Search Results (1,059)

Background and Objectives: This systematic review and meta-analysis aimed to evaluate the efficacy of xenogeneic collagen matrix (XCM) in combination with a coronally advanced flap (CAF) in the management of multiple gingival recessions and to compare its outcomes with those achieved using the conventional connective tissue graft (CTG) + CAF approach. Materials and Methods: After searching and reviewing the literature in the electronic databases PubMed/PMC, Google Scholar, ScienceDirect, Cochrane Library, and LILACS, 601 publications were found. The titles and abstracts of 543 publications were screened. After evaluating the full text of 290 publications, based on the inclusion criteria, four randomized controlled clinical trials were included in the systematic review and meta-analysis. In all the studies, the test group was treated with XCM + CAF, whereas in the control group, CTG + CAF was used. Results: Clinical attachment level (CAL), gingival recession depth (GR), keratinized tissue width (KTW), and complete root coverage (CRC) statistically significantly (p < 0.05) improved in both groups in all of the studies. Inter-group comparison showed better results in the control group in individual studies. All clinical trials reported a statistically significant (p < 0.05) decrease in surgery time and less postoperative pain in the test group. The meta-analysis of KTW (−0.438 [95% CI, −0.714 to −0.163], p < 0.002) and GR (0.35 [95% CI, 0.098 to 0.602], p < 0.001) showed a significant difference between the test and the control groups in all of the studies. CAL (0.78 [95% CI, −0.305 to 0.461], p > 0.05) showed no statistically significant difference between test and control groups. Conclusions: CTG + CAF remains the gold standard in root coverage procedures. However, XCM offers a less invasive alternative with improved patient comfort, less postoperative pain, shorter surgical time, and acceptable clinical outcomes. Full article

Takotsubo syndrome (TTS) is a condition marked by sudden and temporary dysfunction of the left ventricle, occurring without significant coronary artery disease. It was previously thought to be a benign and self-limiting condition, associated with a favorable long-term prognosis and minimal impact on survival. However, the most recent findings provide evidence that TTS is a heterogeneous condition with various presentation patterns. Using the most recent evidence regarding long-term prognosis in TTS, this review article aims to provide an overview of the long-term survival of patients with TTS, highlighting potential risk factors and comorbidities that may worsen prognosis. It also explores the risk of recurrence and the utility of advanced imaging modalities for prognosis assessment. Risk factors negatively impacting long-term outcomes include male sex, older age, reduced left ventricular ejection fraction (LVEF), physical triggers (especially pulmonary and neurological diseases), and comorbidities such as atrial fibrillation, chronic obstructive pulmonary disease, and active cancer. Recurrence, though relatively uncommon, can affect up to 11% of patients, with “super recurrence” linked to higher peak troponin levels, lower LVEF, and emotional triggers. Advanced imaging modalities—such as coronary angiography and ventriculography, which are considered the gold standard, along with serial echocardiographic assessment—combined with cardiac biomarkers, including relatively low peak troponin levels and markedly elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP), as well as diagnostic ratios like copeptin/NT-proBNP, provide a robust framework for differentiating TTS from acute coronary syndromes. Key findings suggest that chronic therapeutic strategies in the long-term management of TTS patients should focus on improving long-term outcomes and reducing the risk of mortality and TTS recurrence. Methods: A comprehensive review was conducted using PubMed (U.S. National Library of Medicine and National Institutes of Health) and Google Scholar to identify relevant English-language publications addressing the long-term prognosis, biomarkers, imaging, risk of recurrence, and long-term management of TTS. Full article

Background: Since the 1990s, numerous investigations have assessed the diagnostic effectiveness—specifically sensitivity, specificity, and accuracy—of exercise stress testing (EST), stress echocardiography (SE), stress myocardial single-photon emission computed tomography (SPECT), and stress cardiac magnetic resonance imaging (CMR). However, the outcomes of these studies have often been inconsistent and inconclusive. To provide a clearer comparison, we conducted systematic reviews and meta-analyses aimed at quantitatively evaluating and comparing the aggregated diagnostic performance of these four commonly used techniques for detecting coronary artery disease (CAD). Methods: A comprehensive search of PubMed, Scopus, Embase, Cochrane Library, and Web of Science was conducted to identify cohort studies evaluating the diagnostic accuracy of EST, SE, stress myocardial SPECT, and stress CMR in symptomatic patients with suspected or confirmed CAD. The main goal was to compare their diagnostic value by pooling sensitivity and specificity results. Each study’s data were extracted in terms of true positives, false positives, true negatives, and false negatives. Results: A total of 104 studies, comprising 16,824 symptomatic individuals with either suspected or known CAD, met the inclusion criteria. The pooled sensitivities for CAD detection were 0.66 (95% CI: 0.59–0.72, p < 0.001) for EST, 0.81 (95% CI: 0.79–0.83, p < 0.001) for SE, 0.82 (95% CI: 0.78–0.85, p < 0.001) for stress myocardial SPECT, and 0.83 (95% CI: 0.81–0.85, p < 0.001) for stress CMR. Corresponding specificities were 0.61 (95% CI: 0.55–0.67, p < 0.001), 0.85 (95% CI: 0.82–0.87, p < 0.001), 0.74 (95% CI: 0.70–0.78, p < 0.001), and 0.89 (95% CI: 0.86–0.92, p < 0.001), respectively. Considerable heterogeneity was observed across the studies, as reflected by I² values ranging from 82.5% to 92.5%. Egger’s generalized test revealed statistically significant publication bias (p < 0.05 for all methods), likely due to the influence of smaller studies reporting more favorable results. Despite this, sensitivity analyses supported the overall robustness and reliability of the pooled findings. Conclusions: Among the diagnostic tools assessed, EST demonstrated the lowest accuracy for detecting obstructive CAD, whereas stress CMR exhibited the highest. Although stress myocardial SPECT showed strong sensitivity, its specificity was comparatively limited. SE emerged as the most balanced option, offering good diagnostic accuracy combined with advantages such as broad availability, cost-effectiveness, and the absence of ionizing radiation. Full article

This study examines how educational attainment and creative-class identity influence public library use among Black homeowners in Ward 8, Washington, D.C., a historically disinvested, yet resilient, Black community. Using an adapted theoretical framework (Chatman’s Small World Theory, Florida’s creative class theory, and Crenshaw’s intersectionality), the research investigates how symbolic capital informs institutional engagement in a racially homogeneous but economically stratified setting. A survey of 56 Black homeowners examined the relationships among education, income, creative-class identity, and library use. Logistic regression analysis revealed that higher educational attainment was a significant predictor of identification with the Black Creative Class^TM. However, neither income nor creative-class identity significantly predicted public library use. These findings challenge the assumption that middle-class status or creative-class affiliation ensures participation in educational or cultural institutions. Instead, they suggest that deeper dynamics, such as cultural relevance, perceived alignment, and trust, may shape engagement with public libraries. The study advances knowledge in library and information science (LIS) and urban studies by demonstrating how spatial context and class distinctions within Black communities shape library engagement. The results underscore the need for culturally responsive library strategies that recognize class-based variation within racial groups, moving beyond monolithic models of community outreach. Full article

Background: Occupational risk prevention is a critical discipline for ensuring safe working conditions and minimizing accidents and occupational diseases. With the rise of artificial intelligence (AI) and machine learning (ML), these approaches are increasingly utilized for predicting and preventing workplace hazards. This systematic review aims to identify, evaluate, and synthesize existing literature on the use of AI algorithms for detecting and predicting hazardous environments and occupational risks in the workplace, focusing on predictive modeling and prevention strategies. Methods: A systematic literature review was conducted following the PRISMA 2020 protocol, with minor adaptations to include conference proceedings and technical reports due to the topic’s emerging and multidisciplinary nature. Searches were performed in IEEE Digital Library, PubMed, Scopus, and Web of Science, with the last search conducted on 1 August 2024. Only peer-reviewed articles published from 2019 onwards and written in English were included. Systematic literature reviews were explicitly excluded. The screening process involved duplicate removal (reducing 209 initial documents to 183 unique ones), a preliminary screening based on titles, abstracts, and keywords (further reducing to 92 articles), and a detailed full-text review. During the full-text review, study quality was assessed using six quality assessment (QA) questions, where articles receiving a total score below 4.5 or 0 in any QA question were excluded. This rigorous process resulted in the selection of 61 relevant articles for quantitative and qualitative analysis. Results: The analysis revealed a growing interest in the field, with a clear upward trend in publications from 2021 to 2023, and a continuation of growth into 2024. The most significant contributions originated from countries such as China, South Korea, and India. Applications primarily focused on high-risk sectors, notably construction, mining, and manufacturing. The most common approach involved the use of visual data captured by cameras, which constituted over 40% of the reviewed studies, processed using deep learning (DL) models, particularly Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Conclusions: The study highlights current limitations, including an over-reliance on visual data (especially challenging in low-visibility environments) and a lack of methodological standardization for AI-based risk detection systems. Future research should emphasize the integration of multimodal data (visual, environmental, physiological) and the development of interpretable AI models (XAI) to enhance accuracy, transparency, and trust in hazard detection systems. Addressing long-term societal implications, such as privacy and potential worker displacement, necessitates transparent data policies and robust regulatory frameworks. Full article

Background/Objectives: Computed tomography (CT)-based radiomic analysis is an emerging technique that enables non-invasive assessment of tumor characteristics. In gastrointestinal stromal tumors (GISTs), radiomics may reflect biological behavior such as proliferative activity, often indicated by Ki-67 expression. To our knowledge, this is the first systematic review and meta-analysis synthesizing evidence on the ability of CT radiomics to predict the Ki-67 index in GISTs, addressing an important gap in the literature. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines to evaluate the predictive performance of CT radiomics for Ki-67 expression in GISTs. A literature search of PubMed, Scopus, Science Direct, and the Cochrane Library was performed up to December 2024 using predefined terms. Extracted data included study design, patient demographics, imaging protocols, radiomic features, and diagnostic performance. Study quality was assessed using the QUADAS-2 tool. A random-effects meta-analysis summarized the pooled area under the ROC curve (AUC), sensitivity, and specificity. Subgroup and sensitivity analyses explored heterogeneity sources. Publication bias was assessed using Egger’s test and funnel plots. Results: Six studies involving 1632 patients were included. The pooled sensitivity and specificity for predicting Ki-67 expression were 0.71 and 0.76, respectively, with a summary AUC of 0.79. Subgroup analyses showed consistent results across different imaging protocols and radiomic feature sets, though the Ki-67 cutoff (8% vs. 10%) affected diagnostic performance. Moderate heterogeneity and potential publication bias in specificity were observed. Conclusions: CT-based radiomics demonstrates moderate accuracy for non-invasively predicting Ki-67 index in GISTs. While not a substitute for histology, it may support personalized preoperative planning and guide future immunotherapy strategies. In the future, radiomic signatures—particularly when integrated with molecular or immune-related biomarkers—could help refine patient selection and monitoring strategies for emerging therapies, including immunotherapy. Full article

Malicious actors often exploit persistence mechanisms, such as unauthorized modifications to Windows startup directories or registry keys, to achieve privilege escalation and maintain access on compromised systems. While information technology (IT) teams legitimately use these AutoStart Extension Points (ASEPs), adversaries frequently deploy malicious binaries with non-standard naming conventions or execute files from transient directories (e.g., Temp or Public folders). This study proposes a threat-hunting framework using a custom Elasticsearch Security Information and Event Management (SIEM) system to detect such persistence tactics. Two hypothesis-driven investigations were conducted: the first focused on identifying unauthorized ASEP registry key modifications during user logon events, while the second targeted malicious Dynamic Link Library (DLL) injections within temporary directories. By correlating Sysmon event logs (e.g., registry key creation/modification and process creation events), the researchers identified attack chains involving sequential registry edits and malicious file executions. Analysis confirmed that Sysmon Event ID 12 (registry object creation) and Event ID 7 (DLL loading) provided critical forensic evidence for detecting these tactics. The findings underscore the efficacy of real-time event correlation in SIEM systems in disrupting adversarial workflows, enabling rapid mitigation through the removal of malicious entries. This approach advances proactive defense strategies against privilege escalation and persistence, emphasizing the need for granular monitoring of registry and filesystem activities in enterprise environments. Full article

Private information retrieval (PIR) is a typical application scenario of encrypted computing, which allows users to retrieve data from a database by providing only an encrypted index. In an academic research scenario, multiple parties may entrust their data to a third party and require collaborative retrieval. However, due to competitive relationships and mutual distrust between these parties, they do not share public–private keys, making single-key mechanisms inadequate for meeting actual privacy requirements. In this case, based on the multi-key fully homomorphic encryption (MKFHE) algorithm, we construct an efficient PIR scheme with an access permission verification mechanism and dynamic database. Specifically, we design an MKFHE algorithm to protect multi-user privacy information. The vector–matrix multiplication optimization algorithm is adopted to improve computational efficiency, the expand algorithm is used to reduce user communication traffic, and homomorphic multiplication with ciphertext chunking is used to avoid excessive noise caused by direct ciphertext multiplication. Experiments based on the SEAL library show that by transferring part of the computational pressure to the offline stage, the online query response efficiency of our scheme is improved by about 7.69%, and the online computational efficiency of vector–matrix multiplication is improved by about 19.7%. Full article

Accessing the built environment poses many challenges for people with disabilities, severely affecting their independence and quality of life. A panel of experts with a lived experience of disabilities co-designed a survey capturing the challenges in New Zealand’s public places. There were 319 survey respondents with impairments related to mobility (66.5%), vision (18.8%), hearing (5.0%), sensory processing and cognition (8.8%). They perceived sports stadiums as the least accessible venue, followed by bars, boutique shops and public toilets. The most accessible venues were supermarkets, libraries and shopping malls. The type of disability affected the main accessibility challenges. Significant outdoor barriers included uneven and cluttered paths, inadequate provision of curb cuts, seating and accessible parking spaces, and obscure wayfinding. Entrance barriers included heavy doors, complex access control, remote ramps and narrow, obscure entrances. Interior problems included cluttered paths and poor signage. The top priorities for improvement were simplifying layouts, keeping paths clear, and providing clear, inclusive signage, communication and assistance for people with varying impairments. Providing lower counters, better colour contrast, hearing loop facilities and better control of lighting and acoustics also improve accessibility. This research contributes novel experiential data from people with disabilities that is critical to achieving an inclusive built environment. Full article

Background: Breast cancer remains a critical public health problem worldwide and is a leading cause of cancer-related mortality. Optimizing clinical outcomes is contingent upon the early and precise detection of malignancies. Advances in medical imaging and artificial intelligence (AI), particularly in the fields of radiomics and deep learning (DL), have contributed to improvements in early detection methodologies. Nonetheless, persistent challenges, including limited data availability, model overfitting, and restricted generalization, continue to hinder performance. Methods: This study aims to overcome existing challenges by improving model accuracy and robustness through enhanced data augmentation and the integration of radiomics and deep learning features from the CBIS-DDSM dataset. To mitigate overfitting and improve model generalization, data augmentation techniques were applied. The PyRadiomics library was used to extract radiomics features, while transfer learning models were employed to derive deep learning features from the augmented training dataset. For radiomics feature selection, we compared multiple supervised feature selection methods, including RFE with random forest and logistic regression, ANOVA F-test, LASSO, and mutual information. Embedded methods with XGBoost, LightGBM, and CatBoost for GPUs were also explored. Finally, we integrated radiomics and deep features to build a unified multimodal feature space for improved classification performance. Based on this integrated set of radiomics and deep learning features, 13 pre-trained transfer learning models were trained and evaluated, including various versions of ResNet (50, 50V2, 101, 101V2, 152, 152V2), DenseNet (121, 169, 201), InceptionV3, MobileNet, and VGG (16, 19). Results: Among the evaluated models, ResNet152 achieved the highest classification accuracy of 97%, demonstrating the potential of this approach to enhance diagnostic precision. Other models, including VGG19, ResNet101V2, and ResNet101, achieved 96% accuracy, emphasizing the importance of the selected feature set in achieving robust detection. Conclusions: Future research could build on this work by incorporating Vision Transformer (ViT) architectures and leveraging multimodal data (e.g., clinical data, genomic information, and patient history). This could improve predictive performance and make the model more robust and adaptable to diverse data types. Ultimately, this approach has the potential to transform breast cancer detection, making it more accurate and interpretable. Full article

Kuwait has reported a problematic increase in the prevalence of Antimicrobial Resistance (AMR). However, the absence of studies that analyze AMR from combined human, agricultural (animal), and environmental domains limits our ability to assess the extent of the problem in Kuwait. Therefore, this systematic review provides a comprehensive insight into the AMR status in Kuwait regarding humans, agriculture (animals), and the environment from the perspective of the One Health approach. A systematic search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify the relevant literature on AMR in Kuwait. Multiple online electronic databases, including the Cochrane Library, Google Scholar, Web of Science, PubMed, BioMed Central (BMC), and Scopus, were searched to perform a narrative synthesis and meta-analysis. Twenty-eight studies published between 2009 and 2024 were included in this study. Domain-wise distribution varied, with 11 studies related to clinical settings, 11 to the environment, 4 to agricultural (animal), and 2 to both clinical and community settings. The narrative synthesis indicated a high occurrence of AMR bacteria in human, agriculture (animal), and environmental domains. In human domains, the dominant AMR isolates belonged to four Gram-negative species: E. coli, K. pneumoniae, P. aeruginosa, and Acinetobacter baumannii. In agriculture (animals), Salmonella isolates from poultry display high resistance to cefotaxime, ampicillin, and amoxicillin. Camel milk analyses revealed that 80% of bacterial isolates are resistant to antibiotics such as penicillin, tetracyclines, and carbapenems. An environmental analysis of sewage, seawater, sediment, and aerosol samples documented widespread antibiotic resistance genes (ARGs) with resistance mechanisms such as extended-spectrum beta-lactamase, carbapenemases, and colistin. The cross-domain analysis identified the overlapping of ARGs. Regarding the One Health approach, none of the studies used this approach to interlink these sectors. Meanwhile, the meta-analysis indicated a high resistance rate in humans (34.05%, 95% CI (22.81 to 46.27, p < 0.0001, I² = 98.94%)), agriculture (animals) (67.42%, 95% CI (30.30 to 94.93, p < 0.0001, I² = 97.40%)), and environment (69.86%, 95% CI (48.80 to 87.26, p < 0.0001, I² = 98.78%)). The reported spread of AMR and the overlap of resistance genes among isolates across the domains demonstrate the interconnected nature of AMR in Kuwait. These findings underscore the need to adopt the One Health approach to strengthen surveillance, implement control measures, and enhance public education strategies to address the complexity of AMR challenges in Kuwait. Full article

Public libraries serve as vital community hubs that foster engagement, empowerment, and education, particularly for vulnerable populations, including refugee children and families. This study examines how Oklahoma’s public libraries contribute to refugee resilience and identifies challenges they face in providing these essential services. Using a qualitative method approach, including 20 semi-structured interviews with library staff, questionnaire surveys, and observations conducted across three Oklahoma library systems (Metropolitan, Pioneer, and Tulsa City-County) the study explored programs, services, and strategies that support refugee adaptation and integration. Findings reveal that libraries excel in three key areas: cognitive services (language literacy, digital access, educational resources), socio-cultural services (community building, cultural exchange), and physiological services (safe spaces, welcoming environments). These services contribute to building human, social, and economic capital, with human capital consistently ranked as most crucial for refugee resilience. However, libraries face significant challenges, with language barriers, program gaps, and outreach limitations being the most prevalent obstacles. Additional barriers include facility constraints, transportation difficulties, resource limitations, and privacy concerns. The study proposes nine comprehensive guidelines for creating sustainable pathways to refugee resilience through enhanced library services, emphasizing proactive community engagement, staff training, multilingual resources, advocacy, strategic partnerships, tailored programming, transportation solutions, cultural competence, and welcoming environments. This study contributes to understanding how public libraries can function as inclusive institutions that support refugee children’s successful integration and development in their new communities. Full article

Objectives: Cervical cancer remains a significant global health burden for women. While neoadjuvant chemotherapy (NACT) has emerged as a potential treatment option, the prognostic implications of early non-response to NACT remain inadequately characterized. This systematic review aims to elucidate the association between early non-response to NACT and long-term disease-free survival (DFS) in cervical cancer patients. Methods: A comprehensive systematic review was conducted following PRISMA guidelines. PubMed, Embase, Elsevier, Springer, EBSCO, and Cochrane Library were systematically searched to identify eligible studies. Pooled hazard ratios (HRs) for DFS with 95% confidence intervals (CIs) were calculated using R software (version 4.5.1). Heterogeneity was assessed via Cochran’s Q test and I² statistics. Publication bias was evaluated using funnel plots, Begg’s test, Egger’s test, and trim-and-fill methods. Sensitivity analyses further validated result robustness. Results: Eleven studies (n = 2064 patients; 1546 responders, 518 non-responders) met inclusion criteria. The pooled early non-response rate ranged from 13% to 39%. Early non-response significantly correlated with poorer DFS (HR = 3.29, 95% CI 2.35–4.62). Subgroup analyses by response criteria showed HRs of 2.94 (95% CI 1.72–5.03) for WHO criteria and 4.00 (95% CI 2.52–6.34) for RECIST criteria. No significant publication bias was detected (Begg’s p = 0.35; Egger’s p = 0.28). Sensitivity analyses and trim-and-fill adjustments confirmed result stability. Conclusions: Early non-response to NACT predicts worse DFS in women with cervical cancer. These findings proposed the need for large-scale or prospective studies to validate the prognostic value of early non-response and optimize treatment strategies for non-responders. Future prospective trials with standardized protocols are essential to validate these findings and establish criteria for optimizing patient selection for NACT-based therapeutic strategies. Full article

Background: Freezing of gait (FoG) is one of the most debilitating motor symptoms in Parkinson’s disease (PD). It often leads to falls and reduces quality of life due to the risk of injury and loss of independence. Several types of wearable sensors have emerged as promising tools for the detection of FoG in clinical and real-life settings. Objective: The main objective of this systematic review was to critically evaluate the current usability of wearable sensor technologies for FoG detection in PD patients. The focus of the study is on sensor types, sensor combinations, placement on the body and the applications of such detection systems in a naturalistic environment. Methods: PubMed, IEEE Explore and ACM digital library were searched using a search string of Boolean operators that yielded 328 results, which were screened by title and abstract. After the screening process, 43 articles were included in the review. In addition to the year of publication, authorship and demographic data, sensor types and combinations, sensor locations, ON/OFF medication states of patients, gait tasks, performance metrics and algorithms used to process the data were extracted and analyzed. Results: The number of patients in the reviewed studies ranged from a single PD patient to 205 PD patients, and just over 65% of studies have solely focused on FoG + PD patients. The accelerometer was identified as the most frequently utilized wearable sensor, appearing in more than 90% of studies, often in combination with gyroscopes (25.5%) or gyroscopes and magnetometers (20.9%). The best overall sensor configuration reported was the accelerometer and gyroscope setup, achieving nearly 100% sensitivity and specificity for FoG detection. The most common sensor placement sites on the body were the waist, ankles, shanks and feet, but the current literature lacks the overall standardization of optimum sensor locations. Real-life context for FoG detection was the focus of only nine studies that reported promising results but much less consistent performance due to increased signal noise and unexpected patient activity. Conclusions: Current accelerometer-based FoG detection systems along with adaptive machine learning algorithms can reliably and consistently detect FoG in PD patients in controlled laboratory environments. The transition of detection systems towards a natural environment, however, remains a challenge to be explored. The development of standardized sensor placement guidelines along with robust and adaptive FoG detection systems that can maintain accuracy in a real-life environment would significantly improve the usefulness of these systems. Full article

Background: Dental professionals face high levels of occupational stress, which intensified during the COVID-19 pandemic, contributing to increased burnout, diminished well-being, and signs of accelerated biological aging. This systematic review explores the role of physical activity as a protective factor for mental and physical health, with a focus on promoting resilience and long-term occupational sustainability in a post-pandemic volatile, uncertain, complex, and ambiguous (VUCA) environment. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Studies published between 2000 and 2024 were identified through PubMed, Scopus, and the Cochrane Library using MeSH terms related to dentistry, physical activity, stress management, and occupational health. Of 850 records screened, 28 studies were included: 24 cross-sectional, 2 systematic reviews, 1 retrospective, and 1 case–control study. Inclusion and quality appraisal followed standardized criteria. Results: The included studies consistently showed that physical activity was associated with reduced burnout, improved psychological well-being, enhanced postural function, and potential markers of slowed biological aging in dental professionals. Several studies reported moderate-to-strong associations, with effect sizes ranging from small improvements in perceived stress scores to substantial reductions in MSD prevalence. Interventions combining exercise with ergonomic education, stress management, and institutional support demonstrated the stronger and more consistent benefits for professional sustainability. Conclusions: Physical activity, when integrated into comprehensive workplace wellness frameworks, significantly enhances the mental and physical resilience of dental professionals. Embedding movement, ergonomics, and psychosocial support into practice environments offers a promising strategy for safeguarding long-term workforce sustainability and improving public health outcomes. Full article