Explore the vast range of titles available.

Produced by senescent cells, the senescence-associated secretory phenotype (SASP) is a potential driver of age-related dysfunction. We tested whether circulating concentrations of SASP proteins reflect age and medical risk in humans. We first screened senescent endothelial cells, fibroblasts, preadipocytes, epithelial cells, and myoblasts to identify candidates for human profiling. We then tested associations between circulating SASP proteins and clinical data from individuals throughout the life span and older adults undergoing surgery for prevalent but distinct age-related diseases. A community-based sample of people aged 20-90 years (retrospective cross-sectional) was studied to test associations between circulating SASP factors and chronological age. A subset of this cohort aged 60-90 years and separate cohorts of older adults undergoing surgery for severe aortic stenosis (prospective longitudinal) or ovarian cancer (prospective case-control) were studied to assess relationships between circulating concentrations of SASP proteins and biological age (determined by the accumulation of age-related health deficits) and/or postsurgical outcomes. We showed that SASP proteins were positively associated with age, frailty, and adverse postsurgery outcomes. A panel of 7 SASP factors composed of growth differentiation factor 15 (GDF15), TNF receptor superfamily member 6 (FAS), osteopontin (OPN), TNF receptor 1 (TNFR1), ACTIVIN A, chemokine (C-C motif) ligand 3 (CCL3), and IL-15 predicted adverse events markedly better than a single SASP protein or age. Our findings suggest that the circulating SASP may serve as a clinically useful candidate biomarker of age-related health and a powerful tool for interventional human studies.

Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area.

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

Unnecessary ED visits and transfers to hand clinics raise treatment costs and patient burden at trauma centers. In the present COVID-19 pandemic, needless transfers can increase patients’ risk of viral exposure. Therefore, this review analyzes different aspects of the remote diagnosis and triage of traumatic hand injuries. The most common file was photography, with the most common devices being cell phone cameras. Treatment, triage, diagnosis, cost, and time outcomes were assessed, showing concordance between teleconsultation and face-to-face patient evaluations. We conclude that photography and video consultations are feasible surrogates for ED visits in patients with traumatic hand injuries. These technologies should be leveraged to decrease treatment costs and potentially decrease the time to definitive treatment after initial evaluation.

Introduction Perceived age is defined as how old a person looks to external evaluators. It reflects the underlying biological age, which is a measure based on physical and physiological parameters reflecting a person’s aging process more accurately than chronological age. People with a higher biological age have shorter lives compared to those with a lower biological age with the same chronological age. Our review aims to find whether increased perceived age is a risk factor for overall mortality risk or comorbidities. Methods A literature search of three databases was conducted following the PRISMA guidelines for studies analyzing perceived age or isolated facial characteristics of old age and their relationship to mortality risk or comorbidity outcomes. Data on the number of patients, type and characteristics of evaluation methods, evaluator characteristics, mean chronologic age, facial characteristics studied, measured outcomes, and study results were collected. Results Out of 977 studies, 15 fulfilled the inclusion criteria. These studies found an increase in mortality risk of 6–51% in older-looking people compared to controls (HR 1.06–1.51, p < 0.05). In addition, perceived age and some facial characteristics of old age were also associated with cardiovascular risk and myocardial infarction, cognitive function, bone mineral density, and chronic obstructive pulmonary disease (COPD). Conclusion Perceived age promises to be a clinically useful predictor of overall mortality and cardiovascular, pulmonary, cognitive, and osseous comorbidities. Level of Evidence III 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 .

Background: Effective pain management is crucial for both comfort and outcomes, yet predicting and managing this pain is difficult. This study aimed to analyze postoperative pain in patients undergoing hand surgery at the Mayo Clinic Florida, examining how patient characteristics and anxiety affect pain outcomes. Methods: We conducted a single-arm clinical trial at Mayo Clinic Florida, recruiting patients undergoing hand surgery. Preoperative pain and anxiety were assessed using the Pain Catastrophizing Scale (PCS) and State-Trait Anxiety Inventory (STAI). Postoperatively, patients used an iPhone app to record pain levels and medication use every four hours. Devices were collected three days after surgery. We analyzed the relationship between demographics, PCS, STAI scores, and pain levels using linear and logistic regression models. All statistical tests were two-sided with significance set at p < 0.05, analyzed with R4.2.2. Results: Data were collected from 62 patients (62.9% women, 37.1% men) undergoing hand surgery. Participants were mainly White (90.3%), with 50% being in the middle-aged adult group. Most had low anxiety levels (80.6% STAI-S, 82.3% STAI-T) and low catastrophizing (61.3% PCS). Postoperative pain was low, with median scores between 1.0 and 2.0 over three days. Demographics, anxiety, and catastrophizing were not significant predictors of pain levels. Logistic regression showed time as a significant factor, with pain levels peaking on Day 3. Conclusions: Postoperative pain after hand surgery was generally low, with time being a significant predictor of increased pain. Demographic factors, anxiety, and catastrophizing did not significantly affect pain levels. Pain management should emphasize time-sensitive interventions and ongoing monitoring.

ObjectiveTo examine the hypothesis that speckle myocardial imaging (SMI) modalities, including longitudinal, radial and circumferential systolic (s) and diastolic (d) myocardial velocity imaging, displacement (D), strain rate (SR) and strain (S), as well as left ventricular (LV) rotation/torsion are sensitive for detecting early myocardial dysfunction in isolated LV non-compaction (iLVNC).Design and resultsTwenty patients with iLVNC diagnosed by cardiac magnetic resonance (15) or echocardiography (5) were included. Patients were divided into two groups: ejection fraction (EF)>50% (n=10) and EF≤50% (n=10). Standard measures of systolic and diastolic function including pulsed wave tissue Doppler Imaging (PWTDI) were obtained. Longitudinal, radial and circumferential SMI, and LV rotation/torsion were compared with values for 20 age/sex-matched controls. EF, PWTDI E', E/E' and all of the SMI modalities were significantly abnormal for patients with EF≤50% compared with controls. In contrast, EF and PWTDI E', E/E' were not significantly different between controls and patients with iLVNC (EF>50%). However, SMI-derived longitudinal sS, sSR, sD and radial sS, as well as LV rotation/torsion values, were all reduced in iLVNC (EF>50%) compared with controls. Measurements with the highest discriminating power between iLVNC (EF>50%) and controls were longitudinal sS mean of the six apical segments (area under the curve (AUC)=0.94), sS global average (AUC=0.94), LV rotation apical mean (AUC=0.94); LV torsion (AUC=0.93) LV torsion rate (AUC=0.94).ConclusionsLV SMI values are reduced in patients with iLVNC, even those with normal EF and PWTDI. The most accurate SMI modalities to discriminate between patients and controls are longitudinal sS mean of the six apical segments, LV apical rotation or LV torsion rate.

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

PurposeTo demonstrate the feasibility of directional percutaneous epicardial ablation using a partially insulated catheter.MethodsPartially insulated catheter prototypes were tested in 12 (6 canine, 6 porcine) animal studies in two centers. Prototypes had interspersed windows to enable visualization of epicardial structures with ultrasound. Epicardial unipolar ablation and ablation between two electrodes was performed according to protocol (5–60 W power, 0–60 mls/min irrigation, 78 s mean duration).ResultsOf 96 epicardial ablation attempts, unipolar ablation was delivered in 53.1%. Electrogram evidence of ablation, when analyzable, occurred in 75 of 79 (94.9%) therapies. Paired pre/post-ablation pacing threshold (N = 74) showed significant increase in pacing threshold post-ablation (0.9 to 2.6 mA, P < .0001). Arrhythmias occurred in 18 (18.8%) therapies (11 ventricular fibrillation, 7 ventricular tachycardia), mainly in pigs (72.2%). Coronary artery visualization was variably successful. No phrenic nerve injury was noted during or after ablation. Furthermore, there were minimal pericardial changes with ablation.ConclusionsEpicardial ablation using a partially insulated catheter to confer epicardial directionality and protect the phrenic nerve seems feasible. Iterations with ultrasound windows may enable real-time epicardial surface visualization thus identifying coronary arteries at ablation sites. Further improvements, however, are necessary.

The proliferation of new models like the Centre for Commercialization of Regenerative Medicine (CCRM) in Canada, the California Institute for Regenerative Medicine (CIRM), and the Advanced Regenerative Manufacturing Institute (ARMI) in New Hampshire, among others, has inspired the creation of the Florida Organization for Regenerative Medicine (FORM), a nonprofit organization with a mission to facilitate translational research, commercialization, education, and therapeutic validation in the area of regenerative medicine with the ultimate aim of improving patient outcomes, creating high‐quality jobs, and accelerating innovation through collaboration in the State of Florida and beyond. FORM is comprised of multiple strong and complementary clinical and research groups within Florida, including (from North to South) the Mayo Clinic's Center for Regenerative Medicine; the University of Florida's Center for Regenerative Medicine, which operates across the university and is based in the College of Medicine (CoM), and Institute for Cell and Tissue Science and Engineering (ICTSE) in the College of Engineering (CoE); Nova Southeastern University's Cell Therapy Institute (CTI); Florida Atlantic University's Center for Molecular Biology and Biotechnology (CMBB); and the University of Miami's Diabetes Research Institute (DRI), Cell Transplant Center (CTC), and Interdisciplinary Stem Cell Institute (ISCI). Crayton Pruitt Family Department of Biomedical Engineering & Department of Materials Science and Engineering, University of Florida, Gainesville, Florida, USAJoshua M. HareDepartment of Medicine & Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, Florida, USAGregg B. FieldsDepartment of Chemistry and Biochemistry, Florida Atlantic University, Boca Raton, Florida, USARichard JoveCell Therapy Institute, Nova Southeastern University, Fort Lauderdale, Florida, USANorma KenyonDiabetes Research Institute, University of Miami Miller School of Medicine, Miami, Florida, USAAisha KhanInterdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, Florida, USAKeith MarchDivision of Cardiovascular Medicine & Center for Regenerative Medicine, University of Florida College of Medicine, Gainesville, Florida, USASandro MatosevicDepartment of Industrial and Physical Pharmacy, Purdue University, West Lafayette, Indiana, USA & Akron Biotechnology, Boca Raton, Florida, USAAyesha MahmoodLifelink Foundation, Tampa, Florida, USACarl J. PepineDivision of Cardiovascular Medicine & Center for Regenerative Medicine, University of Florida College of Medicine, Gainesville, Florida, USACamillo RicordiDiabetes Research Institute, University of Miami Miller School of Medicine, Miami, Florida, USAShane A. ShapiroDepartment of Orthopedic Surgery & Center for Regenerative Medicine, Mayo Clinic, Jacksonville, Florida, USAClaudia ZylberbergAkron Biotechnology, Boca Raton, Florida, USAEzequiel ZylberbergIndustrial Performance Center, Akron Biotechnology, Boca Raton, Florida, USA