Adipose-Derived Stem Cells for Facial Rejuvenation

The interest in regenerative medicine is increasing, and it is a dynamically developing branch of aesthetic surgery. Biocompatible and autologous-derived products such as platelet-rich plasma or adult mesenchymal stem cells are often used for aesthetic purposes. Their application originates from wound healing and orthopaedics. Adipose-derived stem cells are a powerful agent in skin rejuvenation. They secrete growth factors and anti-inflammatory cytokines, stimulate tissue regeneration by promoting the secretion of extracellular proteins and secrete antioxidants that neutralize free radicals. In an office procedure, without cell incubation and counting, the obtained product is stromal vascular fraction, which consists of not only stem cells but also other numerous active cells such as pericytes, preadipocytes, immune cells, and extra-cellular matrix. Adipose-derived stem cells, when injected into dermis, improved skin density and overall skin appearance, and increased skin hydration and number of capillary vessels. The main limitation of mesenchymal stem cell transfers is the survival of the graft. The final outcomes are dependent on many factors, including the age of the patient, technique of fat tissue harvesting, technique of lipoaspirate preparation, and technique of fat graft injection. It is very difficult to compare available studies because of the differences and multitude of techniques used. Fat harvesting is associated with potentially life-threatening complications, such as massive bleeding, embolism, or clots. However, most of the side effects are mild and transient: primarily hematomas, oedema, and mild pain. Mesenchymal stem cells that do not proliferate when injected into dermis promote neoangiogenesis, that is why respectful caution should be taken in the case of oncologic patients. A longer clinical observation on a higher number of participants should be performed to develop reliable indications and guidelines for transferring ADSCs.

Predicting Venous Thrombosis in Osteoarthritis Using a Machine Learning Algorithm: A Population-Based Cohort Study

Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish a prediction model, we used six ML algorithms, of which 35 variables were employed. Recursive feature elimination (RFE) was used to screen the most related clinical variables associated with VTE. SHapley additive exPlanations (SHAP) were applied to interpret the ML mode and determine the importance of the selected features. Overall, 3169 patients with OA (average age: 66.52 ± 7.28 years) were recruited from Xi’an Honghui Hospital. Of these, 352 and 2817 patients were diagnosed with and without VTE, respectively. The XGBoost algorithm showed the best performance. According to the RFE algorithms, 15 variables were retained for further modeling with the XGBoost algorithm. The top three predictors were Kellgren–Lawrence grade, age, and hypertension. Our study showed that the XGBoost model with 15 variables has a high potential to predict VTE risk in patients with OA.

Personalized Lifestyle Intervention and Functional Evaluation Health Outcomes SurvEy: Presentation of the LIFEHOUSE Study Using N-of-One Tent–Umbrella–Bucket Design

The working definition of health is often the simple absence of diagnosed disease. This common standard is limiting given that changes in functional health status represent early warning signs of impending health declines. Longitudinal assessment of functional health status may foster prevention of disease occurrence and modify disease progression. The LIFEHOUSE (Lifestyle Intervention and Functional Evaluation-Health Outcomes SurvEy) longitudinal research project explores the impact of personalized lifestyle medicine approaches on functional health determinants. Utilizing an adaptive tent–umbrella–bucket design, the LIFEHOUSE study follows the functional health outcomes of adult participants recruited from a self-insured employee population. Participants were each allocated to the tent of an all-inclusive N-of-one case series. After assessing medical history, nutritional physical exam, baseline functional status (utilizing validated tools to measure metabolic, physical, cognitive, emotional and behavioral functional capacity), serum biomarkers, and genomic and microbiome markers, participants were assigned to applicable umbrellas and buckets. Personalized health programs were developed and implemented using systems biology formalism and functional medicine clinical approaches. The comprehensive database (currently 369 analyzable participants) will yield novel interdisciplinary big-health data and facilitate topological analyses focusing on the interactome among each participant’s genomics, microbiome, diet, lifestyle and environment.

Prevention of Post-Transplant Diabetes Mellitus: Towards a Personalized Approach

Post-transplant diabetes is a frequent complication after transplantation. Moreover, patients suffering from post-transplant diabetes have increased cardiovascular morbidity and reduced survival. Pathogenesis mainly involves beta-cell dysfunction in presence of insulin resistance. Both pre- and post-transplant risk factors are well-described, and some of them may be corrected or prevented. However, the frequency of post-transplant diabetes has not decreased in recent years. We realized a critical appraisal of preventive measures to reduce post-transplant diabetes.

Machine Learning-Based Approaches for Prediction of Patients’ Functional Outcome and Mortality after Spontaneous Intracerebral Hemorrhage

Spontaneous intracerebral hemorrhage (SICH) has been common in China with high morbidity and mortality rates. This study aims to develop a machine learning (ML)-based predictive model for the 90-day evaluation after SICH. We retrospectively reviewed 751 patients with SICH diagnosis and analyzed clinical, radiographic, and laboratory data. A modified Rankin scale (mRS) of 0–2 was defined as a favorable functional outcome, while an mRS of 3–6 was defined as an unfavorable functional outcome. We evaluated 90-day functional outcome and mortality to develop six ML-based predictive models and compared their efficacy with a traditional risk stratification scale, the intracerebral hemorrhage (ICH) score. The predictive performance was evaluated by the areas under the receiver operating characteristic curves (AUC). A total of 553 patients (73.6%) reached the functional outcome at the 3rd month, with the 90-day mortality rate of 10.2%. Logistic regression (LR) and logistic regression CV (LRCV) showed the best predictive performance for functional outcome (AUC = 0.890 and 0.887, respectively), and category boosting presented the best predictive performance for the mortality (AUC = 0.841). Therefore, ML might be of potential assistance in the prediction of the prognosis of SICH.

Predictors of the Intention to Be Vaccinated against COVID-19 in a Sample of Italian Respondents at the Start of the Immunization Campaign

COVID-19 vaccines are the most promising means of limiting the pandemic. The present study aims at determining the roles of several psychological variables in predicting vaccination intention in Italy. An online questionnaire was disseminated between 9 March and 9 May 2021. The sample included 971 participants. Results showed that most of the participants were willing to vaccinate. Acceptance rates were correlated with age, marital status, and area of residence. Intention to be vaccinated was positively correlated with perceived risk, pro-sociality, fear of COVID-19, use of preventive behaviors, and trust in government, in science, and in medical professionals. Intention to be vaccinated was negatively associated with belief in misinformation. The degree of acceptance is likely to be a result of the campaign tailored to address people’s negative attitudes towards vaccines. Trust in government and trust in science were among the strongest psychological predictors of vaccination intention. Fear of COVID-19, but not perceived risk, was associated with increased vaccine uptake, suggesting that the affective component of risk perception was more important than the cognitive component in predicting participants’ behaviors. Belief in misinformation was associated with reduced vaccination intention. Future studies will take into consideration these variables, to better understand the multifaceted process underlying vaccination intention.

Associations between Sjögren Syndrome, Sociodemographic Factors, Comorbid Conditions, and Scleritis in a Taiwanese Population-Based Study

This nationwide, population-based, retrospective, matched case–control study included 111,960 newly diagnosed patients with scleritis who were identified by the International Classification of Diseases, Ninth Revision, Clinical Modification code 379.0, selected from the Taiwan National Health Insurance Research Database. Demographic characteristics, Sjögren syndrome, and comorbid conditions within 1 year before the scleritis diagnosis were examined using univariate logistic regression analyses, and a paired t-test was used for continuous variables. Adjusted logistic regression was used to compare the prognosis odds ratio (OR) of the patients with scleritis with the controls. After adjustment for confounders, patients with Sjögren syndrome were remarkably more likely to have scleritis than the controls (OR = 33.53, 95% confidence interval (CI) = 27.43–40.97, p < 0.001). Other conditions found to have increased odds of scleritis included post ocular pterygium, glaucoma, and scleral surgery (OR = 4.01, 95% CI = 3.64–4.43; OR = 3.16, 95% CI = 2.24–4.47; OR = 6.83, 95% CI = 5.34–8.74, respectively); systemic infections, such as syphilis, tuberculosis, and a human herpes viral infection (OR = 4.01, 95% CI = 2.93–5.50; OR = 2.24, 95% CI = 1.94–2.58; OR = 8.54, 95% CI = 8.07–9.03, respectively); and systemic diseases, such as rheumatoid arthritis, granulomatous vasculitis, systemic lupus erythematosus, ankylosing spondylitis, and gout (OR = 2.93, 95% CI = 2.66–3.23; OR = 7.37, 95% CI = 3.91–13.88; OR = 3.18, 95% CI = 2.63–3.85; OR = 5.57, 95% CI = 4.99–6.22; OR = 2.84, 95% CI = 2.72–2.96, respectively). The results strongly support an association between Sjögren syndrome, post ocular surgery, systemic infection disease, systemic autoimmune disease, and scleritis.

Demand for Pharmacogenomics and Personalized Medicine in the United Arab Emirates

The application of personalized medicine (PM) is rapidly evolving [...]

Efficacy and Safety of Enoxaparin versus New Oral Anticoagulants to Prevent Venous Thromboembolism after Total Hip Replacement: A Systematic Review and Meta-Analysis

Prophylactic anticoagulant therapy is recommended for reducing the risk of venous thromboembolism (VTE) after a total hip replacement (THR). However, it is not clear which anticoagulant is preferable. Hence, a systematic review and meta-analysis of randomized double-blind controlled trials (RDBCTs) were conducted to investigate the clinical efficacy and safety of enoxaparin in comparison with newer oral anticoagulants for the prevention of VTE after THR. The Cochrane Library, Scopus, Web of Science, Embase, and PubMed/Medline databases were used for PICO search strategy. Relative risks (RR) of symptomatic VTE, clinically relevant bleeding, mortality, and a net clinical endpoint were estimated employing a random effect meta-analysis. ITC and RevMan software were used for indirect and direct comparisons, respectively. Nine RDBCTs comprising 24,584 patients were included. As compared to enoxaparin, a reduced risk for symptomatic VTE was observed with rivaroxaban (confidence interval [CI]: 0.32–0.77; RR: 0.46%) and comparable with apixaban (0.12–1.26; 0.42%) and dabigatran (0.22–2.20; 0.70%). Contrarily to enoxaparin, a greater risk for clinically relevant bleeding was observed with rivaroxaban (1.03–1.48; 1.23%), comparable with dabigatran (0.96–1.33; 1.10%) and reduced with apixaban (0.19–5.66; 0.96%). In indirect or direct comparisons, the interventions did not differ on the net clinical endpoint. In conclusion, the findings of this meta-analysis revealed no significant difference in the efficacy and safety of new oral anticoagulants as compared to enoxaparin for the prevention of VTE after total hip replacement surgery.

Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses

Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants.