Citations

Background: The primary aim of this study was to demonstrate the benefits of topical human platelet exosome extract (HPE) (plated) hair serum for improving hair and scalp health in participants with self-perceived thinning hair. Methods: This single-center, placebo-controlled, double-blind study enrolled 39 healthy men and women 18–65 years of age with self-perceived thinning hair. Participants applied either HPE for 9 months or a topical placebo for 6 months with the option to cross over to HPE at 6 months for an additional 3 months. End points included improvements in the global aesthetic appearance of hair, as assessed by a blinded live evaluator; participant-reported outcomes; hair trichoscopy; and independent physician photograph evaluations. Safety data included monitoring for adverse events. Results: Blinded live evaluator assessments demonstrated statistically significant improvements in the global aesthetic appearance of hair for volume/fullness, density, scalp coverage, and appearance, with all P values less than 0.0001 within the HPE group and statistical improvement over the control at months 3 and 6 for volume/fullness, density, and scalp coverage. Participant-reported outcomes for within-group changes for HPE were statistically significant for all hair health parameters, including density, volume fullness, scalp coverage, scalp health, thickness, amount, quality, color, dryness, strength, and overall health of the hair. Hair trichoscopy analyses were significantly improved from baseline within the HPE group. Independent physician photography review demonstrated greater improvements within the HPE group. No adverse effects were observed. Conclusions: This study demonstrates that topical HPE (plated) hair serum with Renewosome technology significantly improves hair and scalp health and is safe and well tolerated.

Abstract The aging process is universal, and it is characterized by a progressive deterioration and decrease in physiological function leading to decline on the organismal level. Nevertheless, a number of genetic and nongenetic interventions have been described, which successfully extend healthspan and lifespan in different species. Furthermore, a number of clinical trials have been evaluating the feasibility of different interventions to promote human health. The goal of the annual Biological Sciences Section of the Gerontological Society of America meeting was to share current knowledge of different topics in aging research and provide a vision of the future of aging research. The meeting gathered international experts in diverse areas of aging research including basic biology, demography, and clinical and translational studies. Specific topics included metabolism, inflammaging, epigenetic clocks, frailty, senescence, neuroscience, stem cells, reproductive aging, inter-organelle crosstalk, comparative transcriptomics of longevity, circadian clock, metabolomics, and biodemography.

Abstract We present a comprehensive global analysis of genetic variants associated with autosomal-dominant Alzheimer’s disease (ADAD). A total of 550 variants in the APP, PSEN1 and PSEN2 genes were identified, of which 279 were classified as pathogenic or likely pathogenic based on American College of Medical Genetics and Genomics and the Association for Molecular Pathology criteria, utilizing data from the Dominantly Inherited Alzheimer Network (DIAN), literature and public databases. Symptomatic age at onset (AAO) data were estimated for 227 of these variants, allowing detailed characterization of their frequency, pathogenicity and AAO. Importantly, 226 variants met eligibility criteria for inclusion in disease-modifying clinical trials. Furthermore, we demonstrated the predictive value of mean variant AAO and parental AAO in predicting symptomatic AAO, validated against converters who became symptomatic during follow-up in the DIAN Observational Study. This dataset provides critical insights into the global landscape of ADAD and reveals the genetic and AAO heterogeneity of ADAD variants while refining variant trial eligibility criteria.

AbstractBackgroundPopulation dementia prevalence is traditionally estimated using cohort studies, surveys, routinely‐collected administrative data, and registries. Hospital Electronic Health Records (EHRs) are comprised of rich structured and unstructured (text) clinical data that are underutilised for this purpose.We aimed to develop a suite of algorithms using routinely‐collected EHR data to reliably identify cases of dementia, as a key step towards incorporating such data in prevalence estimation. Towards this, we developed a novel predictive framework integrating data‐science and biostatistical methods.MethodTraining data were sourced via the National Centre for Healthy Ageing (NCHA) Data Platform, a linked, curated, EHR‐derived data warehouse. Individuals within the platform catchment aged >60 years with confirmed dementia were identified through hospital specialist dementia clinics. A comparison group of individuals aged >60years with EHR records without dementia was recruited from the community.A panel of clinical experts (Neurology, Geriatric Medicine) informed variable and concept selection and guided data cleaning efforts within both streams. Algorithms were developed via two work‐streams; a traditional biostatistical approach to fit logistic regression models using structured data elements, and a data science stream used Natural Language Processing (NLP) to fit models to the unstructured (text) parts of the EHR, for the same individuals.ResultOf 568 individuals (362 with dementia), 434 had clinical notes available. In the data science stream using unstructured data, among a range of NLP derived models, the Random Forest classifier performed best in assigning dementia status, with Area Under the Curve (AUC) 0.95, specificity 90.2% and sensitivity 88.4%. In the biostatistics stream, 15 structured variables were included in the final model, covering demographics, health service attendance, medications, and ICD‐10 Codes, with AUC 0.94, specificity 85.9% and sensitivity 85.6%.ConclusionArtificial intelligence techniques applied to unstructured electronic health data and guided by human clinical expertise may be powerful tools in capturing the presence of dementia, at least comparable to traditional techniques using structured data, and conferring practical and scientific advantages for dementia prevalence estimation. Future validation is required in less crisply delineated real‐world settings.

Abstract Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that profoundly impacts the lives of people with the condition and their family caregivers. Little is known about the experiences of care at home among individuals with ALS and their family caregivers. This qualitative study explored these experiences in 14 in-depth interviews that included seven persons with ALS and 13 family caregivers. The findings were organized based on the Salutogenic Model of Health. The main sources of stress reported included the overwhelming nature of getting an ALS diagnosis, inadequacy of health care teams in providing emotional support and promoting shared decision-making in regard to the management of ALS, high level of illness acuity, the intense nature of caregiving involved in ALS, high cost of treatments and equipment, difficulties navigating the healthcare system, unavailability of suitable medical equipment, scarcity of paid caregivers competent in managing ALS, and lack of adequate training for family caregivers. Perspectives on positive coping included mental toughness, maintaining a positive outlook about life and the future, being proactive in seeking support to maintain function and quality of life, proactively communicating with care teams, advanced care planning, having a sense of control over ALS management plan of care, and taking a break from caregiving. Instrumental and emotional support from family, friends, community organizations, and healthcare teams, as well as sufficient insurance and financial coverage for ALS treatments, facilitated better coping. Future research and policies should focus on identifying effective multisectoral strategies for supporting people with ALS and their caregivers.

Background and Objectives: Fibromyalgia has many unmet needs relating to treatment, and the delivery of effective and evidence-based healthcare is lacking. We analyzed social media conversations to understand the patients’ perspectives on the lived experience of fibromyalgia, factors reported to trigger flares of pain, and the treatments being discussed, identifying barriers and opportunities to improve healthcare delivery. Methods: A non-interventional retrospective analysis accessed detail-rich conversations about fibromyalgia patients’ experiences with 714,000 documents, including a fibromyalgia language tag, which were curated between May 2019 and April 2021. Data were analyzed via qualitative and quantitative analyses. Results: Fibromyalgia conversations were found the most on Twitter and Reddit, and conversation trends remained stable over time. There were numerous environmental and modifiable triggers, ranging from the most frequent trigger of stress and anxiety to various foods. Arthritis and irritable bowel syndrome (IBS) were the most frequently associated comorbidities. Patients with fibromyalgia reported a wide range of symptoms, with pain being a cardinal feature. The massage, meditation and acupuncture domains were the most reported treatment modalities. Opportunities to improve healthcare delivered by medical providers were identified with current frustration relating to a lack of acknowledgement of their disease, minimization of symptoms and inadequately meeting their care needs. Conclusions: We developed a comprehensive, large-scale study which emphasizes advanced natural language processing algorithm application in real-world research design. Through the extensive encapsulation of patient perspectives, we outlined the habitual symptoms, triggers and treatment modalities which provide a durable foundation for addressing gaps in healthcare provision.

The Quantitative Imaging Biomarkers Alliance Profile for dynamic susceptibility contrast (DSC) MRI and its longitudinal claims for the precision of a DSC MRI–derived quantitative imaging biomarker to assess the relative cerebral blood volume changes in gliomas are summarized.

Background: Pathologic reflux is a common debilitating digestive condition and anti-reflux surgery is an effective treatment option. Depending on the specifics of the procedure, anti-reflux surgery leads to distinct changes in the endoscopic appearance of the esophagogastric junction (EGJ). This American Foregut Society (AFS) white paper develops nomenclature for describing the endoscopic characteristics of the EGJ after commonly performed laparoscopic antireflux operations. Methods: The AFS Anti-Reflux Barrier Collaborative analyzed the anatomic manipulation and corresponding endoscopic appearance of the EGJ after commonly performed anti-reflux surgery including: (1) 360° anterior/posterior (AP) fundoplication (Nissen); (2) posterior partial fundoplication (Toupet); (3) anterior partial fundoplication (Watson, Dor); (4) magnetic sphincter augmentation; and (5) concomitant hiatal hernia repair with transoral fundoplication (cTIF). Results: Characteristics common to the anti-reflux surgery procedures include restoration of intra-abdominal esophageal length and crural diaphragm repair such that the hiatal aperture is no longer visible in the retroflexed endoscopic view. With regard to the geometry of anti-reflux procedures, the Collaborative established that when evaluated endoscopically in retroflection, the surgically constructed valve should be described in reference to the angle of His and the extent of anatomic change described in terms of (1) the depth of the anterior and posterior grooves, (2) the apposition of the valve collar to the endoscope, (3) the length of the valve, and (4) the position of the squamocolumnar junction relative to the lip of the valve. Conclusions: Current anti-reflux operations share much in common but employ varying degrees of valvular reconstruction leading to distinct endoscopic characteristics. Developing a consistent nomenclature for describing the appearance of the post-surgical valve is essential to develop a better understanding of how variables such as the tightness of the hiatal repair, tightness of the valve collar, and length of the valve can impact the outcome of anti-reflux surgery and predict reflux recurrence.