Inhibiting membrane rupture with NINJ1 antibodies limits tissue injury.

Plasma membrane rupture (PMR) in dying cells undergoing pyroptosis or apoptosis requires the cell-surface protein NINJ11. PMR releases pro-inflammatory cytoplasmic molecules, collectively called damage-associated molecular patterns (DAMPs), that activate immune cells. Therefore, inhibiting NINJ1 and PMR may limit the inflammation that is associated with excessive cell death. Here we describe an anti-NINJ1 monoclonal antibody that specifically targets mouse NINJ1 and blocks oligomerization of NINJ1, preventing PMR. Electron microscopy studies showed that this antibody prevents NINJ1 from forming oligomeric filaments. In mice, inhibition of NINJ1 or Ninj1 deficiency ameliorated hepatocellular PMR induced with TNF plus D-galactosamine, concanavalin A, Jo2 anti-Fas agonist antibody or ischaemia-reperfusion injury. Accordingly, serum levels of lactate dehydrogenase, the liver enzymes alanine aminotransaminase and aspartate aminotransferase, and the DAMPs interleukin 18 and HMGB1 were reduced. Moreover, in the liver ischaemia-reperfusion injury model, there was an attendant reduction in neutrophil infiltration. These data indicate that NINJ1 mediates PMR and inflammation in diseases driven by aberrant hepatocellular death.

Proprioceptive Modulation of Pallidal Physiology in Cervical Dystonia.

BACKGROUND: There is a growing body of evidence suggesting that botulinum toxin can alter proprioceptive feedback and modulate the muscle-spindle output for the treatment of dystonia. However, the mechanism for this modulation remains unclear. METHODS: We conducted a study involving 17 patients with cervical dystonia (CD), seven of whom had prominent CD and 10 with generalized dystonia (GD) along with CD. We investigated the effects of neck vibration, a form of proprioceptive modulation, on spontaneous single-neuron responses and local field potentials (LFPs) recorded from the globus pallidum externus (GPe) and internus (GPi). RESULTS: Our findings demonstrated that neck vibration notably increased the regularity of neck-sensitive GPi neurons in focal CD patients. Additionally, in patients with GD and CD, the vibration enhanced the firing regularity of non-neck-sensitive neurons. These effects on single-unit activity were also mirrored in ensemble responses measured through LFPs. Notably, the LFP modulation was particularly pronounced in areas populated with burst neurons compared to pause or tonic cells. CONCLUSION: The results from our study emphasize the significance of burst neurons in the pathogenesis of dystonia and in the efficacy of proprioceptive modulation for its treatment. Moreover, we observed that the effects of vibration on focal CD were prominent in the α band LFP, indicating modulation of pallido-cerebellar connectivity. Moreover, the pallidal effects of vibration in GD with CD involved modulation of cerebro-pallidal θ band connectivity. Our analysis provides insight into how vibration-induced changes in pallidal activity are integrated into the downstream motor circuit. © 2023 International Parkinson and Movement Disorder Society.

Examination of potentially modifiable dementia risk factors across the adult life course: The Framingham Heart Study.

INTRODUCTION: We examined for associations between potentially modifiable risk factors across the adult life course and incident dementia. METHODS: Participants from the Framingham Heart Study were included (n = 4015). Potential modifiable risk factors included education, alcohol intake, smoking, body mass index (BMI), physical activity, social network, diabetes, and hypertension. Cox models were used to examine associations between each factor and incident dementia, stratified by early adult life (33-44 years), midlife (45-65 years), and late life (66-80 years). RESULTS: Increased dementia risk was associated with diabetes (hazard ratio [HR] = 1.62; 95% confidence interval [CI] = 1.07-2.46) and physical inactivity (HR = 1.57; 95% CI = 1.12-2.20) in midlife, and with obesity (HR = 1.76; 95% CI = 1.08-2.87) in late life. Having multiple potential modifiable risk factors in midlife and late life was associated with greater risk. DISCUSSION: Potentially modifiable risk factors individually have limited impact on dementia risk in this population across the adult life course, although in combination they may have a synergistic effect. HIGHLIGHTS: Diabetes and physical inactivity in midlife is associated with increased dementia risk. Obesity in late life is associated with increased dementia risk. Having more potentially modifiable risk factors in midlife and late life is associated with greater dementia risk.

Mid- to Late-Life Body Mass Index and Dementia Risk: 38 Years of Follow-up of the Framingham Study.

Growing evidence relates body mass index (BMI) to poorer health outcomes; however, results across studies associating BMI and dementia are conflicting. A total of 3,632 Framingham Offspring participants aged 20 to 60 years at their second health examination (1979-1983) were included in this study, with 190 cases of incident dementia identified by 2017. Cox proportional hazards regression models were fitted to investigate the association of BMI at each of their 8 exams as a baseline for dementia risk and the associations between obesity and dementia across age groups. Spline models were fitted to investigate nonlinear associations between BMI and dementia. Each 1-unit increase in BMI at ages 40-49 years was associated with higher risk of dementia, but with lower risk after age 70 years. Obesity at ages 40-49 years was associated with higher risk of dementia. Overall, the relationship between BMI and dementia risk was heterogeneous across the adult age range. Monitoring BMI at different ages might mediate risk for dementia across an individual's lifetime.

Development and validation of an interpretable deep learning framework for Alzheimer's disease classification.

Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer's disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer's disease and cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer's Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer's disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Exploring the Hierarchical Influence of Cognitive Functions for Alzheimer Disease: The Framingham Heart Study.

BACKGROUND: Although some neuropsychological (NP) tests are considered more central for the diagnosis of Alzheimer disease (AD), there is a lack of understanding about the interaction between different cognitive tests. OBJECTIVE: This study aimed to demonstrate a global view of hierarchical probabilistic dependencies between NP tests and the likelihood of cognitive impairment to assist physicians in recognizing AD precursors. METHODS: Our study included 2091 participants from the Framingham Heart Study. These participants had undergone a variety of NP tests, including Wechsler Memory Scale, Wechsler Adult Intelligence Scale, and Boston Naming Test. Heterogeneous cognitive Bayesian networks were developed to understand the relationship between NP tests and the cognitive status. The performance of probabilistic inference was evaluated by the 10-fold cross validation. RESULTS: A total of 4512 NP tests were used to build the Bayesian network for the dementia diagnosis. The network demonstrated conditional dependency between different cognitive functions that precede the development of dementia. The prediction model reached an accuracy of 82.24%, with sensitivity of 63.98% and specificity of 92.74%. This probabilistic diagnostic system can also be applied to participants that exhibit more heterogeneous profiles or with missing responses for some NP tests. CONCLUSIONS: We developed a probabilistic dependency network for AD diagnosis from 11 NP tests. Our study revealed important psychological functional segregations and precursor evidence of AD development and heterogeneity.

Medical emergencies in a dental school clinic - A 12-year review and lessons learned.

PURPOSE: The aims of this study were to estimate the type and frequency of different medical emergencies that occurred over the study period (twelve years) and discuss the lessons learned and the modifications made in the curriculum to better equip dental students and faculty in their management. MATERIALS AND METHODS: A retrospective study was conducted to evaluate all medical emergencies that needed activation of the response team at our school from 2008 to 2020. RESULTS: The emergency response system was activated 250 times during the 12-year period. There were 132 medical emergencies in the pre-doctoral clinic and 105 events in the post-doctoral clinic (p 0.0680). Most of the emergencies occurred in patients between 45 and 64 years of age. Syncope occurs most often followed by adverse cardiovascular, respiratory, anxiety, and hypoglycemic events. CONCLUSIONS: Medical emergencies occurring in a dental school provide a unique opportunity for students to gain experience in their management. The key lies in preparing the students and faculty to prevent them from occurring, but should these occur, then they should be able to promptly recognize symptoms and institute prompt intervention.

Deep learning for risk-based stratification of cognitively impaired individuals.

Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n = 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-ß levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.192 [discovery], and 0.108 [validation]). Using modern interpretability techniques, we verified that regions important for model prediction are classically associated with AD. We confirmed AD diagnosis labels using postmortem data. We conclude that our framework provides a strategy for risk-based stratification of individuals with MCI and for identifying regions key for disease prognosis.

Structural basis of antibody inhibition and chemokine activation of the human CC chemokine receptor 8.

The C-C motif chemokine receptor 8 (CCR8) is a class A G-protein coupled receptor that has emerged as a promising therapeutic target in cancer. Targeting CCR8 with an antibody has appeared to be an attractive therapeutic approach, but the molecular basis for chemokine-mediated activation and antibody-mediated inhibition of CCR8 are not fully elucidated. Here, we obtain an antagonist antibody against human CCR8 and determine structures of CCR8 in complex with either the antibody or the endogenous agonist ligand CCL1. Our studies reveal characteristic antibody features allowing recognition of the CCR8 extracellular loops and CCL1-CCR8 interaction modes that are distinct from other chemokine receptor - ligand pairs. Informed by these structural insights, we demonstrate that CCL1 follows a two-step, two-site binding sequence to CCR8 and that antibody-mediated inhibition of CCL1 signaling can occur by preventing the second binding event. Together, our results provide a detailed structural and mechanistic framework of CCR8 activation and inhibition that expands our molecular understanding of chemokine - receptor interactions and offers insight into the development of therapeutic antibodies targeting chemokine GPCRs.

Ceiling effects and differential measurement precision across calibrated cognitive scores in the Framingham Study.

OBJECTIVE: To calibrate cognitive assessment data across multiple waves of the Framingham Heart Study (FHS), addressing study design considerations, ceiling effects, and measurement precision. METHOD: FHS participants completed several cognitive assessments including screening instruments and more comprehensive batteries at different study visits. We used expert opinion to assign each cognitive test item to a single domain-memory, executive function, language, visuospatial abilities, or none of the above. As part of a larger cross-study harmonization effort, we calibrated each domain separately using bifactor confirmatory factor analysis (CFA) models, incorporating item parameters for anchor items previously calibrated from other studies and freely estimating item parameters for FHS-specific items. We obtained scores and standard errors (SEs) for each participant at each study visit. We addressed psychometric considerations of ceiling effects and measurement precision. RESULTS: Overall, memory domain scores were the most precisely estimated. Scores for all domains from visits where the Mini-Mental State Examination (MMSE) was the only test administered were imprecisely estimated and suffered from ceiling effects. Scores from visits with a more extensive battery were estimated more precisely and better differentiated between ability levels. CONCLUSIONS: The harmonized and calibrated cognitive data from the FHS should prove useful for future analyses examining cognition and cognitive decline. They will be of particular interest when combining FHS with other studies that have been similarly calibrated. Researchers should be aware of varying levels of measurement precision and the possibility of ceiling effects in their planned analyses of data from the FHS and similar studies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.

Enhancing magnetic resonance imaging-driven Alzheimer's disease classification performance using generative adversarial learning.

BACKGROUND: Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer's disease (AD) classification performance. METHODS: T1-weighted brain MRI scans from 151 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer's Coordinating Center (NACC, n = 565) were used for model validation. RESULTS: The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets. CONCLUSION: This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.

Structural MRI profiles and tau correlates of atrophy in autopsy-confirmed CTE.

BACKGROUND: Chronic traumatic encephalopathy (CTE), a neurodegenerative tauopathy, cannot currently be diagnosed during life. Atrophy patterns on magnetic resonance imaging could be an effective in vivo biomarker of CTE, but have not been characterized. Mechanisms of neurodegeneration in CTE are unknown. Here, we characterized macrostructural magnetic resonance imaging features of brain donors with autopsy-confirmed CTE. The association between hyperphosphorylated tau (p-tau) and atrophy on magnetic resonance imaging was examined. METHODS: Magnetic resonance imaging scans were obtained by medical record requests for 55 deceased symptomatic men with autopsy-confirmed CTE and 31 men (n = 11 deceased) with normal cognition at the time of the scan, all >60 years Three neuroradiologists visually rated regional atrophy and microvascular disease (0 [none]-4 [severe]), microbleeds, and cavum septum pellucidum presence. Neuropathologists rated tau severity and atrophy at autopsy using semi-quantitative scales. RESULTS: Compared to unimpaired males, donors with CTE (45/55=stage III/IV) had greater atrophy of the orbital-frontal (mean diff.=1.29), dorsolateral frontal (mean diff.=1.31), superior frontal (mean diff.=1.05), anterior temporal (mean diff.=1.57), and medial temporal lobes (mean diff.=1.60), and larger lateral (mean diff.=1.72) and third (mean diff.=0.80) ventricles, controlling for age at scan (ps<0.05). There were no effects for posterior atrophy or microvascular disease. Donors with CTE had increased odds of a cavum septum pellucidum (OR = 6.7, p < 0.05). Among donors with CTE, greater tau severity across 14 regions corresponded to greater atrophy on magnetic resonance imaging (beta = 0.68, p < 0.01). CONCLUSIONS: These findings support frontal-temporal atrophy as a magnetic resonance imaging finding of CTE and show p-tau accumulation is associated with atrophy in CTE.

Preparation and Evaluation of N-Trimethyl Chitosan Nanoparticles of Flurbiprofen for Ocular Delivery.

PURPOSE: A major challenge in ocular therapeutics is poor bioavailability of drug, 1% or even less of the instilled dose is absorbed and frequent administration of conventional products leads to poor adherence to therapy. Hence, the present study is to synthesize N-trimethyl chitosan (TMC), a water-soluble chitosan derivative and to prepare flurbiprofen (FLU):hydroxyl propyl-ß-cyclodextrin (HP-ß-CD) complex-loaded nanoparticles for treatment of bacterial conjunctivitis which aims to increase the residence time in ocular tissue, thus enhancing patient compliance and improved efficacy. METHODS: TMC was synthesized and characterized by 1H NMR and FT-IR. TMC and chitosan (CS) nanoparticles containing inclusion complex were prepared by ionic gelation using sodium tripolyphosphate (TPP). The nanoparticles thus obtained were evaluated for particle size, zeta potential, drug entrapment, in-vitro release, in-vitro mucoadhesion, and TEM for morphology and irritation potential was evaluated by the HET-CAM technique. RESULTS: N-methyl quaternization of CS was confirmed by 1H NMR. The particle size and zeta potential of the TMC nanoparticles were found to be 201 ± 1.55 nm and +13.9 ± 1.697 mV and that of CS nanoparticles were 361.2 ± 1.55 nm and +10.9 ± 0.424 mV, respectively. The entrapment of FLU- HP-ß-CD inclusion complex in polymeric nanoparticles was found to be 10.91 ± 1.541%. The observed in-vitro release profile of TMC nanoparticles indicated characteristic burst release followed by delayed release. HET-CAM studies demonstrated the ocular safety of TMC nanoparticles. CONCLUSION: The developed TMC nanoparticles offered prolonged release potential for transmucosal ocular delivery of hydrophobic flurbiprofen.

Temporal association of neuropsychological test performance using unsupervised learning reveals a distinct signature of Alzheimer's disease status.

INTRODUCTION: Subtle cognitive alterations that precede clinical evidence of cognitive impairment may help predict the progression to Alzheimer's disease (AD). Neuropsychological (NP) testing is an attractive modality for screening early evidence of AD. METHODS: Longitudinal NP and demographic data from the Framingham Heart Study (FHS; N = 1696) and the National Alzheimer's Coordinating Center (NACC; N = 689) were analyzed using an unsupervised machine learning framework. Features, including age, logical memory-immediate and delayed recall, visual reproduction-immediate and delayed recall, the Boston naming tests, and Trails B, were identified using feature selection, and processed further to predict the risk of development of AD. RESULTS: Our model yielded 83.07 ± 3.52% accuracy in FHS and 87.57 ± 1.19% accuracy in NACC, 80.52 ± 3.93%, 86.74 ± 1.63% sensitivity in FHS and NACC respectively, and 85.63 ± 4.71%, 88.41 ± 1.38% specificity in FHS and NACC, respectively. DISCUSSION: Our results suggest that a subset of NP tests, when analyzed using unsupervised machine learning, may help distinguish between high- and low-risk individuals in the context of subsequent development of AD within 5 years. This approach could be a viable option for early AD screening in clinical practice and clinical trials.

Using data science to diagnose and characterize heterogeneity of Alzheimer's disease.

INTRODUCTION: Despite the availability of age- and education-adjusted standardized scores for most neuropsychological tests, there is a lack of objective rules in how to interpret multiple concurrent neuropsychological test scores that characterize the heterogeneity of Alzheimer's disease. METHODS: Using neuropsychological test scores of 2091 participants from the Framingham Heart Study, we devised an automated algorithm that follows general diagnostic criteria and explores the heterogeneity of Alzheimer's disease. RESULTS: We developed a series of stepwise diagnosis rules that evaluate information from multiple neuropsychological tests to produce an intuitive and objective Alzheimer's disease dementia diagnosis with more than 80% accuracy. DISCUSSION: A data-driven stepwise diagnosis system is useful for diagnosis of Alzheimer's disease from neuropsychological tests. It demonstrated better performance than the traditional dichotomization of individuals' performance into satisfactory and unsatisfactory outcomes, making it more reflective of dementia as a spectrum disorder. This algorithm can be applied to both within clinic and outside-of-clinic settings.

A Novel Method to Generate MNase Ladders Reveal Rapid Chromatin Remodeling upon Gametogenesis and Mating in Chlamydomonas.

To circumvent nuclei isolation for nucleosomal mapping of wild-type (cell walled) algal cells, we developed a quick and versatile methodology, by abrasion of whole cells (Chlamydomonas, Scenedesmus and yeast), allowing Micrococcal Nuclease (MNase) direct access to nuclear chromatin, in situ. Varying parameters such as bead abrasion, vortex and incubation conditions, we optimized capture of an 'early digest' which may probe chromatin differentially, based on nucleosome accessibility. A comparison of such ladders across vegetative cells, gametes and zygotes revealed an increase in the average nucleosomal repeat length (+17-34nt) upon gametogenesis, indicating a trend of chromatin compaction. Using PCR, we compared promoter enrichment in increasing orders of fractionated nucleosomal repeats (mono-, di-, up to penta-), each differing in cleavability based on chromatin accessibility. Concordant with higher gene expression (mating locus), promoters revealed an enrichment in mono-nucleosomal fractions. Interestingly, the zygote specific gene, MT0828 displayed rapid remodelling from penta-nucleosomal enrichment when completely repressed (vegetative), to intermediate states during gametogenesis (24hrs), which finally shifted to being largely mono-nucleosomal, when induced (1h zygotes). Summarizing three candidate genes from the mating locus, we conclude that the MNase based 'Chromatin Accessibility Assay' can track a range of large-scale rapid chromatin remodelling transitions within the binaries of gene expression.

Small Molecule Derived From Carboxyethylpyrrole Protein Adducts Promotes Angiogenesis in a Mouse Model of Peripheral Arterial Disease.

Background CEP (ω-[2-carboxyethyl]pyrrole) protein adducts are the end products of lipid oxidation associated with inflammation and have been implicated in the induction of angiogenesis in pathological conditions such as tissue ischemia. We synthesized small molecules derived from CEP protein adducts and evaluated the angiogenic effect of the CEP analog CEP 03 in the setting of peripheral arterial disease. Methods and Results The angiogenic effect of CEP 03 was assessed by in vitro analysis of primary human microvascular endothelial cell proliferation and tubelike formation in Matrigel (Corning). In the presence of CEP 03, proliferation of endothelial cells in vitro increased by 27±18% under hypoxic (1% O2) conditions, reaching similar levels to that of VEGF A (vascular endothelial growth factor A) stimulation (22±10%), relative to the vehicle control treatment. A similar effect of CEP 03 was demonstrated in the increased number of tubelike branches in Matrigel, reaching >70% induction in hypoxia, compared with the vehicle control. The therapeutic potential of CEP 03 was further evaluated in a mouse model of peripheral arterial disease by quantification of blood perfusion recovery and capillary density. In the ischemic hind limb, treatment of CEP 03 encapsulated within Matrigel significantly enhanced blood perfusion by 2-fold after 14 days compared with those treated with Matrigel alone. Moreover, these results concurred with histological finding that treatment of CEP 03 in Matrigel resulted in a significant increase in microvessel density compared with Matrigel alone. Conclusions Our data suggest that CEP 03 has a profound positive effect on angiogenesis and neovessel formation and thus has therapeutic potential for treatment of peripheral arterial disease.

Oral Health for Older Adults: An Interprofessional Workshop for Medical Students.

INTRODUCTION: Because many older adults lack dental insurance and have limited or no access to dental care, it is essential to train future physicians to conduct brief oral health assessments on them. Likewise, interprofessional educational experiences are crucial in teaching medical students the skills necessary to provide comprehensive, team-based care to complex and vulnerable populations. Thus, this workshop was designed to increase fourth-year medical students' knowledge and confidence in performing oral health examinations on older adult patients using an interprofessional and hands-on approach. METHODS: The curriculum includes an online presession self-study module followed by a 75-minute workshop. The workshop is comprised of a brief introduction (5 minutes), a lecture about the impact of oral health on older adults (30 minutes), a hands-on skill session practicing a focused oral exam led by dental students (30 minutes), and a large-group debrief and wrap-up (10 minutes). RESULTS: A pre-/postsurvey assessed learners' knowledge, attitudes, and confidence in oral health skills. The results were compared to a lecture-only format that was in place prior to the implementation of the workshop. In comparison to medical students who received the lecture-only format, those who participated in the workshop showed a greater increase in confidence and skills over time. The medical students also expressed interest in more frequent opportunities for collaborative learning experiences with dental students. DISCUSSION: This workshop was successful in introducing an interprofessional experience to medical students in order to ensure more comprehensive and coordinated care for older adult patients in the future.

Study of menstrual patterns in adolescent girls with disabilities in a residential institution.

BACKGROUND: The gynecological health needs of girls with disabilities is an issue related to their rights as individuals. OBJECTIVE: The objective of this study is to describe the menstrual pattern of girls with disabilities. MATERIALS AND METHODS: A descriptive study was undertaken on thirty girls with different types of disabilities in a residential institution. The diagnosis, type of disability, secondary sexual characters, age at menarche, menstrual pattern and practice of menstrual hygiene was noted. RESULTS: The girls with intellectual disabilities had later age of menarche, irregular cycles and more behaviour problems. The girls with hearing impairment and locomotor disabilities had normal menstrual pattern. The girl with low vision had earlier menarche and regularized cycles. Girls with normal intelligence and mild intellectual disabilities were independent in maintaining menstrual hygiene. The menstrual disorders are managed conservatively in accordance with latest guidelines. CONCLUSION: Onset of menarche is towards the extremes of normal age range in girls with intellectual disabilities or visual impairment but not in girls with hearing impairments or locomotor disabilities. Girls with disabilities have potential for independent menstrual care. Menstrual disorders were managed conservatively.