Evaluasi Program Keluarga Berencana Metode Kontrasepsi Jangka Panjang

Abstract: This study aims to determine the evaluation of the long-term contraceptive method planning program (KB-MKJP) and the inhibiting factors for the long-term contraceptive method family planning program (KB-MKJP). The assessment indicators used include inputs, processes, outputs, outcomes. The research method used is qualitative with a qualitative descriptive approach. Informants in this study were the Head of Duri City Health Center, Family Planning Field Extension Officer (PLKB), Midwives, and the Community. This research uses informant selection technique with purposive sampling. The types and data collection techniques used are primary data using interview techniques and secondary data using observation techniques. The results showed that the implementation of the long-term family planning program was not running optimally.Abstrak: Penelitian ini bertujuan untuk mengetahui Evaluasi Program Berencana Metode Kontrasepsi Jangka Panjang (KB-MKJP) dan Faktor Penghambat Program Keluarga Berencana Metode Kontrasepsi Jangka Panjang (KB-MKJP). Indikator penilaian yang digunakan meliputi input, proses, output, outcomes. Metode penelitian yang digunakan adalah kualitatif dengan pendekatan deskriptif kualitatif. Informan dalam penelitian ini adalah Kepala Puskesmas Duri Kota, Penyuluh Lapangan Keluarga Berencana (PLKB), Bidan, dan Masyarakat. Penelitian ini menggunakan teknik pemilihan informan dengan purposive sampling. Jenis dan teknik pengumpulan data yang digunakan yaitu data primer yang menggunakan teknik wawancara serta data sekunder yang menggunakan teknik observasi. Hasil penelitian menunjukkan pelaksanaan program keluarga berencana jangka panjang kurang berjalan maksimal

The age-dependent associations of white matter hyperintensities and neurofilament light in early- and late-stage Alzheimer's disease

Neurofilament light (NFL) is an emerging marker of axonal degeneration. This study investigated the relationship between white matter hyperintensities (WMHs) and plasma NFL in a large elderly cohort with, and without, cognitive impairment. We used the Alzheimer's Disease Neuroimaging Initiative and included 163 controls, 103 participants with a significant memory concern, 279 with early mild cognitive impairment (EMCI), 152 with late mild cognitive impairment (LMCI), and 130 with Alzheimer's disease, with 3T MRI and plasma NFL data. Multiple linear regression models examined the relationship between WMHs and NFL, with and without age adjustment. We used smoking status, history of hypertension, history of diabetes, and BMI as additional covariates to examine the effect of vascular risk. We found increases of between 20% and 41% in WMH volume per 1SD increase in NFL in significant memory concern, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease groups (p < 0.02). Marked attenuation of the positive associations between WMHs and NFL were seen after age adjustment, suggesting that a significant proportion of the association between NFL and WMHs is age-related. No effect of vascular risk was observed. These results are supportive of a link between WMH and axonal degeneration in early to late disease stages, in an age-dependent, but vascular risk-independent manner

Instantiated mixed effects modeling of Alzheimer's disease markers

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease are of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified “marker signature” that defines a subpopulation of similar cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models

Genomics and CSF Analyses Implicate Thyroid Hormone in Hippocampal Sclerosis of Aging

We report evidence of a novel pathogenetic mechanism in which thyroid hormone dysregulation contributes to dementia in elderly persons. Two single nucleotide polymorphisms (SNPs) on chromosome 12p12 were the initial foci of our study: rs704180 and rs73069071. These SNPs were identified by separate research groups as risk alleles for non-Alzheimer’s neurodegeneration. We found that the rs73069071 risk genotype was associated with hippocampal sclerosis (HS) pathology among people with the rs704180 risk genotype (National Alzheimer’s Coordinating Center/Alzheimer’s Disease Genetic Consortium data; n = 2113, including 241 autopsy-confirmed HS cases). Furthermore, both rs704180 and rs73069071 risk genotypes were associated with widespread brain atrophy visualized by MRI (Alzheimer’s Disease Neuroimaging Initiative data; n = 1239). In human brain samples from the Braineac database, both rs704180 and rs73069071 risk genotypes were associated with variation in expression of ABCC9, a gene which encodes a metabolic sensor protein in astrocytes. The rs73069071 risk genotype was also associated with altered expression of a nearby astrocyte-expressed gene, SLCO1C1. Analyses of human brain gene expression databases indicated that the chromosome 12p12 locus may regulate particular astrocyte-expressed genes induced by the active form of thyroid hormone, triiodothyronine (T3). This is informative biologically, because the SLCO1C1 protein transports thyroid hormone into astrocytes from blood. Guided by the genomic data, we tested the hypothesis that altered thyroid hormone levels could be detected in cerebrospinal fluid (CSF) obtained from persons with HS pathology. Total T3 levels in CSF were elevated in HS cases (p \u3c 0.04 in two separately analyzed groups), but not in Alzheimer’s disease cases, relative to controls. No change was detected in the serum levels of thyroid hormone (T3 or T4) in a subsample of HS cases prior to death. We conclude that brain thyroid hormone perturbation is a potential pathogenetic factor in HS that may also provide the basis for a novel CSF-based clinical biomarker

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Introduction Developing cross‐validated multi‐biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. Methods We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid‐PET and fluorodeoxyglucose positron‐emission tomography (FDG‐PET) to predict rates of cognitive decline. Prediction models were trained in autosomal‐dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross‐validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model‐based risk enrichment was estimated. Results A model combining all biomarker modalities and established in ADAD predicted the 4‐year rate of decline in global cognition (R2 = 24%) and memory (R2 = 25%) in sporadic AD. Model‐based risk‐enrichment reduced the sample size required for detecting simulated intervention effects by 50%–75%. Discussion Our independently validated machine‐learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease

Background: Although convolutional neural networks (CNN) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including N=1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps. Results: Across three independent datasets, group separation showed high accuracy for AD dementia vs. controls (AUC$\geq$0.92) and moderate accuracy for MCI vs. controls (AUC$\approx$0.75). Relevance maps indicated that hippocampal atrophy was considered as the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r$\approx$-0.86, p<0.001). Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels.Comment: 24 pages, 9 figures/tables, supplementary material, source code available on GitHu

The psychosocial impact of chronic wounds on patients with severe epidermolysis bullosa.

OBJECTIVE To explore the lived experience of individuals with chronic wounds associated with dystrophic and junctional epidermolysis bullosa (EB),to improve understanding and, therefore, enhance the care provided to this group of patients by acquiring in depth data on the psychosocial issues that affect them. METHOD A phenomenological study using interpretive phenomenological analysis was employed. A purposive sampling method was used with six individuals replying to postal invitation to participate. RESULTS Following one-to-one interviews, six superordinate themes were identified. These were: coping, pain, perceptions, emotional impact, social impact and support network, each with subordinate themes. All of the superordinate themes have been identified by previous research into chronic wounds, burns and disfiguring conditions; however, new subordinate themes arose. CONCLUSION This study highlighted the need for individuals with EB to have a multidisciplinary approach to their care with a particular need for pain management, psychological intervention and nursing support from those whom clients perceive as understanding the requirements of patients with EB. Further research into identity issues in individuals with EB is advocated. DECLARATION OF INTEREST There were no external sources of funding for this study.The authors have no conflicts of interest to declare

Successful renal transplant in a patient with non-Herlitz junctional epidermolysis bullosa.

Non-Herlitz junctional epidermolysis bullosa (NH-JEB) is a very rare inherited disorder, with an array of complications. We present the case of a 33-year-old patient of Chinese origin, diagnosed with NH-JEB in childhood, who developed severe IgA nephropathy. His renal impairment was initially treated by haemodialysis. He underwent successful renal transplantation, resulting in normalization of his renal function. To our knowledge, this is the first report of renal transplantation in a patient with epidermolysis bullosa, which should support use of this intervention in other similar cases