Progressive Trend Research Articles

Exploration of the research hotspot and application rule of Tengligen in anti-digestive system tumor based on bibliometric analysis and data mining

Objective: Based on the systematic bibliometric and data mining of the literature on the anti-digestive system tumor of Tengligen, the state of research, research hotspots and medication rules in this field were explored. Methods: From January 2004 to October 2024, literature pertaining to Tengligen’s anti-digestive system tumor was obtained from the CNKI, Wanfang, and VIP databases. CiteSpace 6.3.R1 and VOSviewer 1.6.20 software were used to intuitively display the number of publications, authors, institutions, keywords and other information, and then analyzed. The medical record information of famous Chinese medicine treating digestive system tumors with prescriptions containing Tengligen was extracted, a database was established, and the association rule analysis and cluster analysis of Chinese medicine compatible with Tengligen were carried out by IBM SPSS Modeler18.0, SPSS Statistics 26.0 and other software. Results: There were 115 publications in all, and there was a growing tendency in the quantity of papers published in this area. There were 295 authors total, with 28 serving as key authors. The primary publishing institutions are Zhejiang Chinese Medicine University, the First Clinical School of Zhejiang Chinese Medicine University, and Nanjing University of Chinese Medicine. Seven core tags were obtained by keyword cluster analysis, and research focuses on the mechanism of action, extract of Tengligen, clinical efficacy, etc. A total of 41 prescriptions containing Tengligen were included, involving 125 Chinese medicines, among which Tengligen was the most associated with Fuling, Huangqi and Yiyiren, respectively. Cluster analysis showed that Tengligen, Ezhu, Yiyiren and Huangqi were the core prescriptions. Conclusion: The scientific knowledge graph directly shows the research progress and hot trends of Tengligen against digestive system tumors, analyzes the rule of its clinical application, and provides a cutting-edge perspective for new research.

Effect of the COVID-19 Pandemic on Suicide Mortality in Brazil: An Interrupted Time Series Analysis

This study analyzed the effect of the COVID-19 pandemic on suicide rates among Brazilian residents, stratified by sex. It examined monthly suicide rates using interrupted time series analysis. Researchers compared the months before the pandemic (January 2017 to February 2020) with those after the first diagnosed case of COVID-19 in Brazil (March 2020 to December 2023). They applied an interrupted time series model (quasi-Poisson) to account for serial autocorrelation in the residuals and seasonality. During this period, authorities reported 102,081 suicides in Brazil. The age-standardized annual suicide rate among men was 3.71 times higher than the rate among women (12.33 suicides per 100,000 vs. 3.32 suicides per 100,000 women). The South and Midwest regions had the highest standardized annual average suicide rates. Suicide rates among men dropped abruptly at the pandemic’s onset (RR < 1, p < 0.05). However, Black men, women aged 15 to 19 years, and elderly individuals exhibited a significant increase (RR > 1, p < 0.05). Over time, suicide rates rose across most age groups, regions, and methods studied (RR > 1, p < 0.05). The pandemic’s impact differed significantly between men and women based on stratification variables. Nonetheless, a progressive upward trend emerged throughout the pandemic.

A 7,000 years trajectory of socio-ecosystems in the montane belt of the northern French Alps

IntroductionThis study, conducted on the Plateau des Glières, in the northern French Alps, investigates the evolution of vegetation composition over the past 7000 years. This site, within a montane ecosystem, offers a new opportunity to understand the human and landscape interactions under the influence of climate. To study these interactions as a whole and by integrating their complexity, we used two conceptual frameworks: the agroecosystem and the trajectory.MethodsThe analyses conducted are based on a detailed analysis of pollen, NPP, macro-charcoal, and geochemical data from peat cores.ResultsThe vegetation history of the Plateau des Glières is dominated by forest, due to its location within the montane belt (1,435 m). However, the vegetation composition trajectory of the Glières reveals three regimes: two are characterized by an evolutionary trend of the system, and between them, the a third regime is characterized by a “dynamic steady state” of the system. The first regime, corresponding to the Neolithic period, is initially characterized by a closed forested environment with a progressive trend toward a more open landscape due to the first agro-pastoral activities. The second regime is recorded from the very end of the Neolithic until the High Roman Empire. It corresponds to a relatively stationary system, characterized by the progressive development of pastoral activities leading to low impacts on the landscape. This long period (4300 to 1800 cal. BP) is characterized by the sustainability of the agroecosystem that developed. After this long pseudo-stable phase, a tipping point in the vegetation composition trajectory is highlighted. The third regime, spanning from the Migration Period to the present day, is the result of the intensification and diversification of agro-pastoral activities (pastoralism, cereal crops, and fruit trees). The landscape that we can see today in the Plateau des Glières is the legacy of this trajectory characterizing the interactions between ecosystems and human societies, i.e., the socio-ecosystem.DiscussionWhile the agroecosystem trajectory reconstructed on the Plateau des Glières is rather consistent with the altitudinal model of anthropization previously proposed for this region, the vegetation response to the activities is unusual because it shows a long phase of ecosystem stability despite the relatively high human pressure.

The effect of neuromuscular electrical stimulation superimposed on quadriceps training on gait dynamics after anterior cruciate ligament reconstruction

Background Abnormal gait dynamics are an important risk factor responsible for the high incidence of traumatic knee osteoarthritis in patients undergoing anterior cruciate ligament (ACL) reconstruction. Objective To evaluate the effect of neuromuscular electrical stimulation (NMES) superimposed quadriceps training on gait dynamics. Methods A total of 32 postoperative patients were randomly allocated to the NMES and conventional training (CT) groups, with 16 patients per group. Patients in the CT group received the standardized conventional training for 60 min per day, 3 days per week for 6 weeks, while those in the NMES group received the same conventional training, but with NMES superimposed on the conventional open chain training of the quadriceps. The amount of time for training was equal between the two groups. Patients were evaluated at 8 and 14 weeks after surgery. The outcome measurements were maximum voluntary isometric contraction (MVIC) of the quadriceps used to assess the quadriceps strength, Lysholm score used to assess knee function and peak vertical ground reaction force (PvGRF), heel medial (HM) peak pressure and heel lateral (HL) peak pressure used to assess gait dynamics. Results Compared to the CT group, the NMES group had greater improvements in quadriceps strength (p<0.001, effect size = 2.93), knee function (p = 0.049, effect size = 2.06), and gait loading (including HL peak pressure (P = 0.007, effect size = 0.89) and PvGRF (P = 0.019, effect size = 0.62)). After the intervention, participants in both groups had a significantly lower rearfoot impulse and significantly higher forefoot impulse. Conclusions The NMES superimposed on conventional quadriceps open chain training has shown additional improvements in gait loading in patients after ACL reconstruction, which suggests it may contribute to delaying the onset of early postoperative traumatic knee osteoarthritis. In addition, changes in foot impulse may suggest a progressive trend in quadriceps avoidance gait in the early postoperative period.

Soil Conservation Services and Its Enlightenment for Improving Ecosystems for Rocky Desertification Management: A Literature Review and Bibliometric Analysis

Soil conservation service (SC), as one of the most crucial ecosystem services (ES), plays a vital role in promoting the restoration of ecosystem structure. Severe soil erosion in karst areas has resulted in the ongoing degradation of soil conservation services. Understanding the current and evolution of research in this field is essential for establishing a theoretical foundation for the future management of rocky desertification in karst areas. Based on the Web of Science (WOS) and China Knowledge Network (CNKI) databases, 224 articles on SC were collected. This paper summarizes the research progress, results, and development trends from various aspects through systematic review and qualitative analysis of the literature. The findings are visually presented using spatial visualization and analysis tools such as SATI and Gephi. Key scientific issues and future development trends of existing SC are summarized with a view to improving SC in rocky desertification areas. It is of great significance to further improve the layout of ecological engineering construction and rocky desertification management in karst regions.

Determining the Degree of Preference Liking and Effectiveness of DMUs over a Long Period of Time by Means of a New Approach based on the Cross Efficiency Value Chain in Harmony with Fuzzy Arithmetic

In this paper, an alternative approach is presented for the evaluation of the likeability preference and effectiveness of DMUs, based on the DEA and fuzzy DEA models. In the magnitudes of variable values according to input-output levels, over the time period, some have not completely clear (fuzzy) values obtained from perceptions and surveys. For a more realistic assessment of effectiveness and determination of the degree of preference liking, to avoid accidental fluctuation values, and to get as close as possible to the trend of the process's progress, dynamic analysis of smoothing of the time series is applied to the input-output value levels. This is done according to a k-order moving average, determining the new levels of the input-output values. The approach is applied in two phases. In the first phase, the efficiency value chain matrix is determined, applying conventional DEA models with constant and variable returns to scale, evaluation of super efficiencies, fuzzy efficiency, and cross-efficiency. The data and the comparison of the models are analyzed, focusing in particular on the cross-efficiency value chain. In the second phase, fuzzy triangular numbers are composed of of the chain of cross-efficiency values for each DMU. Then based on fuzzy arithmetic as well as the concept given by the geometric probability model is determined and the transition matrix of the degree of preference liking, the evaluation of the ranking is obtained according to the degree of preference liking of each DMU in relation to other DMUs. In the paper, the contributions of the approach to the evaluation of the effectiveness and the degree of preference liking with the relevant conclusions are highlighted.

The Cause Analysis and Treatment of Alzheimer Disease

Alzheimer's Disease (AD) is a common neurodegenerative disease that mainly affects the elderly. The onset of this disease is insidious, and the course of the disease is characterized by chronic progressive development, mainly manifested as symptoms such as memory loss, language ability decline, spatial cognitive impairment, emotional and behavioral abnormalities, etc. As the condition worsens, the patient gradually loses their ability to live, ultimately leading to severe dementia. Recent studies have found that the age of onset of the disease is gradually shifting to younger. Once the onset of AD can not be reversed, care and treatment are very difficult, bring a heavy burden to the patient's family and society, scientific prevention can effectively delay or reduce the incidence.This article explores the etiology of AD, including genetics, lifestyle and environmental factors, biochemical mechanisms, and outlines current treatment methods, cutting-edge research progress and future trends, providing readers with a comprehensive understanding. Studies have shown that maintaining a clean environment, a balanced diet, moderate exercise and developing good social habits and positive emotional attitudes can help prevent the AD.

The evolving role of Finnish universities of applied sciences in the regional innovation ecosystem

ABSTRACT This article examines how Finnish universities of applied sciences (UAS) have developed as a form of higher vocational education and their current role in the education system, and how this is reflected in student flows and the share of research funding. Furthermore, we have aimed to increase understanding of the frameworks and conceptualisations that have contributed to modelling the position of UAS with respect to their relation to knowledge. Our study thus explores the central frameworks that have been important in framing their mission from this perspective. We present how approaches such as the Triple Helix model and innovation ecosystem and the competence-based approach have been important in shaping our understanding of their role in society and regions and in developing curricula. The study critically examines the relationship between UAS and knowledge by presenting a framework for a restricted versus expansive view of the relationship between UAS and knowledge. Finally, trends in UAS research, student enrolment, student progression and transition to the world of work are presented to give further ground for discussing the role of UAS with respect to knowledge. To conclude, the need for a future review of the purpose and role of UAS in the education system and new research and frameworks for envisioning their role are discussed.

Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis

In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in the field of diabetes, where the deep analysis of Electronic Health Records (EHR) has unlocked new opportunities for early detection and effective intervention strategies. Our research presents an innovative model that synergizes the capabilities of Bidirectional Long Short-Term Memory Networks-Conditional Random Field (BiLSTM-CRF) with a fusion of XGBoost and Logistic Regression. This model is designed to enhance the accuracy of diabetes risk prediction by conducting an in-depth analysis of electronic medical records data. The first phase of our approach involves employing BiLSTM-CRF to delve into the temporal characteristics and latent patterns present in EHR data. This method effectively uncovers the progression trends of diabetes, which are often hidden in the complex data structures of medical records. The second phase leverages the combined strength of XGBoost and Logistic Regression to classify these extracted features and evaluate associated risks. This dual approach facilitates a more nuanced and precise prediction of diabetes, outperforming traditional models, particularly in handling multifaceted and nonlinear medical datasets. Our research demonstrates a notable advancement in diabetes prediction over traditional methods, showcasing the effectiveness of our combined BiLSTM-CRF, XGBoost, and Logistic Regression model. This study highlights the value of data-driven strategies in clinical decision-making, equipping healthcare professionals with precise tools for early detection and intervention. By enabling personalized treatment and timely care, our approach signifies progress in incorporating advanced analytics in healthcare, potentially improving outcomes for diabetes and other chronic conditions.

Assessment of the economic and social impact of digitalization in Ukraine: a comprehensive analysis

This article provides a thorough analysis of the economic and social criteria for evaluating the effectiveness of digitalization in Ukraine. It particularly focuses on the relationship between the investment attractiveness of the digital sector and bridging the digital divide in society. Empirical data on the economic efficiency of digital transformations are analysed, including budget savings from the implementation of digital platforms and the growth of IT service exports. The analysis of statistical data reveals significant regional and social disparities in access to digital services. It establishes that the level of internet penetration in rural areas (45.8%) lags considerably behind that of urban areas (78.3%), posing risks of deepening socio-economic stratification in society. The phenomenon of duality in digital transformation in Ukraine is explored, characterized by the simultaneous existence of progressive trends (development of the IT sector, implementation of electronic services) and regressive trends (digital divide, uneven development). A paradoxical situation is identified, where high indicators of IT sector development coexist with significant internal disparities in regional digital progress. The public perception of digital innovations amid martial law is analysed, highlighting increased trust in government digital services, as evidenced by over 20 million users engaging with the “Diia” platform. However, concerns regarding cybersecurity and personal data protection are also noted. The study emphasizes the need to transform approaches to evaluating the effectiveness of digitalization, focusing on qualitative parameters of digital service usage and the development of citizens’ digital competencies. Conceptual foundations for improving state digitalization policy aimed at ensuring digital inclusion and overcoming regional disparities are proposed. The scientific novelty of the research lies in developing a comprehensive approach to assessing digitalization effectiveness that integrates economic and social criteria. The practical significance of the findings pertains to their potential use in enhancing state digital transformation policy during martial law and in the context of Euro integration processes.

Advances in biomaterials for dental implants: A comprehensive review of progress, challenges, and future trends

Advances in biomaterials for dental implants: A comprehensive review of progress, challenges, and future trends

Renal cell carcinoma and macrophage research: A bibliometric analysis (2004-2023).

To analyze hotspots and trends in renal cell carcinoma (RCC)-macrophage research using bibliometric analysis, although numerous studies on macrophages in RCC have been recently reported, understanding the progressive trends in this field remains challenging. Publications focused on macrophages in RCC were extracted from the Web of Science Core Collection. VOSviewer, Citespace, and Bibliometrics online platforms were used to visualize hot topics and global trends in RCC-macrophage research. In total, 778 papers were collected. China produced the most articles; however, the United States accounted for the largest number of citations. Oncology journals published the most articles, and these were cited most frequently. Based on keyword analysis, "prognosis," "immunotherapy," "tumor microenvironment," and "immune infiltration" represented the primary research hotspots. In summary, RCC-macrophage studies have emerged as a key research focus; particularly, incorporating multiomics data and applying artificial intelligence for predictive modeling have demonstrated significant potential. Our study suggests that the resistance mechanism of immune checkpoint inhibitors and the interaction between macrophages and immune checkpoint inhibitors will be pivotal areas for future research.

Current progress and future trends of clinical trials of CAR-T cell therapy for triple-negative breast cancer

Current progress and future trends of clinical trials of CAR-T cell therapy for triple-negative breast cancer

AI-Based Supply Chain Risk Management

As the complexity of global supply chains increases, supply chain risk management becomes increasingly important. The development of artificial intelligence (AI) technology provides new tools and methods for managing supply chain risks. This paper aims to review the application of AI in supply chain risk management, exploring current research progress, challenges, and future development trends. Through a systematic literature search and analysis, this paper examines relevant papers from databases such as Google Scholar, Web of Science, EI, and Scopus, discussing the specific applications of machine learning, deep learning, neural networks, fuzzy logic, genetic algorithms, and evolutionary algorithms in supply chain risk management. The study finds that these AI technologies demonstrate significant advantages in risk prediction, anomaly detection, image recognition, text mining, supply chain optimization, and emergency strategy formulation. However, issues related to data privacy and security, technical complexity, and implementation difficulties remain major challenges in current applications. In the future, as AI technology advances and interdisciplinary integration develops, supply chain risk management will have more opportunities. This study provides specific application suggestions for enterprises and decision-makers and points the way for future research.

Relationship Between Breast Density and Ptosis Degree in Direct-to-Implant Breast Reconstruction.

Direct-to-implant breast reconstruction is a popular procedure since breast volume can be acquired immediately. Various evaluations have been utilized to achieve symmetry. We measured the weight and volume of mastectomy specimens and calculated the relationship between breast density and ptosis degree. We conducted a retrospective chart review of patients who had undergone direct-to-implant breast reconstruction. Clinical data from 155 patients were reviewed, and they were classified based on breast density (weight/volume) (Group 1: density < 0.9 g/mL [n=17], Group 2: 0.9 g/mL ≤ density <1.1 g/mL [n=59], and Group 3: density ≥ 1.1 g/mL [n=79]). The relationship between breast density and ptosis degree was analyzed using the Cochran-Armitage test for trend. Adequate suture and anchoring techniques were applied to achieve symmetric breasts. Postoperative outcomes were assessed by measuring the discrepancy in nipple height. A statistically significant relationship was found between higher breast density and a greater degree of ptosis. The outcome showed a significant trend across Group 1, 2 and 3 (p<0.05). In postoperative analysis, the nipple level discrepancy was less than 10 mm as follows: 100% in Group 1, 89.8% in Group 2, and 70.9% in Group 3, showing a progressive decreasing trend from Group 1 to Group 3 (p<0.05). Direct-to-implant breast reconstruction is an attractive technique, offering predictable outcomes based on adequate measurements. The relationship between breast density and the degree of ptosis is a significant indicator in making intraoperative decisions. Breast density is an adjunctive index for achieving patients' expectations of symmetrical breasts. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Quality of Life as a Non-mortality Patient-centred Outcome in the Critically Ill: A Retrospective Analysis.

Mortality is a common gauged endpoint in critically ill patients. Reduced quality of life is an aligned repercussion of protracted critical illness. Baseline status, severity of illness and its trajectory influence the outcomes. Patient-oriented outcomes are those that matter the most to a patient. However, quite often, family approves of trade-offs with survivorship in the Indian context. We looked at non-mortality outcomes in patients on high-intensity life-sustaining interventions admitted to the intensive care unit (ICU) despite poor prognosis and died on full support or survived to be completely dependent. In this retrospective chart review study, we studied patients (1) who spent more than 1 month in the hospital enduring a myriad of distressing physical and psychological vicissitudes, (2) whose primary illness was fairly advanced (3) and either succumbed or survived to be impeded in their response to cognitive assessment and with severe functional impairment. Patient demographics, comorbidities, pre-morbid functional status, burden of critical illness, use of life-sustaining therapies, functional dependence in the last week of ICU stay, best neurological status in the last week pre-death or discharge, dying trajectories and economic analysis were noted. Trends of clinical progress of 23 patients were deliberated. The mean age of males was 65 years and 61 years for females. Five patients had a Barthel index score of 10-20, indicating total dependency and two patients had a score of 21-60, indicating severe dependency. Two patients were cognitively impaired at baseline. The worst neurological status in the week before death or discharge was eye1, motor1, and verbaltracheostomised. Thirteen patients succumbed during ongoing treatment. Daily discussions on the dynamics of illness progression need to take place with family on a regular basis for patients managed in ICU. Realistic perceptions and grounded expectations from the families and caregivers are necessary for patient-centred outcomes.

Cardiovascular Health Metrics of Spouses of Indian Women with a History of Gestational Diabetes and Diabetes in Pregnancy: Results from CHIP-F Study.

There are limited data on the prevalence of cardiovascular risk factors/diseases (diabetes, obesity, hypertension, and dyslipidemia) and their composite scores reflecting overall cardiovascular health among young (< 50years old) married couples. We have an established longitudinal prospective cohort of postpartum women with a history of hyperglycemia [pre-existing diabetes (PED; n = 101), overt diabetes in pregnancy (ODiP; n = 92), gestational diabetes (GDM; n = 643)], and normoglycemia (n = 183) in pregnancy and their spouses (n = 819). In this study, we report data from baseline cross-sectional evaluation of these 819 young couples regarding the burden of cardiovascular risk factors/diseases, their concordance and associations. The mean (SD) age was 33.5 (4.9) years for women and 36.9 (5.4) years for men, and the evaluation was performed at a median (IQR) postpartum interval of 30 (20-45) months. Diabetes, hypertension, obesity, and dyslipidemia were present in 25.0%, 6.9%, 25.6%, and 4.0% of women and 15.8%, 20.2%, 14.3%, and 8.2% of men, respectively. Among men, dysglycemia (diabetes/prediabetes) and adverse cardiovascular score (average/poor) showed a significant trend in progression according to the antenatal categories of their partner (highest in DIP, intermediate in GDM, and lowest in normoglycemia). The concordance was higher for adverse cardiovascular score (58.5%), overweight/obesity (48.7%), and dysglycemia (42.4%) compared to prehypertension/hypertension (29.2%) and hypercholesterolemia (8.2%). The odds ratios were significant for all associations evaluated, except for elevated blood pressure, being strongest (OR > 2.0) for overweight/obesity and adverse cardiovascular score. Nearly three in five young couples evaluated in the study showed concordance for adverse cardiovascular scores. The cardiovascular risk in men increased in a graded manner across the increasing severity of antenatal glycemic categories of their partners.

Neutrophil-to-lymphocyte ratio as a prognostic indicator of mortality in Polycythemia Vera: insights from a prospective cohort analysis

We analyzed the neutrophil-to-lymphocyte ratio (NLR) in 1508 patients with PV and found that those with an NLR ≥ 5 were generally older, had a longer disease history, and had higher cardiovascular risk factors, more arterial thrombosis, and more aggressive blood counts, indicating a more proliferative disease. NLR was an accurate predictor of mortality, with patients with NLR ≥ 5 having significantly worse overall survival and more than twice the mortality rate compared to those with NLR < 5. Multivariable models confirmed that increasing age, previous venous thrombosis and NLR ≥ 5 were strong predictors of death, further influenced by cardiovascular risk factors. We examined the interaction between NLR and the number of cardiovascular risk factors and found a progressive trend of increased mortality risk for NLR values ≥ 5 in addition to the presence of more than one risk factor. In conclusion, patients with NLR ≥ 5 require careful monitoring and management of cardiovascular risk factors because they increase mortality when associated with progressive levels of NLR.

Organoids research progress in gynecological cancers: a bibliometric analysis.

Gynecological cancers (GC) pose a severe threat to the health and safety of women's lives, and organoids, as in-vitro research models, have demonstrated significant advantages in simulating tissue characteristics and drug screening. In recent years, there has been a rapid increase in research outcomes related to organoids in GC. However, there has been no bibliometric study concerning. Publications related to GC and organoids from 2010-2023 were retrieved from the Web of Science Core Collection (WoSCC). We conducted a bibliometric analysis and visualization using CiteSpace, VOSviewer, and the Bibliometrix R Package. This analysis included the spatiotemporal distribution, author, sources, references, and keywords. A total of 333 publications were included. The number of annual publications indicated an explosive phase of development since 2019. The USA was the most important country in terms of cooperation, publication output, citation and centrality. University of California system ranked first in productivity among institutions, and HIPPO Y is the most relevant author in the research field. CANCERS published the most documents, and NATURE is the most cited sources. Analysis of Keywords and References, it is possible to establish the trend, and find the hotspots in the research field. This bibliometric analysis delineated global landscapes and progress trends in GC organoids research. This study emphasized that organoids can effectively replicate the original tissue or tumors, providing a good in-vitro model for research on tumor-related mechanisms and showing significant advantages in drug screening and efficacy clinical prediction. Additionally, as preclinical models, they provide compelling evidence for personalized therapy and prediction of patient drug responses.

Fostering long-term conviviality for refugees in the city: Inclusive, collaborative housing in Vienna

Vienna faces a ‘twofold housing and refugee crisis’ amid ongoing migration to Europe. As housing options become increasingly limited, refugees, migrants, and asylum seekers often struggle to find adequate housing, spending a disproportionate share of their income on substandard options. This research highlights progressive trends in Vienna’s refugee housing sector, particularly following the 2009 introduction of ‘Social Sustainability’ in building competitions by the City Council. This initiative has fostered several collaborative housing communities that support inclusive living. By analyzing four of these projects, the study identifies key factors that facilitate newcomers' social and spatial integration. Ultimately, the paper aims to advocate for newcomers’ right to quality housing, emphasizing the everyday and long-term challenges faced by inclusive communities striving for conviviality in a state of thrown togetherness.