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Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

Schizophrenia is a very complex syndrome involving widespread brain multi-dysconnectivity. Schizophrenia is marked by cognitive, behavioral, and emotional dysregulations. Recent studies suggest that inflammation in the central nervous system (CNS) and immune dysfunction could have a role in the pathogenesis of schizophrenia. This hypothesis is supported by immunogenetic evidence, and a higher incidence rate of autoimmune diseases in patients with schizophrenia. The dysregulation of the WNT/β-catenin pathway is associated with the involvement of neuroinflammation in schizophrenia. Several studies have shown that there is a vicious and positive interplay operating between neuroinflammation and oxidative stress. This interplay is modulated by WNT/β-catenin, which interacts with the NF-kB pathway; inflammatory factors (including IL-6, IL-8, TNF-α); factors of oxidative stress such as glutamate; and dopamine. Neuroinflammation is associated with increased levels of PPARγ. In schizophrenia, the expression of PPAR-γ is increased, whereas the WNT/β-catenin pathway and PPARα are downregulated. This suggests that a metabolic-inflammatory imbalance occurs in this disorder. Thus, this research’s triptych could be a novel therapeutic approach to counteract both neuroinflammation and oxidative stress in schizophrenia.

Digital twin technology is revolutionizing healthcare systems by leveraging real-time data integration, advanced analytics, and virtual simulations to enhance patient care, enable predictive analytics, optimize clinical operations, and facilitate training and simulation. With the ability to gather and analyze a wealth of patient data from various sources, digital twins can offer personalized treatment plans based on individual characteristics, medical history, and real-time physiological data. Predictive analytics and preventive interventions are made possible by machine learning algorithms, allowing for early detection of health risks and proactive interventions. Digital twins can optimize clinical operations by analyzing workflows and resource allocation, leading to streamlined processes and improved patient care. Moreover, digital twins can provide a safe and realistic environment for healthcare professionals to enhance their skills and practice complex procedures. The implementation of digital twin technology in healthcare has the potential to significantly improve patient outcomes, enhance patient safety, and drive innovation in the healthcare industry.

Inflammation and oxidative stress are common and co-substantial pathological processes accompanying, promoting, and even initiating numerous cancers. The canonical WNT/β-catenin pathway and peroxisome proliferator-activated receptor gamma (PPARγ) generally work in opposition. If one of them is upregulated, the other one is downregulated and . WNT/β-catenin signaling is upregulated in inflammatory processes and oxidative stress and in many cancers, although there are some exceptions for cancers. The opposite is observed with PPARγ, which is generally downregulated during inflammation and oxidative stress and in many cancers. This helps to explain in part the opposite and unidirectional profile of the canonical WNT/β-catenin signaling and PPARγ in these three frequent and morbid processes that potentiate each other and create a vicious circle. Many intracellular pathways commonly involved downstream will help maintain and amplify inflammation, oxidative stress, and cancer. Thus, many WNT/β-catenin target genes such as c-Myc, cyclin D1, and HIF-1α are involved in the development of cancers. Nuclear factor-kappaB (NFκB) can activate many inflammatory factors such as TNF-α, TGF-β, interleukin-6 (IL-6), IL-8, MMP, vascular endothelial growth factor, COX2, Bcl2, and inducible nitric oxide synthase. These factors are often associated with cancerous processes and may even promote them. Reactive oxygen species (ROS), generated by cellular alterations, stimulate the production of inflammatory factors such as NFκB, signal transducer and activator transcription, activator protein-1, and HIF-α. NFκB inhibits glycogen synthase kinase-3β (GSK-3β) and therefore activates the canonical WNT pathway. ROS activates the phosphatidylinositol 3 kinase/protein kinase B (PI3K/Akt) signaling in many cancers. PI3K/Akt also inhibits GSK-3β. Many gene mutations of the canonical WNT/β-catenin pathway giving rise to cancers have been reported (CTNNB1, AXIN, APC). Conversely, a significant reduction in the expression of PPARγ has been observed in many cancers. Moreover, PPARγ agonists promote cell cycle arrest, cell differentiation, and apoptosis and reduce inflammation, angiogenesis, oxidative stress, cell proliferation, invasion, and cell migration. All these complex and opposing interactions between the canonical WNT/β-catenin pathway and PPARγ appear to be fairly common in inflammation, oxidative stress, and cancers.

Digital twin (DT) technology is revolutionizing clinical practice by integrating diverse epidemiological data sources to create dynamic, patient-specific simulations. By leveraging data from genomics, proteomics, imaging, sociodemographics, and real-world behaviors, DTs provide a computational framework to model disease progression, optimize treatments, and personalize health care interventions. Through artificial intelligence (AI) and mathematical modeling, DTs facilitate predictive analytics for disease risk assessment, early diagnosis, and treatment response forecasting. This viewpoint explores the mathematical foundations of DTs, including differential equations for health trajectory modeling, Bayesian networks for multiomics integration, Markov models for disease progression, and reinforcement learning for treatment optimization. In addition, machine learning techniques such as recurrent neural networks and transformers enhance the predictive power of DTs by analyzing time-series clinical data and predicting future health events. The potential applications of DTs extend beyond individual patient care to public health surveillance, hospital resource management, and epidemiological modeling. However, several challenges persist, including data privacy concerns, computational infrastructure requirements, validation of predictive models, and regulatory compliance. Addressing these limitations requires interdisciplinary collaboration among health care providers, data scientists, and policy makers. With advancements in AI, wearable technology, and multiomics data integration, DTs are poised to reshape precision medicine. Future research should focus on refining computational efficiency, standardizing data interoperability, and ensuring ethical AI-driven decision-making. The continued evolution of DTs offers a transformative approach to proactive and personalized health care, reducing disease burden and enhancing patient outcomes.

Arterial stiffness, measured by arterial stiffness index (ASI), could be considered a main denominator in target organ damage among hypertensive subjects. Currently, no reported ASI normal references have been reported. The index of arterial stiffness is evaluated by calculation of a stiffness index. Predicted ASI can be estimated regardless to age, sex, mean blood pressure, and heart rate, to compose an individual stiffness index [(measured ASI–predicted ASI)/predicted ASI]. A stiffness index greater than zero defines arterial stiffness. Thus, the purpose of this study was 1) to determine determinants of stiffness index 2) to perform threshold values to discriminate stiffness index and then 3) to determine hierarchical associations of the determinants by performing a decision tree model among hypertensive participants without CV diseases. A study was conducted from 53,363 healthy participants in the UK Biobank survey to determine predicted ASI. Stiffness index was applied on 49,452 hypertensives without CV diseases to discriminate determinants of positive stiffness index (N = 22,453) from negative index (N = 26,999). The input variables for the models were clinical and biological parameters. The independent classifiers were ranked from the most sensitives: HDL cholesterol≤1.425 mmol/L, smoking pack years≥9.2pack-years, Phosphate≥1.172 mmol/L, to the most specifics: Cystatin c≤0.901 mg/L, Triglycerides≥1.487 mmol/L, Urate≥291.9 μ mol/L, ALT≥22.13 U/L, AST≤32.5 U/L, Albumin≤45.92 g/L, Testosterone≥5.181 nmol/L. A decision tree model was performed to determine rules to highlight the different hierarchization and interactions between these classifiers with a higher performance than multiple logistic regression (p<0.001). The stiffness index could be an integrator of CV risk factors and participate in future CV risk management evaluations for preventive strategies. Decision trees can provide accurate and useful classification for clinicians.

The use of exoskeletons in nursing practice has gained attention as a potential solution to address the physical demands and risks associated with the profession. This narrative review examines the effectiveness, usability, and impact of exoskeleton technology on nurses’ quality of work life. The review focuses on the reduction of physical strain and fatigue, improved posture and body mechanics, enhanced patient care, usability and acceptance factors, and the broader impact on work life. The effectiveness of exoskeletons in reducing physical strain and fatigue among nurses is supported by evidence showing decreased muscle activation and reduced forces exerted on the body. The usability and acceptance of exoskeletons are critical considerations, including device comfort and fit, ease of use and integration into workflows, user experience and training, compatibility with the work environment, and user feedback for iterative design improvements. The implementation of exoskeletons has the potential to positively impact nurses’ work life by reducing work-related injuries, improving physical well-being, enhancing job satisfaction, and promoting psychological and psychosocial benefits. Additionally, the use of exoskeletons can lead to improved patient care outcomes. Challenges and future directions in the field of exoskeleton technology for nurses include cost and accessibility, adaptability to nursing specialties and tasks, long-term durability and maintenance, integration with personal protective equipment, and ethical considerations. Addressing these challenges and considering future research and development efforts are crucial for the successful integration of exoskeleton technology in nursing practice, ultimately improving nurses’ quality of work life and patient care delivery.

Blended learning, which combines face-to-face learning and e-learning, has grown rapidly to be commonly used in education. Nevertheless, the effectiveness of this learning approach has not been completely quantitatively synthesized and evaluated using knowledge outcomes in health education. The aim of this study was to assess the effectiveness of blended learning compared to that of traditional learning in health education. We performed a systematic review of blended learning in health education in MEDLINE from January 1990 to July 2019. We independently selected studies, extracted data, assessed risk of bias, and compared overall blended learning versus traditional learning, offline blended learning versus traditional learning, online blended learning versus traditional learning, digital blended learning versus traditional learning, computer-aided instruction blended learning versus traditional learning, and virtual patient blended learning versus traditional learning. All pooled analyses were based on random-effect models, and the I statistic was used to quantify heterogeneity across studies. A total of 56 studies (N=9943 participants) assessing several types of learning support in blended learning met our inclusion criteria; 3 studies investigated offline support, 7 studies investigated digital support, 34 studies investigated online support, 8 studies investigated computer-assisted instruction support, and 5 studies used virtual patient support for blended learning. The pooled analysis comparing all blended learning to traditional learning showed significantly better knowledge outcomes for blended learning (standardized mean difference 1.07, 95% CI 0.85 to 1.28, I =94.3%). Similar results were observed for online (standardized mean difference 0.73, 95% CI 0.60 to 0.86, I =94.9%), computer-assisted instruction (standardized mean difference 1.13, 95% CI 0.47 to 1.79, I =78.0%), and virtual patient (standardized mean difference 0.62, 95% CI 0.18 to 1.06, I =78.4%) learning support, but results for offline learning support (standardized mean difference 0.08, 95% CI -0.63 to 0.79, I =87.9%) and digital learning support (standardized mean difference 0.04, 95% CI -0.45 to 0.52, I =93.4%) were not significant. From this review, blended learning demonstrated consistently better effects on knowledge outcomes when compared with traditional learning in health education. Further studies are needed to confirm these results and to explore the utility of different design variants of blended learning.

The associations between blood pressure and cannabis use remain inconsistent. The purpose of our study was to examine gender stratified associations of cannabis use and blood pressure [systolic, diastolic blood pressure (BP), pulse pressure (PP)] levels among the general UK Biobank population based study. Among 91,161 volunteers of the UK Biobank population, cannabis use status was assessed by questionnaire and range as heavy, moderate, low and never users. Associations between cannabis use and BP were estimated using multiple gender linear regressions. In adjusted covariates models, lifetime heavy cannabis use was associated with decrease in both SBP, DBP and PP in both genders, but with a higher effect among women (for SBP in men, b = − 1.09 (0.27), p < 0.001; in women, b = − 1.85 (0.36), p < 0.001; for DBP in men, b = − 0.50 (0.15), p < 0.001; in women, b = − 0.87 (0.17), p < 0.001; and for PP in men, b = − 0.60 (0.20), p < 0.001; in women, b = − 0.97 (0.27), p < 0.001. Among men, lower SBP and DBP levels were observed with participants without dyslipidemia and lower PP in participants with high income levels. Among women, lower SBP, DBP and PP were observed with current smokers, moderate/low alcohol levels and participants without dyslipidemia. Current cannabis use was associated with lower SBP levels in men (b = − 0.63 (0.25), p = 0.012) and in women (b = − 1.17 (0.31), p < 0.001). Same results were observed for DBP and PP. Negative association between BP in men was found but not in women. The small association in BP differences between heavy users and never users remains too small to adopt cannabis-blood pressure public policy in clinical practice.

Endometriosis is one of the main common gynecological disorders, which is characterized by the presence of glands and stroma outside the uterine cavity. Some findings have highlighted the main role of inflammation in endometriosis by acting on proliferation, apoptosis and angiogenesis. Oxidative stress, an imbalance between reactive oxygen species and antioxidants, could have a key role in the initiation and progression of endometriosis by resulting in inflammatory responses in the peritoneal cavity. Nevertheless, the mechanisms underlying this disease are still unclear and therapies are not currently efficient. Curcumin is a major anti-inflammatory agent. Several findings have highlighted the anti-oxidant, anti-inflammatory and anti-angiogenic properties of curcumin. The purpose of this review is to summarize the potential action of curcumin in endometriosis by acting on inflammation, oxidative stress, invasion and adhesion, apoptosis and angiogenesis.