Prevalence of Hair Loss among Men and its Association with Smoking and Stress: A Case Study in the City of Lahore, Pakistan

Aim: To explore the prevalence of hair loss and its relation to men’s stress and smoking, Methodology: A cross sectional study attempts to explore the prevalence of baldness among the male residents of Lahore. For this purpose, a randomized sample of the male population was taken into consideration. The socio-demographic details, along with the smoking status of the sample were determined. Moreover, the body mass index (BMI) was also determined by referring to a prefixed formula. Baldness and stress levels were also quantified by taking into account the Norwood Hamilton categorical scale, and the perceived stress scale, respectively. A total of 250 male members were invited to participate in the research. Results: The study results showed that 51.2%, 41.2% and 7.6%of male experienced moderate, high and low level of stress respectively. When taking the Norwood Hamilton categorical scale into account, the results revealed that most prevalent type of baldness was Type II, which is the fronto-temporal hairline recession, with 19.2% of the individuals experiencing it. Age was significantly associated with baldness. Similarly a higher frequency of baldness was recorded in those men who lived in a nuclear family arrangement. Conclusion: No association was found between the type of baldness, stress level and smoking status. Keyword: Alopecia, Baldness, Norwood Hamilton categorical scale, Perceived stress scale, smoking

Early constipation predicts faster dementia onset in Parkinson’s disease

Constipation is a common but not a universal feature in early PD, suggesting that gut involvement is heterogeneous and may be part of a distinct PD subtype with prognostic implications. We analysed data from the Parkinson’s Incidence Cohorts Collaboration, composed of incident community-based cohorts of PD patients assessed longitudinally over 8 years. Constipation was assessed with the MDS-UPDRS constipation item or a comparable categorical scale. Primary PD outcomes of interest were dementia, postural instability and death. PD patients were stratified according to constipation severity at diagnosis: none (n = 313, 67.3%), minor (n = 97, 20.9%) and major (n = 55, 11.8%). Clinical progression to all three outcomes was more rapid in those with more severe constipation at baseline (Kaplan–Meier survival analysis). Cox regression analysis, adjusting for relevant confounders, confirmed a significant relationship between constipation severity and progression to dementia, but not postural instability or death. Early constipation may predict an accelerated progression of neurodegenerative pathology.

Gender, Anxiety, and Legitimation of Violence in Adolescents Facing Simulated Physical Aggression at School

We analyzed gender and anxiety differences in middle school students facing a physical peer aggression situation. The participants were 1147 adolescents aged between 12 and 18 years (male: n = 479; female: n = 668) who watched a 12 s animation representing the situation and filled out a questionnaire to analyze the legitimation of violent behaviors and anxiety levels. We registered their decisions to solve the situation using a categorical scale that included assertive, avoidant, aggressive, submissive, and supportive behaviors. Gender was not associated with the adolescent’s behaviors in facing a simulated peer aggression situation. However, male teenagers tended to perceive adults as sanctioners and neutrals; those who used the diffusion of responsibility and dehumanization to justify their behavior also showed a higher state of anxiety. Female teenagers who expected legitimation from their peers, presented higher anxiety as well. Educational interventions may use these results, helping adolescents to understand that their acts have substantial implications in the lives of others. It is essential to develop group interventions that modify how adolescents manage their conflicts and change gender stereotypes that significantly impact health. We highlight the need for linking families in educational programs facing the challenges of transforming the legitimization of violence in parental practices.

Mapping the common and distinct neural correlates of visual, rule and motor conflict

Flexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

Approach of scaling the level of government intervention in the financial market

In the field of the economics’ regulation researchers so far have built the conceptual framework showing how the deadweight loss of market failures decrease and costs of the government intervention in-crease with the increased level of the government intervention. In order to quantify relationships between the level of intervention, intervention costs and the deadweight loss with econometric models it is im-portant to understand how to apply coordinates for the data points to be included in the modelling. The main goal of the research presented in this paper is to find the unit measure for the asis of the independentvariable, i.e. to shape the categorical scale corresponding to the level of intervention.

MODEL DAN PELUANG TERJADINYA HUJAN BERDASARKAN KELEMBABAN MENGGUNAKAN REGRESI LOGISTIK BINER DI KABUPATEN MANOKWARI

Manokwari Regrency has a tropical climate. This results in significant rainfall. One factor that stimulates rain is humidity. By using binary logistic regression, the model an chance of rainfall based on humidity can be determined. Logistic regression analysis is used to determine the relationship between categorical scale response variables and numeric or categoric scale explanatory variables. If response variable used is nominal scale with two possoble value (0 and 1), then it is called binary logistic regression. Estimation of the model is done by logit transformation. The model produce in this study is g(x) = -23.443 + 0.289 humidity. The accuracy of the model is 70.4 percent and the probability of rain for humidity lowering one unit will be reduced to 0.74.

Loglinear Model Formation using Hierachial Backward Method

The loglinear model is a special case of a general linear model for poissondistributed data. The loglinear model is also a number of models in statistics that are used todetermine dependencies between several variables on a categorical scale. The number ofvariables discussed in this study were three variables. After the variables are investigated,the formation of the loglinear model becomes important because not all the modelinteraction factors that exist in the complete model become significant in the resultingmodel. The formation of the loglinear model in this study uses the Backward Hierarchicalmethod. This research makes loglinear modeling to get the model using the HierarchicalBackward method to choose a good method in making models with existing examples.From the challenging examples that have been done, it is known that the HierarchicalReverse method can model the third iteration or scroll. Then, also use better assessmentmethods about faster workmanship and computer-sponsored assessments that are used moreefficiently through compatibility testing for each model made

Abstract TP458: High Accuracy of Predictive Models for SAH Using Different Machine Learning Approaches

Background: Patients suffering from subarachnoid hemorrhage (SAH) have poor long-term outcomes. There are predictive models for ischemic and hemorrhagic stroke. However, there is paucity of models for SAH. Machine learning concepts were applied to build multi-stage Neural Networks (NN), Support Vector Machines (SVM) and Keras/Tensor Flow models to predict SAH outcomes. Methods: A database of ~800 aneurysmal SAH patients from Kasturba Medical College was utilized. Baseline variables of World Federation of Neurosurgeons 5-point scale (WFNS 1-5), age, gender, and presence/absence of hypertension and diabetes were considered in Stage 1. Stage 2 included all Stage 1 variables along with presence/absence of radiologic signs vasospasm and ischemia. Stage 3 includes earlier 2 stages and discharge Glasgow Outcome Scale (GOS 1-5). GOS at 3 months was predicted using 2-layer NN/SVM/Keras-TensorFlow models on the five point categorical scale as well as dichotomized to dead/alive and favorable (GOS 4-5) or unfavorable (GOS 1-3). Prediction accuracy of models was compared to the recorded GOS. Results: Prediction accuracy shown as percentages (See Table) for all three stages was similar for SVM, NN and Keras/TensorFlow models. Accuracy was remarkably higher with dichotomization compared to the complete five point GOS categorical scale. Conclusions: SVM, NN, and Keras-TensorFlow based machine learning models can be used to predict SAH outcomes to a high degree of accuracy. These powerful predictive models can be used to prognosticate and select patients into trials.

Pemodelan Faktor-Faktor yang Mempengaruhi Jenis Kanker Payudara Menggunakan Regresi Logistik Biner (Kasus : Pasien Penderita Kanker Payudara di RSUP Dr. Wahidin Sudirohusodo tahun 2016).

Abstrak Regresi logistik adalah suatu metode analisis statistik yang diterapkan untuk memodelkan variabel dependen yang memiliki dua kategori atau lebih dengan satu atau lebih variabel independen. Regresi Logistik biner merupakan suatu analisis statistika yang digunakan untuk menganalisis hubungan antara satu atau lebih peubah bebas dengan peubah respon yang bersifat biner atau dichotomous. Peubah bebas pada regresi logistik dapat berupa peubah skala kategorik maupun peubah yang skala kontinu sedangkan peubah respon berupa peubah berskala kategorik. Regresi Logistik Biner dapat diterapkan pada kasus kesehatan, khususnya pada penelitian ini yaitu mengenai kanker payudara. Sesuai uraian diatas maka penulis bermaksud untuk mengkaji dan melakukan penelitian tentang Pemodelan Faktor-Faktor yang Mempengaruhi Jenis Kanker Payudara Menggunakan Regresi Logistik Biner (Kasus : Pasien Penderita Kanker Payudara di Rumah Sakit Umum Pusat Dr. Wahidin Sudirohusodo). Dari hasil analisis didapatkan bahwa peubah penjelas yang berpengaruh nyata terhadap jenis keganasan kanker terhadap pasien penderita kanker payudara adalah peubah Kemoterapi (X2) dan peubah Metastase (X5) yang masing-masing memiliki nilai odds rasio sebesar 0,17 dan 6,16. Kata kunci : Kanker Payudara, Regresi Logistik, Regresi Logistik Biner. Abstract Logistic regression is a method of statistical analysis that is applied to model the dependent variable which has two or more categories with one or more independent variables. Binary Logistic Regression is a statistical analysis that is used to analyze the relationship between one or more independent variables with variable binary or dichotomous responses. The free variables in logistic regression can be either categorical scale or continuous scale variables while the response variables are categorical scale variables. Binary Logistic Regression can be applied to health cases, especially in this study, namely breast cancer. In accordance with the description above, the author intends to study and conduct research on Modeling Factors Affecting Types of Breast Cancer Using Binary Logistic Regression (Case: Patients with Breast Cancer Patients at Dr. Wahidin Sudirohusodo Central General Hospital). From the results of the analysis it was found that the explanatory variables that significantly affected the type of cancer malignancy in patients with breast cancer were Chemotherapy variables (X2) and Metastase variables (X5), each of which had odds ratio values of 0.17 and 6.16. Keywords: Breast Cancer, Logistic Regression, Binary Logistic Regression.

Effectiveness Of Prenatal Yoga On The Level Of Anxiety And Long-Time Labor On Maternity Primiparous

The process of delivering on each mother must be different. Apparently, in addition to be the cause of the clinical, psychological atmosphere of the mother who does not support also contribute to complicate the process ofchildbirth. As a mother in a state of anxiety, excessive worry and fear, until eventually lead to stress. The purpose of the research was to determine the effectiveness of prenatal yoga on anxiety level and the second stage of labor longer on maternity primiparous. Methods research with quantitative approachquasi experimental with control grou pdesign. The total sample of 20 respondents, each group of 10 respondents. Bivariate analysis is done in two variables: the old stage II with categorical scale so usingtest. chi-square Jikasyaratchi-squareis not met using the Fisher exactprovided that if the value sig(p)0.05 then say no relationship signifikan.dan anxiety level has a categorical scale, if it does not qualifytest chi-square then used thealternative test Kolmogorov- Smirnov. The results of the study was prenatal yoga is proven effective against anxiety levels in maternal primipara withvalue significancy 0,003and prenatal yoga are effective against the second stage of labor longer on maternity primipara withvalue of significancy 0.003. Prenatal yoga is proven effective against anxiety levels in maternal and prenatal yoga primiparous effective against second stage of labor longer in primiparous birthmothers.