Backward Model Research Articles

Comprehensive Clinical Characterisation of Brain Fog in Adults Reporting Long COVID Symptoms.

(1) Introduction: A subset of individuals experiencing long COVID symptoms are affected by ‘brain fog’, a lay term that often refers to general cognitive dysfunction but one that is still poorly characterised. In this study, a comprehensive clinical characterisation of self-reported brain fog was conducted vis-à-vis other long COVID symptoms and parameters of mental, cognitive, and physical health. (2) Methodology: Adult participants reporting long COVID symptoms were recruited from hospital clinics and as self-referrals. Participants completed a battery of questionnaires and clinical assessments, including COVID-19 history, symptomatology, self-reported scales (Chalder Fatigue Scale [CFQ], Center for Epidemiological Studies Depression Scale, and Impact of Events Scale–Revised), computer-based cognitive assessments (simple response time and choice reaction time tasks), physical performance tests (gait velocity and muscle strength assessments), and an orthostatic active stand test. A systematic comparison between participants with and without self-reported brain fog was conducted, and a backwards binary logistic regression model was computed to identify the strongest independent associations with brain fog. This was complemented by an automatic cluster analysis to rank the importance of associations. Finally, a structural equation model was postulated with a causal model of key symptomatic indicators and functional consequences of brain fog as a latent variable. (3) Results: Of 108 participants assessed, brain fog was a self-reported symptom in 71 (65.7%) participants. Those with brain fog were at a longer point in time since COVID-19 onset and reported longer duration of low activity during the acute illness. When assessed, those with brain fog had higher frequencies of subjective memory impairment, word-finding difficulties, dizziness, myalgia, arthralgia, hyperhidrosis, cough, voice weakness, throat pain, visual and hearing problems, dysosmia, paraesthesia, chest pain, skin rashes, and hair loss; mean scores in fatigue, depression, and post-traumatic stress scales were higher; performance in both computer-based cognitive tasks was poorer; and measured gait speed and grip strength were lower. The logistic regression suggested that the best independent associations with brain fog were memory impairment, CFQ, and myalgia. The cluster analysis suggested that the most important associations with brain fog were CFQ, dizziness, myalgia, reduced gait speed, word-finding difficulties, reduced grip strength, and memory impairment. The SEM was consistent with key indicators of brain fog being CFQ, dizziness, myalgia, word-finding difficulties, and memory impairment; and reduced grip strength, gait speed, and cognitive response times its functional consequences. (4) Conclusions: The findings indicate that self-reported brain fog in long COVID is a recognisable symptom cluster primarily characterised by fatigue, dizziness, myalgia, word-finding difficulties, and memory impairment and has adverse psychological and psychomotor correlates. In long COVID, brain fog should be regarded as a wide-ranging symptom and addressed holistically with medical, psychological, and rehabilitative supports as guided by individual needs.

Effects of Organ System Courses of the First Two Years of Medical School on the Performance of the Comprehensive Osteopathic Medical Licensing Examination of the United States (COMLEX-USA) Level 2-Cognitive Evaluation

Objectives: The Comprehensive Osteopathic Medical Licensing Examination of the United States (COMLEX-USA) Level 2-Cognitive Evaluation (CE) is a board examination that medical students usually take in the third or fourth year of medical school. A few researchers have investigated the prediction of COMLEX Level 2-CE scores based on the performance in third-year clerkships. However, given how close the clerkships are to the board exam, this type of prediction is too late for students to have adequate time to get assistance to prepare for COMLEX Level 2-CE. We aimed to investigate the predictive value of each organ system course during the first two years in predicting COMLEX Level 2-CE performance. Our findings will help students at risk focus on important basic and clinical sciences much earlier before preparing for COMLEX Level 2-CE.Methods: Academic data from students enrolled at Rocky Vista University College of Osteopathic Medicine from 2011 to 2017 were retrieved. Data included the Medical College Admission Test (MCAT) scores, course grades in the first two years of medical school, COMLEX Level 1 scores, and COMLEX Level 2-CE scores. Pearson correlation coefficients, a multiple linear regression model, and a backward stepwise regression model were generated for analysis.Results: The highest correlation with COMLEX Level 2-CE scores was the COMLEX Level 1 score, followed by the performances in the third-semester Cardiovascular System II (CV II) and Renal System II (REN II) courses. Multiple linear regression and backward stepwise regression predictive models found that scores on third-semester CV II and Principles of Clinical Medicine III (PCM III) were the most significant predictors of performance on Level 2-CE. Both models explained 46% of the variance in COMLEX Level 2-CE scores.Conclusions: Performances in third-semester courses are the most important predictors of COMLEX Level 2-CE scores.

Real-Time Synchronous 3-D Detection of Air Pollution and Wind Using a Solo Coherent Doppler Wind Lidar

The monitoring and tracking of urban air pollution is a challenging environmental issue. The approach of synchronous 3-D detection of wind and pollution using a solo coherent Doppler wind lidar (CDWL) is developed and demonstrated. The 3-D distribution of pollutant is depicted by the backscatter coefficient based on signal intensity of CDWL. Then, a high-resolution wind field is derived to track the local air pollution source with its diffusion and to analyze transboundary air pollution episodes. The approach is experimentally implemented in a chemical industry park. Smoke plumes caused by point source pollutions are captured well using plan position indicator (PPI) scanning with low elevation. A typical source of pollution is located, combining the trajectory of the smoke plume and the horizontal wind vector. In addition, transboundary air pollution caused by the transport of dust storms is detected in a vertical profile scanning pattern, which is consistent with the results of national monitoring stations and backward trajectory models. Our present work provides a significant 3-D detection approach to air pollution monitoring with its sources, paths, and heights by using a solo-CDWL system.

Two-step aberration correction: application to transcranial histotripsy

Objective: Phase aberration correction is essential in transcranial histotripsy to compensate for focal distortion caused by the heterogeneity of the intact skull bone. This paper improves the 2-step aberration correction (AC) method that has been previously presented and develops an AC workflow that fits in the clinical environment, in which the computed tomography (CT)-based analytical approach was first implemented, followed by a cavitation-based approach using the shockwaves from the acoustic cavitation emission (ACE). Approach: A 700 kHz, 360-element hemispherical transducer array capable of transmit-and-receive on all channels was used to transcranially generate histotripsy-induced cavitation and acquire ACE shockwaves. For CT-AC, two ray-tracing models were investigated: a forward ray-tracing model (transducer-to-focus) in the open-source software Kranion, and an in-house backward ray-tracing model (focus-to-transducer) accounting for refraction and the sound speed variation in skulls. Co-registration was achieved by aligning the skull CT data to the skull surface map reconstructed using the acoustic pulse-echo method. For ACE-AC, the ACE signals from the collapses of generated bubbles were aligned by cross-correlation to estimate the corresponding time delays. Main results: The performance of the 2-step method was tested with 3 excised human calvariums placed at 2 different locations in the transducer array. Results showed that the 2-step AC achieved 90 ± 7% peak focal pressure compared to the gold standard hydrophone correction. It also reduced the focal shift from 0.84 to 0.30 mm and the focal volume from 10.6 to 2.0 mm3 on average compared to the no AC cases. Significance: The 2-step AC yielded better refocusing compared to either CT-AC or ACE-AC alone and can be implemented in real-time for transcranial histotripsy brain therapy.

Delivering person-centred palliative care in long-term care settings: is humanism a quality of health-care employees or their organisations?

Abstract Reflecting on sustained calls for patient-centredness and culture change in long-term care, we evaluated the relative importance of personal and organisational predictors of palliative care, hypothesising the former as weaker predictors than the latter. Health-care employees (N = 184) from four Canadian long-term care homes completed a survey of person-centred care, self-efficacy, employee wellbeing and occupational characteristics. Using backward stepwise regression models, we examined the relative contributions of these variables to person-centred palliative care. Specifically, blocks of variables representing personal, organisational and occupational characteristics; palliative care self-efficacy; and employee wellbeing were simultaneously regressed on variables representing aspects of person-centred care. The change in R2 associated with the removal of each block was examined to determine each block's overall contribution to the model. We found that occupational characteristics (involvement in care planning), employee wellbeing (compassion satisfaction) and self-efficacy were reliably associated with person-centred palliative care (p < 0.05). Facility size was not associated, and facility profit status was less consistently associated. Demographic characteristics (gender, work experience, education level) and some aspects of employee wellbeing (burnout, secondary trauma) were also not reliably associated. Overall, these results raise the possibility that humanistic care is less related to intrinsic characteristics of employees, and more related to workplace factors, or to personal qualities that can be cultivated in the workplace, including meaningful role engagement, compassion and self-efficacy.

Influence of Deep Serratus Anterior Plane Block on Chronic Pain at 3 Months After Breast-Conserving Surgery: Prospective, Cohort Study.

In 2015, we evaluated our practices regarding pain after breast-conserving surgery. Thereafter, we have adapted our practices by performing a systematic deep serratus plane block before the surgical incision. In 2019, we assessed the impact of these changes in terms of chronic pain. The main objective of this study was to evaluate the prevalence of chronic pain 3 months after this type of surgery. All patients treated with breast-conserving surgery as outpatients were included in this prospective study between April and July 2019. After inducing general anesthesia, 20 mL of ropivacaine 3.75 mg/mL were injected under the serratus muscle. Morphine titration was performed in the recovery room according to pain scores. A pain and quality of life questionnaire was sent 3 months after surgery. A backward logistic regression model was applied to calculate the adjusted odds ratios. The final analysis involved 137 patients. Three months after surgery, 43 patients (31%) reported persistent pain related to the surgery. Maximum pain in the last 24 hours was moderate to severe in 60% of cases, 16 patients (35%) took painkillers. Morphine titration in the recovery room was required in 25 patients (18%). Younger age and the use of lidocaine to prevent after injection of propofol during general anesthesia induction appeared to be protective factors for the risk of pain at 3 months (secondary endpoints). No persistent pain at 3 months was reported in 69% of cases. Furthermore, the use of a deep serratus anterior plane block before the surgical incision has limited the need for morphine titration in the recovery room to <1 patient in 5. These evaluations of professional practices should be encouraged.

173-LB: Cognitive Performance and Diabetic Retinopathy

Background: Diabetic retinopathy (DR) is a chronic diabetes complication. People with type 2 diabetes mellitus (T2DM) have two times the risk for dementia, suggesting it is a new chronic diabetes complication. Objective: Evaluate the association of DR with cognitive performance in a T2DM population. Methods: Cross-sectional study with 400 T2DM adults from whom socio-demographic, clinical, laboratory data, screening test for depression symptoms [Patient Health Questionaire-9 (PHQ-9) ], Mini Mental State Examination (MMSE) , Semantic Verbal Fluency test, Trail Making test A and B, Word Memory test were collected and performed. All cognitive test scores were converted into Global Cognition z-Score [GCS (z) ]. The association between GCS (z) &amp;lt; 0 with DR was performed using backward multivariate binary logistic regression model adjusted forage ≥ 65 years, school years ≤ 6 years, DM duration ≥ 10 years, depression symptoms score &amp;gt; 9 at PHQ-9, arterial hypertension, physical activity, diabetic retinopathy, macular edema and cardiovascular disease. Results: After exclusions, the 251 eligible patients were 56.6% female, mean age 61.1 (±9.8) years, DM duration of 12.6 (±8.9) years and 7.6 (±4.2) years of school education. DR prevalence was 46.5%. Multivariate Binaric Logistic Regression Model showed an association between DR and GCS (z) &amp;lt; 0, with odds ratio (CI95%) of 2.50 (1.18-5.34) , adjusted for age, low education level, arterial hypertension and depression symptoms [OD and CI95%respectively: 5.46 (2.42-12.34) ; 12.19 (5.62-26.46) ; 2.55 (0.88-7.39) ; 3.53 (1.55-8.07) ]. Conclusion: In this T2DMpopulation, having DR increased the chance for worse cognitive performance even when adjusted for age, low education level, presence of arterial hypertension, and depression symptoms. Disclosure A. R. A. Faria: None.

Clinical and pathological predictors of micrometastasis in marginal thickness melanoma.

e21585 Background: Sentinel lymph node biopsy (SLNB) remains controversial in patients with localized marginal thickness melanoma (MTM). Nonetheless, micrometastasis (miLN) i.e. the presence of clinically occult lymph node (LN) metastasis, upstages localized tumors to Stage III, indicating the need for more extensive surgical resection and escalated systemic therapy. We aim to identify disease predictors associated with miLN in MTM. Methods: This National Cancer Database (2004-2018) analysis includes patients with T1a/T1b, N0, and M0 (AJCC 8th edition) melanoma (Breslow’s depth: 0.76-1.00 mm). We selected 2 cohorts based on the pathological status of LN metastasis, as per SLNB or elective lymphadenectomy: negative LN versus miLN. Bivariate association of various clinical characteristics and LN status was assessed using Chi-square and Fisher’s exact tests. Significant covariates were integrated into a multivariable logistic regression (MLR) with a backward elimination model to identify the risk factors associated with the presence of miLN. A multivariable Cox regression model with backward elimination compared overall survivor (OS) between both cohorts, adjusting for significantly unbalanced factors. Results: N = 72,945 patients with clinical N0 MTM were identified. N = 68,912 (94.5%) had negative LN and N = 4,033 (5.5%) had miLN on pathology. Ethnicity (p = 0.260), Charlson/Deyo comorbidity score (p = 0.0894) and LDH levels (p = 0.2278) did not significantly vary across cohorts. On MLR analysis, patients with miLN were more likely to be younger and have tumors located outside the head &amp; neck. Tumors with miLN were more likely to show lymphovascular invasion, vertical growth, and a mitotic rate of ≥ 1 but less likely to show positive tumor regression. Ulceration was not an independent risk factor of miLN (p = 0.9305). Multivariable analysis showed that patients with miLN had lower OS rates than those with negative LN (HR = 1.66; 95% CI: 1.45-1.90; p &lt; 0.0001). Conclusions: miLN is associated with a worse prognosis, warranting early recognition and prompt treatment initiation. Despite NCCN guidelines incorporating tumor ulceration into decision-making regarding the need for SLNB in clinical N0 MTM, we show that tumor ulceration was not significantly associated with miLN in MTM. However, patient age, tumor location, tumor mitotic rate, lymphovascular invasion, and vertical growth phase were associated with miLN and thus need to be assessed when considering SLNB.[Table: see text]

The Characteristics of Obesogenic Environment and Their Association With BMI Among the Elderly Population in Rural South Korea

ObjectivesThe purpose of this research is to analyze the environmental factors affecting obesity in the elderly in rural South Korea. MethodsA survey and Geographical Information System(GIS) data analysis were conducted on 300 elderly people aged 65 and over living in four rural Counties in two Provinces. Formative research was conducted to develop the survey questionnaire. The perception of food environment was measured with five questions, mainly on availability, affordability, and accessibility. The perception of walking environment was measured with eleven questions on esthetics, safety, traffic and community. Social cohesion and the sense of community were asked for measuring social environment. T-test, chi-squared test and stepwise backward regression modeling were performed using Stata/SE 17.0 version. ResultsWe found that the participants from Gangwon-do had higher obesity prevalence than the ones from Jeollanam-do. There was no significant difference in the physical environment between the two Provinces using GIS data analysis; however, there were differences in the perception of the physical environment and the social environment. It was confirmed that the perceptions of the physical environment and social environment were more positive among participants from Jeollanam-do, where obesity prevalence was lower. In stratified stepwise regression models, better perception of food environment was associated with lower BMI in both Provinces. ConclusionsIn this study, various obesity-related environmental factors were explored in rural South Korea. The differences in obesity prevalence in four rural Counties may be explained by different perception on food, walking, and social environments, despite of the similar background factors, such as age distribution, sex ratio, educational level, and financial environment. These findings confirmed that even areas with similar physical settings have different environmental characteristics, which should be considered in developing effective obesity prevention programs. Future research should focus on the development of environmental evaluation tools and the effective measures to enhance the perception of social environment in rural South Korea. Funding SourcesMinistry of Health and Welfare, South Korea NRF of Korea (No. 2021R1A6A1A03044501, No. 2021R1I1A306024811).

Differential outcome of females with uterine versus extra-uterine leiomyosarcomas, a retrospective analysis.

e23557 Background: Leiomyosarcomas, are aggressive sarcomas with survival rates among the lowest of all soft tissue sarcomas. Patients with uterine leiomyosarcoma (ULMS) have worse overall survival (OS) than those with extra-uterine leiomyosarcoma (EULMS). The effect of several clinical factors on OS have been investigated, including age, tumor size, grade, stage, and use of chemotherapy. Looking at the epigenetics of the 2 diseases, differentially methylated regions (DMRs) were statistically significant between the 2 groups. Methods: The 2004-2016 National Cancer Database was queried for females with ULMS and EULMS. We investigated several factors as predictors of binary survival status (alive or dead): patient’s age, race, year at diagnosis, tumor grade, size and stage, receipt of surgical treatment, chemotherapy, palliative care, surgical margins and time to treatment in days. Univariate and backward stepwise multivariate logistic regression models were used to determine prognostic factors associated with survival in each group. Kaplan-Meier (KM) method with log-rank test was used to compare OS rates between the 2 groups. All analyses were conducted using Stata/SE 15.1. Results: The total number of subjects was 16883. The median OS was 36.7 months in ULMS vs 57.2 in EULMS (P &lt; 0.001). Older age was associated with a lower likelihood of survival in both groups (OR = 0.95, P = 0.018 in ULMS; OR = 0.97, P &lt; 0.001 in EULMS). Lymphovascular invasion and receipt of palliative care were associated with a relatively large decrease in survival in ULMS only (OR = 0.25, P &lt; 0.001; OR = 0.13, P = 0.015 respectively). In the EULMS group, increased likelihood of survival was associated with later years of diagnosis (OR = 1.27; P &lt; 0.001). Larger tumor size (OR = 0.39; P = 0.001), stages II-IV or unstageable disease (OR = 0.56; P &lt; 0.001), positive surgical margins or margins that were unevaluable (OR = 0.87; P = 0.009) and receipt of systemic therapy (OR = 0.85; P = 0.016) were associated with a lower likelihood of survival. Conclusions: Our study reiterated that females with ULMS had worse OS than those with EULMS. this difference can be attributed to certain clinical risk factors, however, it may also be secondary to genetic and epigenetic factors. Surprisingly, the use of palliative care in this study was associated with poorer OS in the EULMS group. Further studies are needed to investigate the genetic map of these diseases and highlight certain potentially targetable prognostic and predictive factors. Study limitations: Our study did not stratify ULMS and EULMS according to the stage to examine if the aforementioned factors would still be relevant in the limited versus metastatic setting.

87-OR: Female Sexual Dysfunction and Type 2 Diabetes: Results from the Diabetes Prevention Program Outcomes Study (DPPOS)

Introduction and Objective: Female sexual dysfunction (FSD) has been reported to be more common in women with T2D. However, detailed risk profiles are lacking. We sought to determine the burden and correlates of FSD in a well characterized cohort of women with Prediabetes (PreD) and T2D enrolled in the DPPOS. Methods: Between 1996-1999, 3,234 adults with PreD were randomized to intensive lifestyle intervention, masked metformin, or placebo in the DPP. In 2001, open-label metformin was continued for those originally randomized to it and modified lifestyle intervention offered to all. In 2016-2017, 1320 of 1464 participants completed the Female Sexual Function Index (FSFI) . 426 women were sexually active and included in the current report. FSD was defined as FSFI≥26.55. A backwards selection multivariable logistic regression model estimated odds of FSD for sociodemographic, clinical, and diabetes-related covariates significant in bivariate analysis. Results: Overall, 185 (43%) women met the criteria for FSD. After adjustment for treatment arm, age and race, depression (defined by the Beck Depression inventory or antidepressant use) and weekly urinary incontinence were both associated with over a two-fold (OR=2.81, 95%CI=1.57, 5.02; and OR=2.14, 95%CI=1.31, 3.51) increase in odds of FSD, respectively. Women who reported hysterectomy were 1.95 times more likely (95%CI=1.07, 3.54) to report FSD. Mean study BMI was slightly protective for FSD (OR=0.93, 95%CI=0.89, 0.96) . Neither DPP treatment group nor any diabetes-related factors were associated with FSD. Conclusions: FSD is highly prevalent in this population of women with PreD and T2D. Our findings suggest that FSD in this cohort is most strongly associated with depression. The lack of association between any measures of diabetes or glycemic control suggests that the sexual response in women is more likely to be affected by psychosocial aspects than by metabolic control or complications of diabetes. Disclosure P.Gupta: Consultant; Inspire . L.Doherty: None. M.Temprosa: None. H.Wessells: None. R.Pop-busui: Advisory Panel; Averitas Pharma, Inc., Boehringer Ingelheim International GmbH, Nevro Corp., Novo Nordisk, Reata Pharmaceuticals, Inc., Regenacy Pharmaceuticals, Inc. K.M.Gadde: Research Support; AstraZeneca, BioKier, Inc. A.H.Owora: None. A.V.Sarma: None. D.Research group: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (UDK048489, UDK048339, UDK048377, UDK048349, UDK048381, UDK048468, UDK048434, UDK048485, UDK048375, UDK048514, UDK048437, UDK048413, UDK048411, UDK048406, UDK048380, UDK048397, UDK048412, UDK048404, UDK048387, UDK048407, UDK048443, and UDK048400)

General selection models: Bernstein duality and minimal ancestral structures

$\Lambda$-Wright--Fisher processes provide a robust framework to describe the type-frequency evolution of an infinite neutral population. We add a polynomial drift to the corresponding stochastic differential equation to incorporate frequency-dependent selection. A decomposition of the drift allows us to approximate the solution of the stochastic differential equation by a sequence of Moran models. The genealogical structure underlying the Moran model leads in the large population limit to a generalisation of the ancestral selection graph of Krone and Neuhauser. Building on this object, we construct a continuous-time Markov chain and relate it to the forward process via a new form of duality, which we call Bernstein duality. We adapt classical methods based on the moment duality to determine the time to absorption and criteria for the accessibility of the boundaries; this extends a recent result by Gonz\'alez Casanova and Span\`o. An intriguing feature of the construction is that the same forward process is compatible with multiple backward models. In this context we introduce suitable notions for minimality among the ancestral processes and characterise the corresponding parameter sets. In this way we recover classic ancestral structures as minimal ones.

Backward Chaining Model for Identifying Learning Difficulties Experienced by Special Needs Children

Identification of learning difficulties is the most important process because it determines the next step in conducting an assessment especially for students with special needs. The first step that needs to be taken in determining the learning difficulties of children with special needs is through identification, where each student has a different character. The purpose of the research is to identfy learning difficulties faced by children with special needs by using backward chaining model and give solutions to learning difficulties. The study's findings indicate that the main factor causing learning challenges for children with special needs is an internal component coming from the students themselves, namely their motivation (75%). The second component is an external factor that has to do with how parents educate their special needs children (25%). To solve the problems, students should have a high motivation in studying and parents should change the way they educate their children. It is suggested that parents should give more attention on students’ academic progress

Diagnostic Process in Rare Diseases: Determinants Associated with Diagnostic Delay

Many people living with rare disease (RD) report a difficult diagnostic process from the symptom onset until they obtain the definitive diagnosis. The aim of this study was thus to ascertain the diagnostic process in RDs, and explore the determinants related with having to wait for more than one year in this process (defined as “diagnostic delay”). We conducted a case–control study, using a purpose-designed form from the Spanish Rare Diseases Patient Registry for data-collection purposes. A descriptive analysis was performed and multivariate backward logistic regression models fitted. Based on data on 1216 patients living with RDs, we identified a series of determinants associated with experiencing diagnostic delay. These included: having to travel to see a specialist other than that usually consulted in the patient’s home province (OR 2.1; 95%CI 1.6–2.9); visiting more than 10 specialists (OR 2.6; 95%CI 1.7–4.0); being diagnosed in a region other than that of the patient’s residence at the date of symptom onset (OR 2.3; 95%CI 1.5–3.6); suffering from a RD of the nervous system (OR 1.4; 95%CI 1.0–1.8). In terms of time taken to see a specialist, waiting more than 6 months to be referred from the first medical visit was the period of time which most contributed to diagnostic delay (PAR 30.2%). In conclusion, this is the first paper to use a collaborative study based on a nationwide registry to address the diagnostic process of patients living with RDs. While the evidence shows that the diagnostic process experienced by these persons is complex, more studies are needed to determine the implications that this has for their lives and those of their families at a social, educational, occupational, psychological, and financial level.

Machine Learning Classifiers to Evaluate Data From Gait Analysis With Depth Cameras in Patients With Parkinson's Disease.

IntroductionThe assessments of the motor symptoms in Parkinson’s disease (PD) are usually limited to clinical rating scales (MDS UPDRS III), and it depends on the clinician’s experience. This study aims to propose a machine learning technique algorithm using the variables from upper and lower limbs, to classify people with PD from healthy people, using data from a portable low-cost device (RGB-D camera). And can be used to support the diagnosis and follow-up of patients in developing countries and remote areas.MethodsWe used Kinect®eMotion system to capture the spatiotemporal gait data from 30 patients with PD and 30 healthy age-matched controls in three walking trials. First, a correlation matrix was made using the variables of upper and lower limbs. After this, we applied a backward feature selection model using R and Python to determine the most relevant variables. Three further analyses were done using variables selected from backward feature selection model (Dataset A), movement disorders specialist (Dataset B), and all the variables from the dataset (Dataset C). We ran seven machine learning models for each model. Dataset was divided 80% for algorithm training and 20% for evaluation. Finally, a causal inference model (CIM) using the DoWhy library was performed on Dataset B due to its accuracy and simplicity.ResultsThe Random Forest model is the most accurate for all three variable Datasets (Dataset A: 81.8%; Dataset B: 83.6%; Dataset C: 84.5%) followed by the support vector machine. The CIM shows a relation between leg variables and the arms swing asymmetry (ASA) and a proportional relationship between ASA and the diagnosis of PD with a robust estimator (1,537).ConclusionsMachine learning techniques based on objective measures using portable low-cost devices (Kinect®eMotion) are useful and accurate to classify patients with Parkinson’s disease. This method can be used to evaluate patients remotely and help clinicians make decisions regarding follow-up and treatment.

Model Pembelajaran Tahfizh Al-Qur’an pada Dayah Ar Raudhah Tahfizh Al-Qur’an Kota Lhokseumawe

This study examines the Tahfizh Al-Qur’an Learning Model. The object of his research is Dayah Ar Raudhah Tahfizh Al-Qur’an Lhokseumawe. Where there is a uniqueness in Dayah Ar Raudhah, namely in the learning model that is carried out which has been proven to have been able to print Al-Qur’an memorization cadres in a shorter time compared to tahfizh Al-Qur’an educational institutions in general. This study aims to analyze the Tahfizh Al-Qur’an learning model at Dayah Ar Raudhah Tahfiz Al-Qur’an Lhokseumawe, Blang mangat District. The type of research used in this research is a qualitative research type. Data collection techniques are carried out through: Observation, interviews and documentation studies. The data analysis technique was carried out by means of data reduction, data presentation, and drawing conclusions. The results showed that, first, the learning model of Dayah Tahfizh Al-Qur’an Ar Raudhah Lhokseumawe used the Ottoman Turkish tahfizh model or known as the backward sequence model with a standard time of memorizing 30 juz for 20. Second, the implementation of the Dayah Tahfizh Al-Qur' learning model. An Ar Raudhah Lhokseumawe was carried out in several stages. The first stage, known as the pre-tahfizh program for 6 months. The second stage, memorizing with the Ottoman model that has been set, is carried out in several steps, (1) Talib reads repeatedly the page to be memorized. (2) Memorizing verse by verse by dividing 1 page into 3 parts, 1 part consisting of 5 lines of verses from the Qur'an, then memorizing 5 lines of verses from the Qur'an from the bottom to the top 5 lines of the beginning of the verse. (3) the memorization of the first round starts from the last page of juz 1 then juz the last page of juz 2, until the last page of juz 30, after that it is continued with the second round starting from the second last page of juz second to the penultimate page of juz the second last page of juz 30 so so on until it's finished. The third stage, the evaluation stage is carried out through 2 stages. The first stage, each talib depositing memorization to his ustadz. The assessment is based on fluency, makaharijul letters and tajwid. Third, constraints in the application of the tahfizh learning model to Dayah Ar Raudhah Tahfizh Al-Qur’an Lhokseumawe. first, feeling difficult when starting to memorize the Qur'an, because memorizing the Qur'an from the last page of each juz. And have to start from the beginning for the talib who had previously memorized the Qur'an. Second, can't confirm or determine how many juz memorized because the system is round.

Bayesian Inversion with Open-Source Codes for Various One-Dimensional Model Problems in Computational Mechanics

The complexity of many problems in computational mechanics calls for reliable programming codes and accurate simulation systems. Typically, simulation responses strongly depend on material and model parameters, where one distinguishes between backward and forward models. Providing reliable information for the material/model parameters, enables us to calibrate the forward model (e.g., a system of PDEs). Markov chain Monte Carlo methods are efficient computational techniques to estimate the posterior density of the parameters. In the present study, we employ Bayesian inversion for several mechanical problems and study its applicability to enhance the model accuracy. Seven different boundary value problems in coupled multi-field (and multi-physics) systems are presented. To provide a comprehensive study, both rate-dependent and rate-independent equations are considered. Moreover, open source codes (https://doi.org/10.5281/zenodo.6451942) are provided, constituting a convenient platform for future developments for, e.g., multi-field coupled problems. The developed package is written in MATLAB and provides useful information about mechanical model problems and the backward Bayesian inversion setting.

Long-range transport of atmospheric microplastics deposited onto glacier in southeast Tibetan Plateau

Micoroplastics (MPs) can be transported through atmospheric circulations, and have caused global attentions due to their potential risk to the environment. In this study, MPs in snowpit samples collected from Demula (DML) glacier in southeast Tibetan Plateau were investigated. The results showed that the average abundance of MPs in snow was 9.55 ± 0.9 items L−1, with dominant shapes of plastic fibers and films. MPs size was dominated by MPs <200 μm, with detected minimum size of 48 μm from the DML glacier. MPs in snowpit indicated seasonal variations, showing relatively higher abundance during the monsoon season than that during the non-monsoon season. The chemical composition of MPs and backward air mass trajectory modeling revealed that MPs in DML snowpit mostly originated from the atmospheric long-range transport, suggesting the glacier in southeast Tibetan Plateau can be a temporal sink of atmospheric MPs. The surface structure of the MPs was rough and adhered to a large amount of mineral dust and metallic particles, revealed that these MPs have undergone severe weathering during transportation and after deposition. Based on the MPs data, multi-year average precipitation, and glacier mass balance of DML glacier, the deposition flux of MPs on DML glacier was estimated to be about 7640 ± 720 to 9550 ± 900 items m−2 yr−1 and the export from melting water was about 5.9 ± 1.3 × 109 to 6.6 ± 1.4 × 109 items yr−1, indicating the glacier may be also an important source of MPs to the downstream ecosystems. These results provided the current status of MPs pollution on the Tibetan Plateau glaciers and new data to the study of MPs in typical cryospheric regions.

A Precise Medical Imaging Approach for Brain MRI Image Classification.

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

PO-686-01 ASSOCIATION OF CLOSED-LOOP STIMULATION VERSUS ACCELEROMETER-BASED RATE ADAPTIVE PACING ON PHYSICAL ACTIVITY AND OUTCOMES IN PATIENTS WITH CARDIOVASCULAR IMPLANTABLE ELECTRONIC DEVICES

Patients with sinus node dysfunction benefit from atrial pacing with rate-adaptive pacing. An alternative to the standard accelerometer (XL), closed-loop stimulation (CLS) relies on intracardiac impedance measurements to allow for a more physiological response to all forms of stimulation. We aimed to evaluate and compare the baseline characteristics, remote monitoring serial data, and long-term outcomes between CLS and XL among three cohorts: 1) dual-chamber pacemaker (DC-PM); 2) dual-chamber implantable cardiac defibrillator (DC-ICD); and 3) cardiac resynchronization therapy (CRT). We used the Biotronik CERTITUDE remote monitoring database, including all Medicare insured patients aged ≥ 65 years with a device programmed to XL or CLS. Remote monitoring data was collected and compared across the cohorts with high arrhythmia burden defined as greater than the median, while chronic conditions and long-term outcomes were obtained via linkage to Medicare claims data. A total of 6,085 patients (mean baseline atrial pacing percentage 63.7%, SD ± 28.8) were included and stratified into respective three respective cohorts: DC-PM (4,346 CLS and 524 XL), DC-ICD (CLS 290 and XL 168), and CRT (CLS 383 and XL 374). Prior stroke and no obesity were predictors of CLS in the DC-PM cohort, while atrial fibrillation was a predictor for XL in the DC-ICD cohort. After multivariable adjustment using a backward selection model, as compared to XL, those with CLS were observed to have 0.5% greater levels of physical activity in the DC-PM cohort (mean 9.3% vs. mean 8.7%, p=0.01), 2.2% lower in the DC-ICD cohort (p<0.001), 1.1% lower in the CRT cohort (p<0.001). There was a significantly lower risk of atrial arrhythmias (OR 0.56, 95% CI 0.39 - 0.81, p = 0.002) and lower PVC burden (OR 0.66, 95% CI 0.47 - 0.93, p=0.02) with CLS as compared to XL in the DC-PM, while no differences were observed in the other cohorts. There were no significant differences between CLS and XL in any cohort with respect to 1-year outcomes linked to Medicare including all-cause hospitalization, cardiac hospitalization, and stroke. As compared to XL, those programmed to CLS had modestly higher physical activity levels and lower risk of high atrial arrhythmias and PVC burden in those with a DC-PM as compared to XL.