Progressive regression of left ventricular hypertrophy two years after bariatric surgery.

BACKGROUND: Obesity is a systemic disorder associated with an increase in left ventricular mass and premature death and disability from cardiovascular disease. Although bariatric surgery reverses many of the hormonal and hemodynamic derangements, the long-term collective effects on body composition and left ventricular mass have not been considered before. We hypothesized that the decrease in fat mass and lean mass after weight loss surgery is associated with a decrease in left ventricular mass. METHODS: Fifteen severely obese women (mean body mass index [BMI]: 46.7+/-1.7 kg/m(2)) with medically controlled hypertension underwent bariatric surgery. Left ventricular mass and plasma markers of systemic metabolism, together with body mass index (BMI), waist and hip circumferences, body composition (fat mass and lean mass), and resting energy expenditure were measured at 0, 3, 9, 12, and 24 months. RESULTS: Left ventricular mass continued to decrease linearly over the entire period of observation, while rates of weight loss, loss of lean mass, loss of fat mass, and resting energy expenditure all plateaued at 9 [corrected] months (P \u3c.001 for all). Parameters of systemic metabolism normalized by 9 months, and showed no further change at 24 months after surgery. CONCLUSIONS: Even though parameters of obesity, including BMI and body composition, plateau, the benefits of bariatric surgery on systemic metabolism and left ventricular mass are sustained. We propose that the progressive decrease of left ventricular mass after weight loss surgery is regulated by neurohumoral factors, and may contribute to improved long-term survival

Troublesome Heterotopic Ossification after Central Nervous System Damage: A Survey of 570 Surgeries

BACKGROUND: Heterotopic ossification (HO) is a frequent complication after central nervous system (CNS) damage but has seldom been studied. We aimed to investigate features of HO for the first time in a large sample and the rate of early recurrence of HO in terms of the time of surgery. METHODOLOGY/PRINCIPAL FINDINGS: We retrospectively analyzed data from an anonymous prospective survey of patients undergoing surgery between May 1993 and November 2009 in our institution for troublesome HO related to acquired neurological disease. Demographic and HO characteristics and neurological etiologies were recorded. For 357 consecutive patients, we collected data on 539 first surgeries for HO (129 surgeries for multiple sites). During the follow-up, recurrences requiring another surgery appeared in 31 cases (5.8% [31/539]; 95% confidence interval [CI]: 3.8%-7.8%; 27 patients). Most HO requiring surgery occurred after traumatic brain injury (199 patients [55.7%]), then spinal cord injury (86 [24.0%]), stroke (42 [11.8%]) and cerebral anoxia (30 [8.6%]). The hip was the primary site of HO (328 [60.9%]), then the elbow (115 [21.3%]), knee (77 [14.3%]) and shoulder (19 [3.5%]). For all patients, 181 of the surgeries were performed within the first year after the CNS damage, without recurrence of HO. Recurrence was not associated with etiology (p = 0.46), sex (p = 1.00), age at CNS damage (p = 0.2), multisite localization (p = 0.34), or delay to surgery (p = 0.7). CONCLUSIONS/SIGNIFICANCE: In patients with CNS damage, troublesome HO and recurrence occurs most frequently after traumatic brain injury and appears frequently in the hip and elbow. Early surgery for HO is not a factor of recurrence

Genomic and Clinical Effects Associated with a Relaxation Response Mind-Body Intervention in Patients with Irritable Bowel Syndrome and Inflammatory Bowel Disease

Introduction: Irritable Bowel Syndrome (IBS) and Inflammatory Bowel Disease (IBD) can profoundly affect quality of life and are influenced by stress and resiliency. The impact of mind-body interventions (MBIs) on IBS and IBD patients has not previously been examined. Methods: Nineteen IBS and 29 IBD patients were enrolled in a 9-week relaxation response based mind-body group intervention (RR-MBI), focusing on elicitation of the RR and cognitive skill building. Symptom questionnaires and inflammatory markers were assessed pre- and post-intervention, and at short-term follow-up. Peripheral blood transcriptome analysis was performed to identify genomic correlates of the RR-MBI. Results: Pain Catastrophizing Scale scores improved significantly post-intervention for IBD and at short-term follow-up for IBS and IBD. Trait Anxiety scores, IBS Quality of Life, IBS Symptom Severity Index, and IBD Questionnaire scores improved significantly post-intervention and at short-term follow-up for IBS and IBD, respectively. RR-MBI altered expression of more genes in IBD (1059 genes) than in IBS (119 genes). In IBD, reduced expression of RR-MBI response genes was most significantly linked to inflammatory response, cell growth, proliferation, and oxidative stress-related pathways. In IBS, cell cycle regulation and DNA damage related gene sets were significantly upregulated after RR-MBI. Interactive network analysis of RR-affected pathways identified TNF, AKT and NF-κB as top focus molecules in IBS, while in IBD kinases (e.g. MAPK, P38 MAPK), inflammation (e.g. VEGF-C, NF-κB) and cell cycle and proliferation (e.g. UBC, APP) related genes emerged as top focus molecules. Conclusions: In this uncontrolled pilot study, participation in an RR-MBI was associated with improvements in disease-specific measures, trait anxiety, and pain catastrophizing in IBS and IBD patients. Moreover, observed gene expression changes suggest that NF-κB is a target focus molecule in both IBS and IBD—and that its regulation may contribute to counteracting the harmful effects of stress in both diseases. Larger, controlled studies are needed to confirm this preliminary finding. Trial Registration ClinicalTrials.Gov NCT0213674

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Antibiotikaeinsatz in Schweizer Ferkel­erzeugungs- und Mastbetrieben

In 164 randomly selected Swiss piglet production farms and 101 fattening farms, the indication for antibiotic use in 2012/2013 was recorded and an animal treatment index (TBI) was calculated for each age group. Sows were treated on average 0.9 days per year mainly due to mastitis-metritis-agalactia (MMA). Suckling piglets were treated on average 0.5 days per production cycle, mainly due to diarrhea and polyarthritis. Weaned piglets were treated during 4.4 days, especially due to diarrhea, polyarthritis and wasting. In fattening pigs, treatments were mainly due to diarrhea and HPS-suspicion, and lasted on average 4.8 days. In sows, antibiotics were used prophylactically on 22.6% of the treatment days, in suckling piglets on 50.5%, in weaners on 86.1% and in fattening pigs on 79.0% of the treatment days. A prophylactic oral antibiotic group therapy did not have a significant positive effect on daily weight gain of fattening pigs, nor was it able to reduce the number of individual or group therapies. In farms with prophylactic oral group therapy, the mortality rate during the first two fattening weeks even tended to be higher (p=0.06) than in farms without oral group therapy. Highest priority critically important antibiotics were used in 22.6% of all treatment days in sows, in 37.5% in suckling piglets, in 17.2% in weaned piglets and in 27.3% in fattening pigs. In many farms, antibiotics were not prescribed and used according to the rules of "prudent use"

SDALF: Modeling Human Appearance with Symmetry-Driven Accumulation of Local Features

In video surveillance, person re-identification (re-id) is probably the open challenge, when dealing with a camera network with non-overlapped fields of view. Re-id allows the association of different instances of the same person across different locations and time. A large number of approaches have emerged in the last 5 years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. In this chapter, we follow this line, presenting a strategy with three important keycharacteristics that differentiate it with respect to the state of the art: (1) a symmetrydriven method to automatically segment salient body parts, (2) an accumulation of features making the descriptormore robust to appearance variations, and (3) a person re-identification procedure casted as an image retrieval problem, which can be easily embedded into a multi-person tracking scenario, as the observation model