing with ICIs to produce deep, durable responses with minimal irAEs. Ruminococcus bromii) and increases their abundance. This promotes an increase in the cluster of differentiation 8 (CD8+)/cluster of differentiation 4 (CD4+)/forkhead box P3 (FOXP3+) T cell ratio and enhances the anti-cancer response to anti-programmed cell death protein 1 (anti-PD-1) agents in solid tumours in mice .RCC NCT052546 [p =cal data . MET4 wasiveness ,14,15. The results of our patients in this case series support a potential novel, easily accessible strategy of microbiome modulation for immunotherapy-refractory patients, which has been confirmed in prospective studies NCT05303493). We presented two cases of patients with metastatic melanoma who had disease progression on ICIs and then durable deep responses with the continuation of ICIs and the addition of CC. These cases support preclinical models of CC or its active component, castalagin, enabling re-sensitization to ICIs. Both patients had previous responses to ICIs , and it is unlikely that the tumour response with addition of CC was due to pseudoprogression, given that both patients were treated with ICIs for at least 5 months prior to progression. It is possible that the gut microbiome had been altered unfavourably in these patients, thus leading to progression on ICIs. For example, during her previous lines of therapy, patient #1 received five courses of antibiotics  for cellulitis and respiratory infections, which may have caused significant alterations in her gut microbiome. This is consistent with extensive published data demonstrating an association of antibiotic use with ICIs and worse survival outcomes, particularly in melanoma and renal cell carcinoma, which was confirmed in systematic reviews and meta-analyses ,8,9.3. We preRuminococcus bromii, which is associated with improved anti-tumour responses, by preferentially binding to the cellular membrane of Ruminococcus bromii and increasing its abundance. Abundance of this bacteria has been correlated with the dose-dependent anti-tumour effect of castalagin [In mouse models, CC supplementation is associated with antitumour activity that is microbiome-dependent and circumvents the anti-PD-1 resistance found in mice that received fecal matter transplant (FMT) from non-responsive patients with non-small cell lung cancer (NSCLC) . Castalastalagin . This case report supports further study of CC as a less expensive, less cumbersome, and more easily scalable alternative to FMT for immunomodulation of the microbiome to achieve improved responses to ICIs. It may also have a lower risk of adverse effects, compared to FMT, which is known to be associated with diarrhea, risk of transmitted infections, and hypophosphatemia ,4. CurreOf note, the rapidity of progressive disease may be a limiting factor for successful use of prebiotic modulation of the microbiome for patients with ICI-refractory cancer. Both of our patients had progression, but not rapidly progressive disease. Therefore, we were able to continue with immunotherapy while waiting for the effects of re-sensitization to ICIs by microbiome modulation.Given that our two patients had histories of significant irAEs prior to ICIs, their minimal irAEs on ICIs with CC raise interesting questions about irAEs and microbiome modulation with CC. In vitro and in vivo models show a correlation of CC with downregulated expression of proinflammatory cytokines and chemokines, such as those associated with the mitogen-activated protein kinases/activator protein-1 pathway, the nuclear factor kappa-light-chain-enhancer of activated B cells pathway, and the nuclear factor of activated T cells pathway ,17. DecrCC is a prebiotic that has demonstrated an additive effect to anti-PD-1 activity in mouse tumour models. There is evidence that the active component of CC, castalagin, shifts the microbiota composition and the systemic metabolite profile, and it has the potential to circumvent anti-PD-1 resistance in mouse models. Here, we presented two patients with ICI-refractory metastatic melanoma, who experienced durable CR/near CR after CC was added to their ICI regimen, with minimal irAEs. A phase I clinical trial is underway to further evaluate the therapeutic role of CC for ICI-refractory advanced melanoma and CC added to standard-of-care first line ICIs for advanced melanoma and NSCLC (NCT05303493). CC, or the active ingredient, castalagin, may prove to be a cost-effective, easily administered microbiome-modulating agent that synergizes with ICIs to induce and maintain durable tumour responses to ICIs.


BackgroundNanoparticle albumin-bound paclitaxel has been developed to avoid the toxicities associated with Cremophor-solved paclitaxel. Although many studies confirm this hypothesis, there is recent evidence showing no difference between paclitaxel and nab-paclitaxel in their efficacy and safety profile. This study further assesses the toxicity of both paclitaxel and nab-paclitaxel in adult patients with breast and pancreatic cancer in a tertiary hospital in Jeddah, Saudi Arabia. These toxicities include neutropenia, anaemia, and effects on kidney and liver functions.MethodsThe study is a retrospective cohort study done at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, from January 2018 to December 2021, conducted on patients diagnosed with breast or pancreatic cancer treated with paclitaxel or nab-paclitaxel.ResultsThere is a statistically significant difference between the two groups in developing anaemia, renal, and liver toxicity (P<0.05). On the other hand, there are no statistically significant differences in developing neutropenia between the two groups (P=0.084).ConclusionsNab-paclitaxel might not be better than paclitaxel in reducing the risk of neutropenia, anaemia, and liver toxicity, as predicted. Nevertheless, both medications require that the patient's renal functions be monitored during the treatment. Further studies conducted in multiple oncology centres with a larger sample are needed to evaluate the toxicity of paclitaxel and nab-paclitaxel in adult patients with breast and pancreatic cancer. Cancer is a disease characterised by uncontrollable cell proliferation and spread. There were an estimated 1.8 million diagnoses and more than 600,000 deaths from cancer in the USA in 2020 . PancreaDue to their ability to inhibit the cell proliferation cycle, taxanes are the most utilised chemotherapeutic medicines to treat various cancer types . The firBecause of this, paclitaxel has been modified utilizing nanotechnology to become nanoparticle albumin-bound paclitaxel (nab-paclitaxel) . This soIndeed, some clinical trials and meta-analyses showed the superiority of nab-paclitaxel to solvent-based paclitaxel, in terms of efficacy and safety, in patients with metastatic breast cancer and patients with pancreatic cancer -14. HoweIn one meta-analysis, side events such as neutropenia were found to be equivalent across the paclitaxel and nab-paclitaxel groups (P=0.26) . AlternaThis study aims to assess the toxicity of paclitaxel and nab-paclitaxel in adult patients with breast and pancreatic cancer in a tertiary hospital in Jeddah, Saudi Arabia. These toxicities include neutropenia, anemia, and effects on liver functions. In addition, one of our primary goals is to find a relation between nephrotoxicity and paclitaxel or nab-paclitaxel since we have not found studies assessing the relation between nephrotoxicity and paclitaxel or nab-paclitaxel.This study is a comparative retrospective cohort study conducted at King Abdulaziz University Hospital, Jeddah, Kingdom of Saudi Arabia, approved by the Ethics Committee of the same hospital .PatientsAll male and female patients older than 18 years who contacted the day care unit at King Abdulaziz University Hospital in the period between January 2018 to December 2020 were included. Patients were included if they were diagnosed with breast or pancreatic cancer and treated with paclitaxel or nab-paclitaxel. Patients could also have received fluorouracil or gemcitabine as adjuvant therapy.Exclusion criteria were patients < 18 years, patients diagnosed with cancer different from breast or pancreatic even if they were treated with paclitaxel or nab-paclitaxel, and patients diagnosed with breast or pancreatic cancer not treated with paclitaxel or nab-paclitaxel.Study endpointThe primary objective was to compare the toxicity of paclitaxel and nab-paclitaxel in adult patients with breast and pancreatic cancer. These toxicities include neutropenia, anaemia, nephrotoxicity, and liver toxicity.Statistical analysisData were transferred to IBM SPSS Statistics  for analysis. Unpaired T-tests, chi-square, and descriptive analysis were used to analyse the relationship between different variables in each group. Unpaired T-tests were used to analyse the relation between neutropenia, haemoglobin level, creatinine clearance, and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in each group (paclitaxel and nab-paclitaxel groups). Chi-square determined the P-value between the two groups; P<0.05 indicates statistically significant differences. Descriptive analysis was used to determine the mean, median, and average.PatientsOne hundred twenty-one adult patients  >18 years old, aged 35 to 60 years old, were treated at King Abdulaziz University Hospital in Jeddah from January 2018 to December 2021. These patients were diagnosed with breast cancer (n=84) or pancreatic cancer (n=37) regardless of clinical stages  and 15 with pancreatic cancer . Thirty-seven of these patients (14 of whom had breast cancer and 22 of whom had pancreatic cancer) had been treated with nab-paclitaxel.Safety and toxicityThe normal range of white blood cells (WBC) in humans is 4.5-11.5 K/µL . In the The expected haemoglobin level in males is 14 -16 g/dL, and in females, 12-16 g/dL . In the The usual range of creatinine clearance is 110 to 150 mL/min in males and 100 to 130 mL/min in females . The aveToxicity of the liver can be assessed by ALT and AST levels; the normal range of ALT is 12-78 IU/L. The average ALT level slightly increased and then decreased in the paclitaxel group. At the same time, in the nab-paclitaxel group, the average ALT level decreased throughout the three visits Table . There iFurther, the normal range of AST is 15-37 IU/L. The average AST level decreased during the third visit in the paclitaxel group. In contrast, in the nab-paclitaxel group, the average AST level decreased throughout the three visits  was first developed to avoid the toxicities associated with Cremophor-solved paclitaxel. However, our study shows that nab-paclitaxel might not be better than paclitaxel in reducing the risk of neutropenia, anaemia, and liver toxicity, as predicted. Nevertheless, both medications require that the patient's renal functions be monitored during treatment. Further comprehensive studies of the toxicity of paclitaxel and nab-paclitaxel in adult patients with breast and pancreatic cancer are required.


Our objective is to determine if specific sociodemographic characteristics were associated with perceived drug-related discrimination among people who use drugs (PWUD) presenting for care in the emergency department (ED).We conducted a secondary analysis of data from the Navigator trial, a randomized control trial of two behavioral interventions in the ED for people at risk of an opioid overdose. Participants included adult patients presenting to two Rhode Island EDs. Eligible participants included those high risk for an opioid overdose, resided or received most of their healthcare in Rhode Island, and were able to provide consent. The primary outcome of this analysis was self-reported feelings of drug-related discrimination by the medical community. The independent variables of interest included race/ethnicity, gender identity, and sexual orientation. Log-binomial multivariable regression models were constructed with all three independent variables of interest and a selection of sociodemographic covariates.Of 620 eligible participants, 251 (40.5%) reported ever experiencing drug-related discrimination in their lifetime. In the adjusted model, participants who identified as women and participants who identified as LGBQIA+ were more likely to report experiencing drug-related discrimination from the medical community in EDs. Racial/ethnic minority groups were less likely than White (non-Hispanic) participants to report drug-related discrimination.In this study population, White participants reported more drug-related discrimination than their minority counterparts, although female and LGBQIA+ patients reported more discrimination. Future studies should further assess the significance of these intersecting identities on self-reported discrimination. This knowledge could improve ED-based interventions, policies, and services for PWUD. Within One of the healthcare settings in which PWUD commonly report experiencing drug-related discrimination is the emergency department (ED) . With ma2This study is a secondary analysis of data from the Navigator trial, a randomized control trial of two behavioral interventions in the ED for people at risk of an opioid overdose, which has previously been described in detail.  Briefly,The primary outcome for this analysis was reporting ever feeling discriminated against by the medical community  due to their current or past drug use. The independent variables of interest included race/ethnicity, current gender identity, and sexual orientation. Self-reported race and ethnicity responses were then categorized into four mutually exclusive groups: African American/Black (non-Hispanic), Hispanic (any race), Other (non-Hispanic), and White (non-Hispanic). The Other category included American Indian/Alaska Native, Asian, Native Hawaiian/Pacific Islander, Mixed/Bi-racial/Multi-racial, and Self-Defined. Participants were asked what best described their current gender identity. Gender identities were categorized into three groups to minimize small cell sizes: woman, man, and non-binary/transgender/other. Lastly, sexual orientation was collected and categorized into two groups, straight and LGBQIA+.Individual bivariable analyses and log-binomial regression models were conducted for each independent variable of interest and the following covariates: age, highest level of education , having children under the age of 17 years old (yes vs. no), pre-pandemic enrollment , having health insurance (yes vs. no), previous experience with substance use disorder treatment (yes vs. no), ever receiving professional or non-professional recovery services (yes vs. no), ever tried to enroll into treatment but was unable to (yes vs. no), ever incarcerated (yes vs. no), ever homeless (yes vs. no), currently employed full time (yes vs. no), and average monthly income . Next, we constructed multivariable regression models with all three independent variables of interest and the following covariates: previous experience with substance use disorder treatment, ever incarcerated and ever homeless. All covariates included in the model were systematically assessed for inclusion into the final model using backward elimination approach. Participants were excluded from the analysis if they were missing the independent or primary dependent variables; missing covariate data were recorded as unknown. Analyses were conducted using R version 3.5.2 and SAS version 9.4 .3Of the 648 participants included in the parent trial, 28 (4.0%) participants were excluded for having missing data on race/ethnicity, gender, sexual orientation, or the study outcome. There were no statistically significant differences in demographics between those who were excluded from the study and those who remained.The remaining 620 eligible participants were predominantly White (non-Hispanic) (66.9%), identified as a man (68.1%), and straight   and Hispanic  participants had a lower prevalence of reporting experiencing discrimination by the medical community for their current or previous drug use compared to White (non-Hispanic) participants . In cont4In this study, EP patients at high risk for opioid overdose, experiences of drug-use related discrimination were common (40.5%), especially among participants who identified as women or LGBQIA+. Participants who identified as Hispanic (any race) or African American/Black (non-Hispanic) were less likely to report experiencing discrimination compared to their White counterparts. These findings could be a result of racial/ethnic minorities attributing the discrimination that they face in medical settings to their race/ethnicity instead of their experience using drugs.This prevalence of participants experiencing any kind of medical discrimination in our sample of PWUD, is approximately double that reported in other studies of non-PWUD . HoweverWomen and LGBQIA + patients were more likely to report experiencing drug-related discrimination from the medical community. These findings are corroborated by prior literature that found women and sexually diverse populations were more likely to report experiencing discrimination in healthcare settings . EffortsIn contrast to prior reports, racial/ethnic minorities in this sample were less likely to report experiencing discrimination due to their current or past drug use . These dThere are several limitations in our study which should be noted. First, participants included in the analysis were patients presenting at two EDs in a northeastern state of the US with a high incidence of opioid overdose, potentially limiting generalizability. Also, our survey instrument did not capture other forms of discrimination experienced by participants; therefore, we cannot determine if minority participants who did not report experiencing drug use-related discrimination were experiencing other forms of discrimination due to sex, gender, race/ethnicity, class, etc. Due to the intersectionality of participants’ identity as a racial/ethnic minority and drug use status, there are a sub-group of participants who were/are potentially experiencing discrimination, but who are attributing it to their race/ethnicity instead of their drug use . For thiAlthough experiences of discrimination varied within patient sub-groups, the prevalence across the whole sample was quite high. This highlights the need to improve patient-provider relationships among PWUD within minority populations and integrating socially amenable policies into EDs in order to protect PWUD who experience drug-related discrimination. Additional research is needed to determine how patient identities intersect with substance use status to influence an individual’s likelihood of reporting experiencing discrimination when interacting with the medical community. This is a necessary next step for achieving the goal of improving healthcare for PWUD and implementing more effective ED-based preventative interventions for reducing overdose events.Funding Sources:The Navigator trial was funded by Arnold Ventures and the Cigna Foundation through investigator-initiated trial programs. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.Shayla Nolen: Investigation, Conceptualization, Methodology, Software, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Taneisha Wilson: Conceptualization, Methodology, Writing – review & editing. Brendan P. Jacka: Conceptualization, Methodology, Writing – review & editing. Yu Li: Resources, Methodology, Writing – review & editing. Francesca L. Beaudoin: Funding acquisition, Conceptualization, Methodology, Writing – review & editing. Brandon D.L. Marshall: Funding acquisition, Supervision, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen . To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein–protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification. Misdiagnosis of the GC in the early stages, when no symptoms are apparent, and only the accumulation of genetic changes occurs at the molecular level, brings a heavy financial and physical burden to patients thus emphasizing the need for a clear understanding of the underlying molecular pathways2. Among the components of this molecular network are the miRNAs, "short-length post-transcriptional regulators," which are part of the dynamic genome changes that act in the initiation and progression of cancers such as GC4. Recent advances in genome-wide sequencing and the use of bioinformatics tools to analyze their data have led to new roads to further identifying components and their functions in this intertwined molecular network. Due to the complex nature of the disease, the current biomarkers lack the necessary power for accurate diagnosis and prognosis, as a result, more studies have been directed towards the discovery of computational methods such as machine learning to predict the miRNA-disease relationship and their use in cancer diagnosis5. In a recent study, the use of Boruta's machine learning algorithm in a GEO dataset of gastric cancer patients introduced 30 miRNAs with high biomarker power, and finally, hsa-miR-1343-3p has been introduced as a valuable diagnostic biomarker6. In another study, Rehman et al. investigated clinically validated miRNAs, using machine learning. The results not only confirmed their concept but also indicated the potential of miRNA as diagnostic biomarkers in breast cancer and introducing machine learning as a powerful tool in functional studies7. Utilizing data from the study of human tumors in large cohorts, the TCGA project has developed an extensive catalog of genetic changes associated with these tumors, make cancer genome and transcriptome uncovered and shed new light on the diagnosis, treatment, and prognosis of cancer, as in gastric cancer with the help of accurate classification of gastric adenoma cancers into four subgroups, (EBV-positive tumors (EBV), microsatellite unstable tumors (MSI), genomically stable tumors and chromosomally unstable tumors (CIN) has led to improved clinical diagnosis and treatment in this cancer8. This study aimed to find differentially expressed miRNAs (demiRs) in gastric cancer which can act as an important biomarker for early detection.Gastric cancer (GC) has a low 5-year survival rate in part due to misdiagnosis in the early stages of the disease and its recurrence, which makes this cancer one of the most prevalent cancers and the leading cause of cancer-related mortalityhttps://tcga-data.nci.nih.gov/), an available dataset that catalogs cancer-causing genomic changes8.Raw data from clinical information and sequencing of 576 stomach cancer were downloaded from the TCGA database (https://www.r-project.org/ ), to identify differentially expressed miRNAs (demiRs), adj. p < 0.05 and log|FC|> 1.5 were set as the threshold.Processing and analyzing of raw data were accomplished using R software . To define and visualize miRNAs with prognostic ability, the Kaplan Meier was used. Survival, survminer, and ggplot2 R packages were used for this purpose.9. One of its several applications is in medical sciences to identify biomarkers with significant diagnostic value for various diseases as in the wide range of cancers such as colorectal cancer, breast cancer, gastric cancer, as well as cardiovascular disease, Alzheimer’s disease, and etc.13. In this study, we used machine learning to find miRNAs with diagnostic and predictive values. For this purpose, in the processing step, important features were identified using heatmap analysis, and five methods  were used for classification; in the next step, all of these methods were compared with four different metrics  to find the most accurate algorithm. The algorithms were used in this study to measure the diagnostic ability of demiRs in GC which will be briefly mentioned in the following. One of the methods of evaluating the performance of binary classification is the "Receiver Operating Characteristic" or ROC curve. The efficiency of "Binary Classifier" algorithms is usually measured by indicators called "Sensitivity" or "Recall" but in the ROC chart, both of these indicators are combined and displayed as a curve. In the current study, the ROC curve is used to evaluate the efficiency diagnostic of selected hub miRNAs using R 4.2.2's combioROC package. For this purpose, sensitivity, specificity, cut-off value, positive predictive value, negative predictive value, and area under the ROC curve were measured. A support vector machine (SVM) algorithm is used for the classification of data points as well as regression in machine learning. SVMs have played an important role in a wide variety of biological applications, for example, the classification of microarray gene expression profiles in tumor and normal samples to find suitable diagnostic or prognostic biomarkers14. The K-Nearest Neighbors (KNN) algorithm is one of the simplest and at the same time the best algorithms used in supervised learning in the field of machine learning which considers the distance in classification and uses both “Regression” and “Classification” issues15. One of the methods of classification in “Supervised Machine Learning” is logistics regression. This method of regression, relies on an odd ratio for calculation. This type of analysis can help predict the probability of occurring an event or choice16. DTs: A decision tree is a tool for making a more appropriate decision in a way that gives a tree structure or hierarchical structure to decisions and their results. The structure of this tree can also be based on chance and probability, so choosing any decision randomly can bring risks or benefits17. Random Forest is an easy-to-use machine learning algorithm that often provides excellent results even without adjusting its meta-parameters. This algorithm is considered one of the most used machine learning algorithms due to its simplicity and usability, both for "Classification" and "Regression"14.Machine learning is defined as the intelligent analysis and design of models with the ability to find features with significant impact and discover the relationships between them based on algorithms and mass data analysis, which has comparable capabilities with human experts18. "System performance characteristic curve" (Receiver Operating Characteristic | ROC) is an appropriate evaluation index to ensure the validity of the results based on sensitivity and specificity. (Area Under Curve) is used as a measure to evaluate the performance of the category19. When the accuracy of the diagnosis of a category is more important compared to the accuracy of the overall diagnosis, the concept of "Confusion Matrix" comes to our aid. confusion matrix can give you a better idea of what your classification model is getting right and what types of errors it is making20.The accuracy parameter is the most commonly used, basic, and simple measure of the quality of a category, and it is the amount of correct recognition of the category in a total of two categorieshttps://gdac.broadinstitute.org/) and dbDMEC (https://www.biosino.org/dbDEMC/index ) webtools which contains the Differentially Expressed MiRNAs in human Cancers based on public repositories including Gene Expression Omnibus (GEO), Sequence Read Archive (SRA), ArrayExpress and The Cancer Genome Atlas (TCGA).In order to validated the candidate miRNAs which resulted by machine learning algorithms, we validated them using online web server such as, Global Data Assembly Centres (GDAC) 21, miRDB (http://www.mirdb.org/)22, and Target scan (https://www.targetscan.org/vert_80/)23, target genes of significant validated up and down expressed miRNAs were predicted.Using online databases for simple prediction of miRNA-targets such as miRwalk (https://string-db.org/)24 with confidence scores ≥ 0.9 as a threshold was used to find an interaction between the identified miRNAs’ targets.Search Tool for the Retrieval of Interacting Genes 25which annotate genes considering three aspects biological functions (BF), molecular functions (MF), and cellular components (CC), and Kyoto Encyclopedia of Genes and Genomes 26, we clarified the functional and enrichment analysis of the candidate genes.Using functional annotation tools such as 27 was used. Highly interconnected miRNAs and genes were then demonstrated. The flowchart of the methodology used in the current study is demonstrated in Fig. To obtain a clear visualization of the miRNAs-mRNAs interaction network, Cytoscape software (version 3.9.1)  men and 188 (35.1%) women included in this study were downloaded from TCGA and summarized in Table p-value of < 0.05 . The confusion matrix can also be seen in Fig. The dataset included 1882 miRNAs, which were reduced to 220 miRNAs after normalization using the Limma package, R software. In the processing step using a heat map, the most important features were selected  which is improved when combined with hsa-miR-93   have the prognostic capability , EXP00131 (GSE23739), EXP00230 GSE26595), EXP00268, EXP00270 (GSE54397) EXP00326 (GSE31568), EXP00337 (GSE59856), EXP00404, EXP00460 (GSE93415), EXP00524 (GSE106817), EXP00405, EXP00118(GSE28700), EXP00406, EXP00666, EXP00444 (GSE78775), EXP00476 (GSE99415), EXP00316 (GSE77380), and EXP00175(GSE33743)) supplementary file 95, EXP00The miRNA-target prediction was accomplished using several databases, that included: miRwalk, miRdb, and Targetscan. Using a Venn diagram online database, the list containing 407 common genes were identified Fig. B.Candidate genes predicted in the previous step were submitted to the STRING database to build a PPI network based on criteria mentioned in the material and method. To obtain hub genes with essential roles, the PPI network was then imported and visualized by Cytoscape software. 100 highly scored genes based on degree were selected using Cytohubba tools Fig. C. FinallTo reveal the role of selected hub genes, enrichment analysis was undertaken using R software. The results demonstrated that hub genes transcription factor binding, enzyme binding, RNA polymerase II cis-regulatory region sequence-specific DNA binding, protein binding, double-stranded DNA binding, arrestin family protein binding, sequence-specific DNA binding, and chromatin binding, regarding molecular function, The majority of genes were enriched in miRNA mediated inhibition of translation, Positive regulation by host of viral transcription, Regulation of gene expression by genetic imprinting, Production of miRNAs involved in gene silencing by miRNA Gene silencing by miRNA, Wnt signaling pathway, calcium modulating pathway, Regulation of cellular senescence, Negative regulation of gene expression, and epigenetic Gene silencing concerning the biological process. Chromatin, Euchromatin, Nucleoplasm, Non-membrane-bounded organelle and Cytosol were top enriched cellular components. Identification of significant signaling pathways using the KEGG database shows that candidate hub genes were mainly involved in Glioma, Melanoma, Prostate cancer, Non − small cell lung cancer, Renal cell carcinoma, GnRH secretion, Aldosterone − regulated sodium reabsorption, and Pancreatic cancer Fig. .Figure 628. Despite many advances in treatment including the use of adjuvant therapies such as surgery, chemotherapy, radiotherapy, and targeted therapy, the overall 5-year survival rate of the disease is still around 20–30% due to the diagnosis of patients in advanced stages of the disease29. Therefore, it is important to identify biomarkers that can detect the disease in its early stages and also identify its likely prognosis. MicroRNAs (miRNAs) are a small member of the non-coding RNAs family that control various post-transcriptional processes and possess important role in carcinogenesis including angiogenesis, drug resistance, and cancer metastasis31. In addition, the importance of miRNA-mRNA networks in activating or inhibiting many cancer-related molecular signaling pathways has recently been observed32. Recently, with the development of bioinformatics as well as the aid of machine learning algorithms, different forms of next-generation sequencing are increasingly being used to detect biomarkers with a role in early detection, treatment, and prognosis of cancer35. Machine learning approaches have gained popularity due to their predictive power of diagnostic and prognostic biomarkers. Studies also suggest the role of machine learning in feature selection such as miRNAs as an initial option when there is no prior knowledge37. In the present study, TCGA data analysis of stomach cancer using machine learning algorithms identified 29 miRNA candidates. Even though machine learning offers different algorithms for selecting a gene as a biomarker, none of them alone and without clinical validation can guarantee the true power of a biomarker and the reproducibility of the results requires more investigations and validations38. In order to overcome the limitations of machine learning, we validated the results obtained from it in different datasets such as GEO, TCGA, and GDAC, and among the 29 miRNAs found, hsa-miR-21, hsa-miR-133a, hsa-miR-146b, hsa-miR-29c, and hsa-miR-204 were among the most valid miRNAs in the investigated datasets.Gastric cancer is a heterogeneous disease with a poor prognosisSubsequent studies were designed to determine the role of these demiRs in gastric cancer through joint bioinformatics and prognosis analyses. In the first step, we explored the genes targeted by the five validated demiRs using online web server such as miRwalk, miRdb, and Targetscan. PPI network and cytohubba were then identified top 100 high-scored genes of each group of genes and functional and enrichment analyses were performed. GO and KEGG analysis determined that DEGs were mainly involved in cancer-related pathways. in concert, it was demonstrated that these feature miRNAs are highly correlated with miRNA mediated inhibition of translation , Regulation of gene expression by genetic imprinting, Production of miRNAs involved in gene silencing by miRNA Gene silencing by miRNA, Wnt signaling pathway, calcium modulating pathway, Regulation of cellular senescence, Negative regulation of gene expression based on functional enrichment analysis.39. In another study conducted by CHAN et.al the diagnostic potency of miR-21 were detected, although they claimed that this miRNA can’t serve as prognostic biomarker40. Simonian considered miR-21 as both diagnostic and prognostic biomarker and suggested that it could be higher diagnostic potential compare to CA-19-941. Also several study mentioned miR-133a as a tumor suppressor miRNA which inhibit the GC growth and exert its effect via different target genes such as Sp1, IGF1R, TCF4 and etc44. Although the effect of has-miR-146b is less investigated in GC, in the study, the over-expression of this miRNA is demonstrated in GC patients, which exert its effect on NOVA1 and associated with poor prognosis45. It is worth noting that based on the results of this study hsa-miR-29c have a dual biomarker potency and serves as both prognostic and diagnostic biomarker in GC. Although several studies mentioned the role of this miRNA as a diagnostic biomarker in GC, as far as our knowledge is concerned there isn’t any study concerning its prognostic capability. On the other hand, the machine learning method used in this study was demonstrated three significant down-regulated miRNAs has-miR-6510, has-miR-6507, has-miR-6512 in gastric cancer which recently gained attention in gastric cancer. Li et.al showed that miR-6512 downregulated in gastric cancer patients and seems to have a correlation with skin manifestations and fibrosis46. Moreover, a study by Ding demonstrated that miR-6507 was among top five down expressed miRNAs in GC and may serve as a good predictive biomarker47. Another study introduced miR-6510 as a potential prognostic biomarker in GC patients48. Although their role is not fully identified and need further investigation, these miRNAs could play an important role in GC.Survival analysis also reveal the prognostic capability of hsa-miR-21, hsa-miR-146b, hsa-miR-185, hsa-miR-1.1, hsa-miR-1.2, hsa-miR-143, hsa-miR-4652, hsa-miR-1911, hsa-miR-29c, hsa-miR-3170, hsa-miR-139, hsa-miR-5683, and hsa-miR-133a.2. On the other hand, ROC curve analysis demonstrated the diagnostic power of hsa-miR-29c in combination with hsa-miR-93. From these data, we can consider hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c a reliable biomarker panel. In a study by Larki et.al, the expression level of miR-21 and miR-93 were investigated in GC patients with the aim of finding a panel of diagnostic signature, it was demonstrated that the over-expression of these miRNAs were highly correlated with early diagnosis and prognosis of this cancerIn conclusion, 29 miRNAs were identified using machine learning algorithms, subsequent analyzes showed a panel of four miRNAs including hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c with high diagnostic and prognostic power, which was validated by several datasets. This study emphasizes the importance of machine learning as an alternative option for predicting biomarkers in gastric cancer. However, for the purpose of further validation, it is suggested that candidate miRNAs will be analyzed in several cohorts in a laboratory manner.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


We analyze recent results obtained from naturally derived CR and relevant rod-like macromolecules under microfluidic flows, with emphasis on the dynamical behavior in shear- and extensional-dominated flows. We highlight the key concepts required in order to assess and evaluate the results obtained from different CR and microfluidic platforms as a whole and to aid interconnections with neighboring fields. Finally, we identify and discuss areas of interest for future research directions.Naturally derived colloidal rods (CR) are promising building blocks for developing sustainable soft materials. Engineering new materials based on naturally derived CR requires an in-depth understanding of the structural dynamics and self-assembly of CR in dispersion under processing conditions. With the advancement of microfabrication techniques, many microfluidic platforms have been employed to study the structural dynamics of CR under flow. However, each microfluidic design has its  I.via lateral aggregation of soluble carbohydrate chains in specific ionic environments, as reported for low acetylated gellan gum and carrageenan polysaccharides.via self-assembly of hydrolyzed peptides under specific conditions of flow, pH, and ionic strength.Naturally derived colloidal rods (CR) are involved in a myriad of processes and applications. Well-known examples of naturally derived CR are cellulose nanocrystals [CNCs, shown in The concentration regimes for CR are based on the number of CR per unit volume, referred to as the number density For The correct estimation of the effective rotational diffusion coefficient Microfluidic devices are versatile platforms to study the flow-induced alignment of CR in different flow fields and for the retrieval of relevant time- and length-scales associated with the CR. For instance, microfluidic devices can be designed to generate two-dimensional (2D) flows that provide, to a good approximation, a uniform flow through the channel height . 2D flows are convenient for flow visualization and for techniques aimed at probing the structural properties of the fluid, such as flow-induced birefringence (FIB) and small-angle scattering (SAS). Alternatively, three-dimensional (3D) flows, generated in microfluidic devices with symmetric and/or low aspect ratio cross-sections, are more representative of processing condition under which naturally derived CR may be employed . Most importantly, microfluidics can be designed to generate not only shearing flows  but also extensional flows and mixed flows comprising both shearing and extensional deformations. Thus, coupling microfluidics with flow visualization techniques, pressure sensors, FIB, and/or SAS techniques provides a comprehensive fingerprint of the structure–property relationship of colloidal rods in unique flow scenarios. Moreover, the small length scales adopted in microfluidic devices allow relatively high deformation rates while preserving creeping flow conditions.This article provides an overview of microfluidic-based techniques to study the dynamics of naturally derived CR. Particular attention is given to CR in concentrations below the liquid crystal phase, where CR are isotropically distributed at rest but can adopt a favorable orientation under flow. In the following sections, we discuss some of the latest results regarding CR and relevant rod-like macromolecules under shearing and extensional flows.II.Understanding the flow-induced alignment of rod-like particles is pivotal for developing materials with structural anisotropy. Although mixed flows are present in real-life applications, studying the shear-induced alignment of CR can provide insightful information that can be interpreted and analyzed based on existing literature and be directly correlated with established rheological parameters . In our research group, we have used glass microfluidic devices, fabricated by selective laser-induced etching, with high aspect ratio [R, in θ . Analogoaxis Eq. .With relative ease, it is possible to link the flow-dependent structural properties of the fluid, such as et al.et al. could probe a decreasing value of With typical 2D microfluidic geometries, such as the one shown in eam path . Murphy shown in  using CRIII.Extensional flows are ubiquitous in most processing conditions, such as in jetting, dispensing and spraying of formulations, fiber spinning, and emulsion formation. A convenient way to study CR dynamics in extensional-dominated flows is by using microfluidics geometries. CR dynamics in planar extensional-dominated flows have been mainly studied using contraction-expansion (expansion-contraction)Lagrangian frame  although the flow is steady in the Eulerian frame . This makes data interpretation difficult, raising questions such as “what is the relevant extension rate and time-scale at play?,” “what is the effect of the inhomogeneous extension rate?,” and “has the fluid accumulated enough strain to align the CR?,” the answers to which are not always straightforward.When the primary interest is the study of CR in extensional flows, the simple designs of contraction-expansion geometries discussed above may not be the most suited, primarily due to the inhomogeneity of the extension rate. Indeed, CR flowing in such devices encounter a complex flow with constantly varying To generate shear-free flows with extension rates that are constant over a sufficiently large area, specific microfluidic geometries are required. For instance, the optimized hyperbolic microchannel device as shown in Lagrangian and Eulerian frames. In In our laboratory, we have used an optimized version of the cross-slot device, referred to as the optimized shape cross-slot extensional rheometer (OSCER), to study the CR alignment in planar extensional flows .9,32,33 The flow-focusing (FF) microfluidic geometry [sketched in  to zero . An effe z/h>3,  where thtube dilation from the Doi Edwards theory.The FF geometry has been used to estimate e≫1).t) . In the e≫1).t) .28,56,68et al.et al.in situ scattering experiments [et al.et al.et al.Recently Corona eriments . The ratectively . Additioectively . As suchIV.We have discussed typical microfluidic-based experiments that can be used to probe the structural dynamics of CR under controlled shear and extensional flows. We addressed potential areas of interest for future works, focusing on naturally derived CR in shear and extensional flows. Specifically, we pinpointed the requirement of additional experimental data regarding the CR microstructure-bulk property  relationship. For instance, it is valuable to understand the dependence of the shear viscosity, Based on our recent work on CR and the literature on soluble polymers in extensional flows, we highlighted the correlation between CR flexibility and CR propensity to align in an extensional-dominated flow. Although a qualitative link is somewhat clear, a quantitative description of this phenomenon is missing and could potentially provide a method to obtain a measure of particle flexibility using microfluidic tools. Overall, more experimental and theoretical research and a combination of the two are needed to obtain a generalized understanding of the dynamics of naturally derived CR under flow.


We investigated the relationship between the neural activity and real one-year sales provided by the restaurant chain. Activity in the ventral striatum, which was defined using the task-specific region of interest, significantly correlated  with one-year sales. A regression analysis, which included ventral striatum activity together with the objective characteristics of the products (price and weight), behavioral, and survey data, showed R2 values of 0.33. Overall, our results confirm prior studies, which have suggested, that brain activity in the reward system of a relatively small number of individuals can forecast the aggregate choice of a larger independent group of people.Neuroimaging studies have demonstrated the ability to use the brain activity of a group of individuals to forecast the behavior of an independent group. In the current study, we attempted to forecast aggregate choices in a popular restaurant chain. During our functional magnetic resonance imaging (fMRI) study, 22 participants were exposed to 78 photos of dishes from a new menu of a popular restaurant chain. In addition to self-reported preferences, fMRI data was extracted from an  Recent neuroimaging studies have suggested that the brain activity of a group of individuals can forecast the behavior of a separate and independent group of individuals –10. In tIn previous work in this field, a pioneering functional magnetic resonance imaging (fMRI) study revealed a significant correlation between brain activity in the ventral striatum (VS) and the ventral medial prefrontal cortex (vmPFC) with market-level song popularity during the three years following brain scanning . A few nAccording to a “partial-scaling” account, only a certain fraction of neurocognitive processes underlying individual choices can effectively forecast aggregate choice . The affInterestingly, certain neuroforecasting studies have examined the activity in the same predefined region of interest (ROI) at the VS to forecast a wide variety of aggregate choices, from video engagement in an internet attention market  to microsecondary rewards such as money. Nevertheless, a meta-analysis of 87 fMRI studies compared activations to monetary, erotic, and food reward outcomes , was selected according to the coordinate-based meta-analyses that examined the neural correlates of the valuation system , was specified using the local maximum from a previous neuroforecasting study [functional ROI) was specified using the local maximum of activation in dish preference contrast, and spherical volumes of interest (4 mm in radius) were placed in the right VS (MNI coordinates) [http://marsbar.sourceforge.net/). Importantly, for each ROI brain activity was averaged across participants. Next, we conducted Pearson’s correlation analysis of the relationship between the sales index and the right VS activity  during the initial presentation of dishes  , 8, 2. Bdinates) ). For thsales index. Each predictor, except objective characteristics, was standardized and normalized within subjects (z-score transform), and then averaged across subjects. In-scanner choices, survey data, and neural activity were averaged across participants and were treated as fixed effects. Dishes were grouped into eight dish categories and these dish categories were treated as random effects. We used the aggregate sales index as the dependent variable and up to seven predictors, including dish categories. The following eight models were tested to explore various predictors of the sales index , survey data, objective characteristics of dishes, and neural activity collected during the neuroimaging task could determine behavior at the population level, or Model I (null model) included only objective characteristics of the dishes (price and weight of each dish) as predictors.Model II included participants’ choices (whether or not participants chose the dish during the fMRI session) as predictors.Model III included survey data  as predictors.Model IV included VS activity  averaged over the first two brain volume acquisitions of each trial  as a predictor.Model V included all the above-mentioned variables.Model VI included all the above-mentioned variables except VS activity.Model VII was similar to Model VI, but instead of the functional ROI, it included neural activity at the ROIs  as defined based on a previous neuroforecasting study [ng study .Model VIII was similar to Model VI, but instead of the functional ROI, it included neural activity at the ROIs (VS and vmPFC) as defined based on the previous meta-analysis [analysis .in-scanner choices index:Additionally, we used the same approach to examine, which variables could determine In-scanner Model I (null model) included only the objective characteristics of the dishes (price and weight of each dish) as predictors.In-scanner Model II included the participants’ choices (whether or not participants chose the dish during the fMRI session) as predictors.In-scanner Model III included survey data  as predictors.In-scanner Model IV included VS activity  averaged over the first two brain volume acquisitions of each trial  as a predictor.in-scanner Model V included all the above-mentioned variables.Combined in-scanner choices index are presented in S4-S5 Tables in dish price, dish weight, choice in scanner, likeability, familiarity, price perception and brain activity as fixed effects and dish category as random effect.Model comparison results using likelihood ratio tests are presented in Five participants were excluded from the analysis because of excessive head movements during scans . Thus, the final data set included s participants . During the decision stage of the task, the participants selected 34.9% of dishes to be able to receive one of them after the study. Overall, the participants liked 79.5% of the dishes . We found no significant correlation between the sales index and the grand average VS activity at the meta-analysis-based ROI  or neuroforecasting study-based ROI . However, the averaged VS activity at the functional ROI significantly correlated with the sales index  sales indexfunctional ROI) were predictive of behavior at the population level  compared with Model I (AIC = 663), Model II (AIC = 657), Model III (AIC = 654) or Model IV (AIC = 658), despite penalties for additional predictors  objective characteristics (dish price and weight), (b) in-scanner behavior (choices), (c) survey metrics , and (d) VS activity , in-scanner choices, participants’ preferences, price perception, and objective product characteristics explained 33% of the variance in aggregate population behavior indicated by the sales index. VS activity alone explained 17% of the variance in the sales index.A combination of VS activity  were predictive of the in-scanner choices index (see in-scanner choices index contained no (in-scanner) behavioral predictor except survey metrics that were collected after the fMRI session. The AIC indicated a better fit of in-scanner Model II, which combined survey variables (AIC = 341), compared with in-scanner Model I (AIC = 410), in-scanner Model III (AIC = 426), and in-scanner Model IV (AIC = 344). Interestingly, VS activity alone (in-scanner Model III) was not significantly associated with the in-scanner choices index, while a combination of VS activity, participants’ preferences, price perception, and objective product characteristics (in-scanner Model IV) explained 69% of the variance in the in-scanner choices index.Additionally, we built separate linear regression models to estimate whether (a) objective characteristics (dish price and weight), (b) survey metrics , and (c) VS activity  meta-analysis-based ROIs and (c) neuroforecasting-based ROIs) improves better prediction of sales index. Results are presented in Finally, we built separate linear regression models to estimate, whether and which of the ROIs ((a) Our findings further support the view that neural activity underlying reward processing could predict decisions at the aggregate level. In an fMRI study, we analyzed the VS activity of 22 participants while they observed images of dishes from the new menu of a popular restaurant chain. This activity significantly correlated with the global (one-year) sales of the restaurant chain. Linear regression modeling indicated that the combination of neural data, choice, and survey data can account for global sales better than in-scanner choices or survey data alone. Taken together, our results offer modest support for a number of neuroforecasting findings, demonstrating that the VS activity of a group of participants may forecast the behavior of an independent group –10.Despite a growing number of neuroforecasting studies, the overall number of such studies—and the scope thereof—remains limited . To theThe VS is implicated not only in the anticipation of reward delivery , but alssales index. This is comparable with previous neuroforecasting studies. For example, a similar analysis conducted by Tong et al. explained 51% of the population’s behavior . Thu. Thusalein-scanner choices index in the combined supplementary models  framework, the “affective” VS activity may broadly generalize broadly across individuals, thereby revealing “hidden information” regarding mass preferences . Furtheres index . Thus, isales index. An activation likelihood estimation meta-analysis of 50 fMRI studies, which used the Monetary Incentive Delay Task, identified reliable activity, particularly in the right VS, to reward anticipation relative to loss anticipation [It is also worth noting that activity in the right (but not left) VS was significantly associated with cipation . A semincipation  demonstrcipation  or showecipation .sales index . In real life, making food choices may take a different amount of time than that taken in our study and may differ among different dishes. Here, the authors extract brain activity from the ROI was defined within the same task. This is especially problematic when the data are used to model in-sample behavior. Overall, further studies are clearly needed in order to extend our findings to real-life decisions. Moreover, additional information regarding seasonality, ad campaigns, current dish promotions, and in-restaurant activities may improve the predictive power of neuroforecasting.Overall, our results demonstrated an ability to neuroforecast aggregate choices of primary rewards. The hierarchical linear regression model demonstrated a significant association of VS activity with aggregate sales. Notably, a combination of neural, behavioral, survey data and objective product characteristics explained aggregate customers’ choices better than each predictor individually. Thus, our results extend the neuroforecasting approach to the domain of dining, thus making an additional step in the verification and development of the new field of neuroforecasting.S1 File(DOCX)Click here for additional data file.S1 Datasales index and in-scanner choices index); (3) Individual_data.mat ; (4) control regions data.Contains following data: (1) dishes.data ; (2) indexes.mat ((ZIP)Click here for additional data file.