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Study design and methods: U.S. study to protect brain health through lifestyle intervention to reduce risk (U.S. POINTER).
Baker, LD, Snyder, HM, Espeland, MA, Whitmer, RA, Kivipelto, M, Woolard, N, Katula, J, Papp, KV, Ventrelle, J, Graef, S, et al
Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024;(2):769-782
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INTRODUCTION The U.S. study to protect brain health through lifestyle intervention to reduce risk (U.S. POINTER) is conducted to confirm and expand the results of the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) in Americans. METHODS U.S. POINTER was planned as a 2-year randomized controlled trial of two lifestyle interventions in 2000 older adults at risk for dementia due to well-established factors. The primary outcome is a global cognition composite that permits harmonization with FINGER. RESULTS U.S. POINTER is centrally coordinated and conducted at five clinical sites (ClinicalTrials.gov: NCT03688126). Outcomes assessments are completed at baseline and every 6 months. Both interventions focus on exercise, diet, cognitive/social stimulation, and cardiovascular health, but differ in intensity and accountability. The study partners with a worldwide network of similar trials for harmonization of methods and data sharing. DISCUSSION U.S. POINTER is testing a potentially sustainable intervention to support brain health and Alzheimer's prevention for Americans. Impact is strengthened by the targeted participant diversity and expanded scientific scope through ancillary studies.
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Comparison of Magnesium Pre-treatment With Two Different Doses of Rocuronium in Rapid Sequence Intubation: A Randomized Controlled Trial.
Sharma, M, Prakash, R, Chaurasia, MK, Prabha, R, Raman, R, Singh, GP, Arora, G
Cureus. 2024;(3):e56794
Abstract
Introduction Magnesium is recognized for its ability to reduce the onset time of rocuronium while simultaneously extending its duration of action. This study aims to assess the efficacy of magnesium pre-treatment in decreasing the onset time with two different doses of rocuronium in patients undergoing rapid sequence intubation. Materials and methods This randomized prospective double-blind clinical study involved 50 patients classified as American Society Of Anesthesiologists (ASA) I/II, with no preoperative indications of difficult intubation, undergoing elective surgery under general anesthesia. The patients were divided into two groups: group A received 60 mg/kg of magnesium 15 minutes before intubation with 1.2 mg/kg of rocuronium, and group B received 60 mg/kg of magnesium before 0.6 mg/kg of rocuronium. Intubating conditions were assessed and graded at loss of last twitch after administration in both groups, considering ease of intubation, vocal cord position, and response to the insertion of the tracheal tube. Simultaneously, hemodynamic variations were recorded just before intubation, at one minute and five minutes post-intubation. Results Intubating conditions with 0.6 mg/kg of rocuronium were comparable or equally good compared to 1.2 mg/kg of rocuronium with magnesium pre-treatment. Conclusions Magnesium pre-treatment enhances the neuromuscular blocking effect of rocuronium, reducing its onset time without clinically significant prolongation of the duration of the block.
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The AUstralian multidomain Approach to Reduce dementia Risk by prOtecting brain health With lifestyle intervention study (AU-ARROW): A study protocol for a single-blind, multi-site, randomized controlled trial.
Gardener, SL, Fuller, SJ, Naismith, SL, Baker, L, Kivipelto, M, Villemagne, VL, Grieve, SM, Yates, P, Rainey-Smith, SR, Chen, J, et al
Alzheimer's & dementia (New York, N. Y.). 2024;(2):e12466
Abstract
INTRODUCTION The Finnish Geriatric Intervention Study (FINGER) led to the global dementia risk reduction initiative: World-Wide FINGERS (WW-FINGERS). As part of WW-FINGERS, the Australian AU-ARROW study mirrors aspects of FINGER, as well as US-POINTER. METHOD AU-ARROW is a randomized, single-blind, multisite, 2-year clinical trial (n = 600; aged 55-79). The multimodal lifestyle intervention group will engage in aerobic exercise, resistance training and stretching, dietary advice to encourage MIND diet adherence, BrainHQ cognitive training, and medical monitoring and health education. The Health Education and Coaching group will receive occasional health education sessions. The primary outcome measure is the change in a global composite cognitive score. Extra value will emanate from blood biomarker analysis, positron emission tomography (PET) imaging, brain magnetic resonance imaging (MRI), and retinal biomarker tests. DISCUSSION The finalized AU-ARROW protocol is expected to allow development of an evidence-based innovative treatment plan to reduce cognitive decline and dementia risk, and effective transfer of research outcomes into Australian health policy. HIGHLIGHTS Study protocol for a single-blind, randomized controlled trial, the AU-ARROW Study.The AU-ARROW Study is a member of the World-Wide FINGERS (WW-FINGERS) initiative.AU-ARROW's primary outcome measure is change in a global composite cognitive score.Extra significance from amyloid PET imaging, brain MRI, and retinal biomarker tests.Leading to development of an innovative treatment plan to reduce cognitive decline.
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Late postoperative vitreous cavity hemorrhage after vitrectomy for proliferative diabetic retinopathy-observation versus intervention.
Brar, AS, Behera, UC, Karande, S, Kanakagiri, A, Sugumar, S, Rani, PK, Vignesh, TP, Manayath, G, Salian, R, Giridhar, A, et al
Indian journal of ophthalmology. 2024;(Suppl 1):S22-S26
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PURPOSE To analyze the outcome of intervention versus observation for vitreous cavity hemorrhage occurring after a 2-month period of blood-free cavity (late postoperative vitreous cavity hemorrhage-POVCH) in eyes operated by vitrectomy for complications of proliferative diabetic retinopathy (PDR). METHODS This study was a 10-year retrospective, observational, multi-center study involving eight major vitreoretinal surgical centers across India from January 2010 to December 2019. The primary objective of the study was to assess the visual and clinical outcomes of various management approaches for late POVCH. The key secondary objective was to determine the best management option that prevented recurrence. Patients with follow-up of less than 6 months of POVCH management were excluded. RESULTS The occurrence of late POVCH was studied in 261 eyes. The median time to occurrence was 7 months (range: 2-87) postvitrectomy/silicone oil removal. The majority (58%) experienced a single, nonrecurring POVCH event. Visual acuity outcome was independent of all management approaches (P = 0.179; mean follow-up 20.7 ± 14.1 months). With watchful observation, spontaneous resolution was noted in 83% (60/72 eyes) of eyes in 81.5 days (interquartile range, 169.75). Silicone oil injection was most effective in preventing recurrence (P < 0.001). CONCLUSION The current treatment practice of late POVCH management in PDR suggests that watchful observation for at least 3 months could be as efficacious as any surgical intervention.
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Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis.
Manikandan, S, Raman, R, Rajalakshmi, R, Tamilselvi, S, Surya, RJ
Indian journal of ophthalmology. 2023;(5):1783-1796
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Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94-0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90-0.96).
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Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma.
Surya, J, Garima, , Pandy, N, Hyungtaek Rim, T, Lee, G, Priya, MNS, Subramanian, B, Raman, R
Indian journal of ophthalmology. 2023;(8):3039-3045
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PURPOSE To analyze the efficacy of a deep learning (DL)-based artificial intelligence (AI)-based algorithm in detecting the presence of diabetic retinopathy (DR) and glaucoma suspect as compared to the diagnosis by specialists secondarily to explore whether the use of this algorithm can reduce the cross-referral in three clinical settings: a diabetologist clinic, retina clinic, and glaucoma clinic. METHODS This is a prospective observational study. Patients between 35 and 65 years of age were recruited from glaucoma and retina clinics at a tertiary eye care hospital and a physician's clinic. Non-mydriatic fundus photography was performed according to the disease-specific protocols. These images were graded by the AI system and specialist graders and comparatively analyzed. RESULTS Out of 1085 patients, 362 were seen at glaucoma clinics, 341 were seen at retina clinics, and 382 were seen at physician clinics. The kappa agreement between AI and the glaucoma grader was 85% [95% confidence interval (CI): 77.55-92.45%], and retina grading had 91.90% (95% CI: 87.78-96.02%). The retina grader from the glaucoma clinic had 85% agreement, and the glaucoma grader from the retina clinic had 73% agreement. The sensitivity and specificity of AI glaucoma grading were 79.37% (95% CI: 67.30-88.53%) and 99.45 (95% CI: 98.03-99.93), respectively; DR grading had 83.33% (95 CI: 51.59-97.91) and 98.86 (95% CI: 97.35-99.63). The cross-referral accuracy of DR and glaucoma was 89.57% and 95.43%, respectively. CONCLUSION DL-based AI systems showed high sensitivity and specificity in both patients with DR and glaucoma; also, there was a good agreement between the specialist graders and the AI system.
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The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review.
Vyas, A, Raman, S, Surya, J, Sen, S, Raman, R
Diagnostics (Basel, Switzerland). 2022;(1)
Abstract
In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.
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A Paradigm Shift in the Management Approaches of Proliferative Diabetic Retinopathy: Role of Anti-VEGF Therapy.
Raman, R, Ramasamy, K, Shah, U
Clinical ophthalmology (Auckland, N.Z.). 2022;:3005-3017
Abstract
Diabetic retinopathy (DR) is considered one of the leading causes of vision loss globally. It principally causes upregulation of pro-angiogenic, proinflammatory, and vascular permeability factors such as vascular endothelial growth factor (VEGF), leading to neovascularisation. The advanced stage of DR or proliferative diabetic retinopathy (PDR) is of more concern, as it leads to vitreous haemorrhage and traction retinal detachment. Various risk factors associated with PDR include hyperglycemia, hypertension, neuropathy, dyslipidemia, anaemia, nephropathy, and retinal complications of drugs used for diabetes. Current management approaches for PDR have been stratified and involve pan-retinal photocoagulation, vitrectomy, and anti-VEGF agents. Given the emerging role of anti-VEGF agents as a favourable adjunct or alternative therapy, they have a critical role in the management of PDR. The review emphasises current management approaches for PDR focusing on anti-VEGF therapy. The review also highlights the risk/benefit evaluation of the various approaches employed for PDR management in various clinical scenarios.
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Addressing the disparities in dementia risk, early detection and care in Latino populations: Highlights from the second Latinos & Alzheimer's Symposium.
Quiroz, YT, Solis, M, Aranda, MP, Arbaje, AI, Arroyo-Miranda, M, Cabrera, LY, Carrasquillo, MM, Corrada, MM, Crivelli, L, Diminich, ED, et al
Alzheimer's & dementia : the journal of the Alzheimer's Association. 2022;(9):1677-1686
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The Alzheimer's Association hosted the second Latinos & Alzheimer's Symposium in May 2021. Due to the COVID-19 pandemic, the meeting was held online over 2 days, with virtual presentations, discussions, mentoring sessions, and posters. The Latino population in the United States is projected to have the steepest increase in Alzheimer's disease (AD) in the next 40 years, compared to other ethnic groups. Latinos have increased risk for AD and other dementias, limited access to quality care, and are severely underrepresented in AD and dementia research and clinical trials. The symposium highlighted developments in AD research with Latino populations, including advances in AD biomarkers, and novel cognitive assessments for Spanish-speaking populations, as well as the need to effectively recruit and retain Latinos in clinical research, and how best to deliver health-care services and to aid caregivers of Latinos living with AD.
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Insights on the mutational landscape of the SARS-CoV-2 Omicron variant receptor-binding domain.
Miller, NL, Clark, T, Raman, R, Sasisekharan, R
Cell reports. Medicine. 2022;(2):100527
Abstract
The Omicron variant features enhanced transmissibility and antibody escape. Here, we describe the Omicron receptor-binding domain (RBD) mutational landscape using amino acid interaction (AAI) networks, which are well suited for interrogating constellations of mutations that function in an epistatic manner. Using AAI, we map Omicron mutations directly and indirectly driving increased escape breadth and depth in class 1-4 antibody epitopes. Further, we present epitope networks for authorized therapeutic antibodies and assess perturbations to each antibody's epitope. Since our initial modeling following the identification of Omicron, these predictions have been realized by experimental findings of Omicron neutralization escape from therapeutic antibodies ADG20, AZD8895, and AZD1061. Importantly, the AAI predicted escape resulting from indirect epitope perturbations was not captured by previous sequence or point mutation analyses. Finally, for several Omicron RBD mutations, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBDdown-RBDdown interface.