A method to determine a personalized set of online exercises for improving the positive mental health of a caregiver of a chronically ill patient.

BACKGROUND: Taking care of chronic or long-term patients at home is an arduous task. Non-professional caregivers suffer the consequences of doing so, especially in terms of their mental health. Performing some simple activities through a mobile phone app may improve their mindset and consequently increase their positivity. However, each caregiver may need support in different aspects of positive mental health. In this paper, a method is defined to calculate the utility of a set of activities for a particular caregiver in order to personalize the intervention plan proposed in the app. METHODS: Based on the caregivers' answers to a questionnaire, a modular averaging method is used to calculate the personal level of competence in each positive mental health factor. A reward-penalty scoring procedure then assigns an overall impact value to each activity. Finally, the app ranks the activities using this impact value. RESULTS: The results of this new personalization method are provided based on a pilot test conducted on 111 caregivers. The results indicate that a conjunctive average is appropriate at the first stage and that reward should be greater than penalty in the second stage. CONCLUSIONS: The method presented is able to personalize the intervention plan by determining the best order of carrying out the activities for each caregiver, with the aim of avoiding a high level of deterioration in any factor.

Validation of a Deep Learning Algorithm for Diabetic Retinopathy.

Background:To validate our deep learning algorithm (DLA) to read diabetic retinopathy (DR) retinographies.Introduction:Currently DR detection is made by retinography; due to its increasing diabetes mellitus incidence we need to find systems that help us to screen DR.Materials and Methods:The DLA was built and trained using 88,702 images from EyePACS, 1,748 from Messidor-2, and 19,230 from our own population. For validation a total of 38,339 retinographies from 17,669 patients (obtained from our DR screening databases) were read by a DLA and compared by four senior retina ophthalmologists for detecting any-DR and referable-DR. We determined the values of Cohen's weighted Kappa (CWK) index, sensitivity (S), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV), and errors type I and II.Results:The results of the DLA to detect any-DR were: CWK = 0.886 ± 0.004 (95% confidence interval [CI] 0.879-0.894), S = 0.967%, SP = 0.976%, PPV = 0.836%, and NPV = 0.996%. The error type I = 0.024, and the error type II = 0.004. Likewise, the referable-DR results were: CWK = 0.809 (95% CI 0.798-0.819), S = 0.998, SP = 0.968, PPV = 0.701, NPV = 0.928, error type I = 0.032, and error type II = 0.001.Discussion:Our DLA can be used as a high confidence diagnostic tool to help in DR screening, especially when it might be difficult for ophthalmologists or other professionals to identify. It can identify patients with any-DR and those that should be referred.Conclusions:The DLA can be valid to aid in screening of DR.

Splenectomy reduces lung metastases and tumoral and metastatic niche inflammation.

The immune microenvironment plays a crucial role in supporting tumor growth and metastasis. Tumor-associated macrophages (TAMs) and neutrophils (TANs) are essential components of this microenvironment and affect tumor growth and progression in almost all solid neoplasms. Furthermore, TAMs, TANs and tumor-infiltrating dendritic cells (TIDCs) are found to infiltrate specific distant organs to prepare them as a site for metastatic cell seeding, forming the pre-metastatic niche. The spleen was identified as a major reservoir and source of circulating and tumor infiltrating immune cells. However, discrepancies about its role in supporting tumor growth exist. Thus, here we investigated the role of splenectomy in primary tumor and metastatic growth, and in the formation of an inflammatory niche. In a murine 4T1 and E0771 breast and Panc02 pancreatic cancer model, our results show that while splenectomy reduces the number of infiltrating TAMs, TANs and TIDCs within primary tumors, it does not affect its growth. In line, fewer TAMs, TANs and TIDCs accumulate in the metastatic microenvironment after splenectomy. Interestingly though, this affected metastatic growth depending on the metastatic route/site. The number of hematogenous breast cancer lung metastases was reduced after splenectomy but no effect was observed in breast or pancreatic lymph node metastases. Moreover, we observed that the immune composition of the pre-metastatic niche in lungs of breast cancer bearing mice was altered, and that this could cause the reduction of metastases. Altogether, our results highlight that splenectomy affects the immune microenvironment not only of primary tumors but also of pre-metastatic and metastatic sites.

A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application.

BACKGROUND: The aim of this study was to build a clinical decision support system (CDSS) in diabetic retinopathy (DR), based on type 2 diabetes mellitus (DM) patients. METHOD: We built a CDSS from a sample of 2,323 patients, divided into a training set of 1,212 patients, and a testing set of 1,111 patients. The CDSS is based on a fuzzy random forest, which is a set of fuzzy decision trees. A fuzzy decision tree is a hierarchical data structure that classifies a patient into several classes to some level, depending on the values that the patient presents in the attributes related to the DR risk factors. Each node of the tree is an attribute, and each branch of the node is related to a possible value of the attribute. The leaves of the tree link the patient to a particular class (DR, no DR). RESULTS: A CDSS was built with 200 trees in the forest and three variables at each node. Accuracy of the CDSS was 80.76%, sensitivity was 80.67%, and specificity was 85.96%. Applied variables were current age, gender, DM duration and treatment, arterial hypertension, body mass index, HbA1c, estimated glomerular filtration rate, and microalbuminuria. DISCUSSION: Some studies concluded that screening every 3 years was cost effective, but did not personalize risk factors. In this study, the random forest test using fuzzy rules permit us to build a personalized CDSS. CONCLUSIONS: We have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.

Role of retinal pigment epithelium-derived exosomes and autophagy in new blood vessel formation.

Autophagy and exosome secretion play important roles in a variety of physiological and disease states, including the development of age-related macular degeneration. Previous studies have demonstrated that these cellular mechanisms share common pathways of activation. Low oxidative damage in ARPE-19 cells, alters both autophagy and exosome biogenesis. Moreover, oxidative stress modifies the protein and genetic cargo of exosomes, possibly affecting the fate of surrounding cells. In order to understand the connection between these two mechanisms and their impact on angiogenesis, stressed ARPE-19 cells were treated with a siRNA-targeting Atg7, a key protein for the formation of autophagosomes. Subsequently, we observed the formation of multivesicular bodies and the release of exosomes. Released exosomes contained VEGFR2 as part of their cargo. This receptor for VEGF-which is critical for the development of new blood vessels-was higher in exosome populations released from stressed ARPE-19. While stressed exosomes enhanced tube formation, exosomes became ineffective after silencing VEGFR2 in ARPE-19 cells and were, consequently, unable to influence angiogenesis. Moreover, vessel sprouting in the presence of stressed exosomes seems to follow a VEGF-independent pathway. We propose that abnormal vessel growth correlates with VEGFR2-expressing exosomes release from stressed ARPE-19 cells, and is directly linked to autophagy.

Induced unbalanced linguistic ordered weighted average and its application in multiperson decision making.

Linguistic variables are very useful to evaluate alternatives in decision making problems because they provide a vocabulary in natural language rather than numbers. Some aggregation operators for linguistic variables force the use of a symmetric and uniformly distributed set of terms. The need to relax these conditions has recently been posited. This paper presents the induced unbalanced linguistic ordered weighted average (IULOWA) operator. This operator can deal with a set of unbalanced linguistic terms that are represented using fuzzy sets. We propose a new order-inducing criterion based on the specificity and fuzziness of the linguistic terms. Different relevancies are given to the fuzzy values according to their uncertainty degree. To illustrate the behaviour of the precision-based IULOWA operator, we present an environmental assessment case study in which a multiperson multicriteria decision making model is applied.

Distribution of Microaneurysms and Hemorrhages in Accordance with the Grading of Diabetic Retinopathy in Type Diabetes Patients.

(1) Underlying Diabetic Retinopathy (DR) is the primary cause of poor vision in young adults. There are automatic image reading systems that can aid screening for DR. (2) Methods: Using our automatic reading system we have counted the number of microaneurysms and hemorrhages in the four quadrants of the ETDRS grid and evaluated the differences between them according to the type of DR. The study was carried out using data from two different databases, MESSIDOR and MIRADATASET. (3) Results: The majority of microaneurysms and hemorrhages are found in the temporal and inferior quadrants of the ETDRS grid. Differences are significant with respect to the other two quadrants at p < 0.001. Differences between the type of DR show that severe-DR has a greater number of microaneurysms and hemorrhages in the temporal and inferior quadrant, being significant at p < 0.001. (4) Conclusions: The count of microaneurysms and hemorrhages is higher in the temporal and inferior quadrants in all types of DR, and those differences are more important in the case of severe-DR.

Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up.

(1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92-0.94) for any DR and 0.90 (95% CI, 0.89-0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient.

A semantic framework to protect the privacy of electronic health records with non-numerical attributes.

Structured patient data like Electronic Health Records (EHRs) are a valuable source for clinical research. However, the sensitive nature of such information requires some anonymisation procedure to be applied before releasing the data to third parties. Several studies have shown that the removal of identifying attributes, like the Social Security Number, is not enough to obtain an anonymous data file, since unique combinations of other attributes as for example, rare diagnoses and personalised treatments, may lead to patient's identity disclosure. To tackle this problem, Statistical Disclosure Control (SDC) methods have been proposed to mask sensitive attributes while preserving, up to a certain degree, the utility of anonymised data. Most of these methods focus on continuous-scale numerical data. Considering that part of the clinical data found in EHRs is expressed with non-numerical attributes as for example, diagnoses, symptoms, procedures, etc., their application to EHRs produces far from optimal results. In this paper, we propose a general framework to enable the accurate application of SDC methods to non-numerical clinical data, with a focus on the preservation of semantics. To do so, we exploit structured medical knowledge bases like SNOMED CT to propose semantically-grounded operators to compare, aggregate and sort non-numerical terms. Our framework has been applied to several well-known SDC methods and evaluated using a real clinical dataset with non-numerical attributes. Results show that the exploitation of medical semantics produces anonymised datasets that better preserve the utility of EHRs.

Effect of Lipids on Diabetic Retinopathy in a Large Cohort of Diabetic Patients after 10 Years of Follow-Up.

(1) Background: Diabetic retinopathy (DR) remains the leading cause of low vision and blindness in young adults of working age. Although the most important risk factors-such as the duration of diabetes mellitus (DM) and glycemic control measured by HbA1c-are known, the effects of lipids are not as clear. The aim of the present study is to analyze the effects of lipids on the development of DR. (2) Methods: This is a retrospective study of a population of 175,645 DM2 patients, during the period 2010 to 2020, in which the effects of different lipid factors are studied. (3) Results: The variables that most influenced the development of DR in our study, based on significance and cumulative hazard (CH), were arterial hypertension (CH 1.217, p < 0.001), HbA1c levels (CH 1.162, p = 0.001), microalbuminuria (CH 1.012, p < 0.001), LDL-C cholesterol (CH 1.007, p = 0.012), TC/HDL-C index (CH 1.092, p < 0.001), No-HDL-C/HDL-C index (CH 1.065, p = 0.002), the use of statins (CH 1.001, p = 0.005), and body mass index (CH 1.007, p < 0.001). (4) Conclusions: LDL-cholesterol, TC/HDL-C, and No-HDL-C/HDL-C indices are related to the development of DR, and there is a protective effect of HDL-cholesterol and the use of fibrates.

Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population.

Aim: The aim of the present study was to build a clinical decision support system (CDSS) that can predict the presence of diabetic retinopathy (DR) in type 1 diabetes (T1DM) patients. Material and Method: We built two versions of our CDSS to predict the presence of any-type DR and sight-threatening DR (STDR) in T1DM patients. The first version was trained using 324 T1DM and 826 T2DM patients. The second was trained with only the 324 T1DM patients. Results: The first version achieved an accuracy (ACC) = 0.795, specificity (SP) = 83%, and sensitivity (S) = 65.7% to predict the presence of any-DR, and an ACC = 0.918, SP = 87.1% and S = 87.8% for STDR. The second model achieved ACC = 0.799, SP = 87.5% and S = 86.3% when predicting any-DR and ACC = 0.937, SP = 90.9% and S = 83.0% for STDR. Conclusion: The two models better predict STDR than any-DR in T1DM patients. We will need a larger sample to strengthen our results.

Multi-Design Differential Expression Profiling of COVID-19 Lung Autopsy Specimens Reveals Significantly Deregulated Inflammatory Pathways and SFTPC Impaired Transcription.

The transcriptomic profiling of lung damage associated with SARS-CoV-2 infection may lead to the development of effective therapies to prevent COVID-19-related deaths. We selected a series of 21 autoptic lung samples, 14 of which had positive nasopharyngeal swabs for SARS-CoV-2 and a clinical diagnosis of COVID-19-related death; their pulmonary viral load was quantified with a specific probe for SARS-CoV-2. The remaining seven cases had no documented respiratory disease and were used as controls. RNA from formalin-fixed paraffin-embedded (FFPE) tissue samples was extracted to perform gene expression profiling by means of targeted (Nanostring) and comprehensive RNA-Seq. Two differential expression designs were carried out leading to relevant results in terms of deregulation. SARS-CoV-2 positive specimens presented a significant overexpression in genes of the type I interferon signaling pathway (IFIT1, OAS1, ISG15 and RSAD2), complement activation (C2 and CFB), macrophage polarization (PKM, SIGLEC1, CD163 and MS4A4A) and Cathepsin C (CTSC). CD163, Siglec-1 and Cathepsin C overexpression was validated by immunohistochemistry. SFTPC, the encoding gene for pulmonary-associated surfactant protein C, emerged as a key identifier of COVID-19 patients with high viral load. This study successfully recognized SARS-CoV-2 specific immune signatures in lung samples and highlighted new potential therapeutic targets. A better understanding of the immunopathogenic mechanisms of SARS-CoV-2 induced lung damage is required to develop effective individualized pharmacological strategies.

An ontology-based measure to compute semantic similarity in biomedicine.

Proper understanding of textual data requires the exploitation and integration of unstructured and heterogeneous clinical sources, healthcare records or scientific literature, which are fundamental aspects in clinical and translational research. The determination of semantic similarity between word pairs is an important component of text understanding that enables the processing, classification and structuring of textual resources. In the past, several approaches for assessing word similarity by exploiting different knowledge sources (ontologies, thesauri, domain corpora, etc.) have been proposed. Some of these measures have been adapted to the biomedical field by incorporating domain information extracted from clinical data or from medical ontologies (such as MeSH or SNOMED CT). In this paper, these approaches are introduced and analyzed in order to determine their advantages and limitations with respect to the considered knowledge bases. After that, a new measure based on the exploitation of the taxonomical structure of a biomedical ontology is proposed. Using SNOMED CT as the input ontology, the accuracy of our proposal is evaluated and compared against other approaches according to a standard benchmark of manually ranked medical terms. The correlation between the results of the evaluated measures and the human experts' ratings shows that our proposal outperforms most of the previous measures avoiding, at the same time, some of their limitations.

The Effect of HbA1c Variability as a Risk Measure for Microangiopathy in Type 1 Diabetes Mellitus.

BACKGROUND: To measure the relationship between variability in HbA1c and microalbuminuria (MA) and diabetic retinopathy (DR) in the long term. METHODS: A prospective case-series study, was conducted on 366 Type 1 Diabetes Mellitus patients with normoalbuminuria and without diabetic retinopathy at inclusion. The cohort was followed for a period of 12 years. The Cox survival analysis was used for the multivariate statistical study. The effect of variability in microangiopathy (retinopathy and nephropathy) was evaluated by calculating the standard deviation of HbA1c (SD-HbA1c), the coefficient of variation of HbA1c (CV-HbA1c), average real variability (ARV-HbA1c) and variability irrespective of the mean (VIM-HbA1c) adjusted for the other known variables. RESULTS: A total of 106 patients developed diabetic retinopathy (29%) and 73 microalbuminuria (19.9%). Overt diabetic nephropathy, by our definition, affected only five patients (1.36%). Statistical results show that the current age, mean HbA1c, SD-HbA1c and ARV-HbA1c are significant in the development of diabetic retinopathy. Microalbuminuria was significant for current age, mean HbA1c, CV-HbA1c and ARV-HbA1c. CONCLUSIONS: By measuring the variability in HbA1c, we can use SD-HbA1c and ARV-HbA1c as possible targets for judging which patients are at risk of developing DR and MA, and CV-HbA1c as the target for severe DR.

Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database.

BACKGROUND: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. METHODS: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). RESULTS: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. CONCLUSIONS: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database.

Validation of a Diagnostic Support System for Diabetic Retinopathy Based on Clinical Parameters.

Purpose: To validate a clinical decision support system (CDSS) that estimates risk of diabetic retinopathy (DR) and to personalize screening protocols in type 2 diabetes mellitus (T2DM) patients. Methods: We utilized a CDSS based on a fuzzy random forest, integrated by fuzzy decision trees with the following variables: current age, sex, arterial hypertension, diabetes duration and treatment, HbA1c, glomerular filtration rate, microalbuminuria, and body mass index. Validation was made using the electronic health records of a sample of 101,802 T2DM patients. Diagnosis was made by retinal photographs, according to EURODIAB guidelines and the International Diabetic Retinopathy Classification. Results: The prevalence of DR was 19,759 patients (19.98%). Results yielded 16,593 (16.31%) true positives, 72,617 (71.33%) true negatives, 3165 (3.1%) false positives, and 9427 (9.26%) false negatives, with an accuracy of 0.876 (95% confidence interval [CI], 0.858-0.886), sensitivity of 84% (95% CI, 83.46-84.49), specificity of 88.5% (95% CI, 88.29-88.72), positive predictive value of 63.8% (95% CI, 63.18-64.35), negative predictive value of 95.8% (95% CI, 95.68-95.96), positive likelihood ratio of 7.30, and negative likelihood ratio of 0.18. The type 1 error was 0.115, and the type 2 error was 0.16. Conclusions: We confirmed a good prediction rate for DR from a representative sample of T2DM in our population. Furthermore, the CDSS was able to offer an individualized screening protocol for each patient according to the calculated risk confidence value. Translational Relevance: Results from this study will help to establish a novel strategy for personalizing screening for DR according to patient risk factors.

STAT3-YAP/TAZ signaling in endothelial cells promotes tumor angiogenesis.

The nuclear translocation and activity of the cotranscriptional activators YAP and TAZ (YAP/TAZ) in endothelial cells (ECs) are crucial during developmental angiogenesis. Here, we studied the role of YAP/TAZ signaling in ECs in tumor angiogenesis and found that the expression of YAP/TAZ and downstream target genes in ECs correlated with tumor vascularization in human colorectal carcinomas and skin melanoma. Treatment with the YAP/TAZ inhibitor verteporfin reduced vessel density and tumor progression in a mouse colorectal cancer (CRC) model. Conditional deletion of YAP/TAZ in ECs reduced tumor angiogenesis and growth in a mouse B16-F10 melanoma model. Using cultured ECs and mice with EC-specific ablation, we showed that signal transducer and activator of transcription 3 (STAT3) was required for the activation of YAP/TAZ in tumor-associated ECs. Moreover, we showed that STAT3-mediated signaling promoted YAP/TAZ activity and that the nuclear shuttling machinery for STAT3 was also required for YAP/TAZ nuclear translocation. Together, our data highlight the role of YAP/TAZ as critical players in ECs during tumor angiogenesis and provide insight into the signaling pathways leading to their activation.

Metastasis-associated fibroblasts promote angiogenesis in metastasized pancreatic cancer via the CXCL8 and the CCL2 axes.

The characteristic desmoplastic stroma of pancreatic ductal adenocarcinoma (PDAC) is a key contributor to its lethality. This stromal microenvironment is populated by cancer-associated fibroblasts (CAFs) that interact with cancer cells to drive progression and chemo-resistance. Research has focused on CAFs in the primary tumour but not in metastases, calling into question the role of analogous metastasis-associated fibroblasts (MAFs). We infer a role of MAFs in murine hepatic metastases following untargeted treatment with the anti-angiogenic drug sunitinib in vivo. Treated metastases were smaller and had fewer stromal cells, but were able to maintain angiogenesis and metastasis formation in the liver. Furthermore, sunitinib was ineffective at reducing MAFs alongside other stromal cells. We speculate that cancer cells interact with MAFs to maintain angiogenesis and tumour progression. Thus, we tested interactions between metastatic pancreatic cancer cells and fibroblasts using in vitro co-culture systems. Co-cultures enhanced fibroblast proliferation and induced angiogenesis. We identify carcinoma-educated fibroblasts as the source of angiogenesis via secretions of CXCL8 (aka IL-8) and CCL2 (aka MCP-1). Overall, we demonstrate that metastasis-associated fibroblasts have potential as a therapeutic target and highlight the CXCL8 and CCL2 axes for further investigation.

Convexity shape constraints for retinal blood vessel segmentation and foveal avascular zone detection.

Diabetic retinopathy (DR) has become a major worldwide health problem due to the increase in blindness among diabetics at early ages. The detection of DR pathologies such as microaneurysms, hemorrhages and exudates through advanced computational techniques is of utmost importance in patient health care. New computer vision techniques are needed to improve upon traditional screening of color fundus images. The segmentation of the entire anatomical structure of the retina is a crucial phase in detecting these pathologies. This work proposes a novel framework for fast and fully automatic blood vessel segmentation and fovea detection. The preprocessing method involved both contrast limited adaptive histogram equalization and the brightness preserving dynamic fuzzy histogram equalization algorithms to enhance image contrast and eliminate noise artifacts. Afterwards, the color spaces and their intrinsic components were examined to identify the most suitable color model to reveal the foreground pixels against the entire background. Several samples were then collected and used by the renowned convexity shape prior segmentation algorithm. The proposed methodology achieved an average vasculature segmentation accuracy exceeding 96%, 95%, 98% and 94% for the DRIVE, STARE, HRF and Messidor publicly available datasets, respectively. An additional validation step reached an average accuracy of 94.30% using an in-house dataset provided by the Hospital Sant Joan of Reus (Spain). Moreover, an outstanding detection accuracy of over 98% was achieved for the foveal avascular zone. An extensive state-of-the-art comparison was also conducted. The proposed approach can thus be integrated into daily clinical practice to assist medical experts in the diagnosis of DR.

Reduction of Liver Metastasis Stiffness Improves Response to Bevacizumab in Metastatic Colorectal Cancer.

Tumors are influenced by the mechanical properties of their microenvironment. Using patient samples and atomic force microscopy, we found that tissue stiffness is higher in liver metastases than in primary colorectal tumors. Highly activated metastasis-associated fibroblasts increase tissue stiffness, which enhances angiogenesis and anti-angiogenic therapy resistance. Drugs targeting the renin-angiotensin system, normally prescribed to treat hypertension, inhibit fibroblast contraction and extracellular matrix deposition, thereby reducing liver metastases stiffening and increasing the anti-angiogenic effects of bevacizumab. Patients treated with bevacizumab showed prolonged survival when concomitantly treated with renin-angiotensin inhibitors, highlighting the importance of modulating the mechanical microenvironment for therapeutic regimens.