Application of the Logistic Model to the COVID-19 Pandemic in South Africa and the United States: Correlations and Predictions

We apply the simple logistic model to the four waves of COVID-19 taking place in South Africa over the period 2020~January~1 through 2022 January 11. We show that this model provides an excellent fit to the time history of three of the four waves. We then derive a theoretical correlation between the growth rate of each wave and its duration, and demonstrate that it is well obeyed by the South Africa data. We then turn to the data for the United States. As shown by Roberts (2020a, 2020b), the basic logistic model provides only a marginal fit to the early data. Here we break the data into six "waves," and treat each one separately. For four of the six the logistic model is useful, and we present full results. We then ask if these data provide a way to predict the length of the ongoing Omicron wave in the US (commonly called "wave 4," but the sixth wave as we have broken the data up). Comparison of these data to those from South Arica, and internal comparison of the US data, suggests that this last wave will die out by about 2022-January-20.

Analysis of historical selection in winter wheat

Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased towards conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.

Competition and forecasts for Nobel prize awards

The evolution of Nobel prize awards is studied as a learning/growing process. A simple logistic function describes the data well and accounts for the competition, be it between individuals or between nations. The American niche appears to be 64% exhausted by 1987, implying a diminishing expected rate of laureates in the future. Europeans have been losing ground continuously from the beginning while the remaining world has recently received more awards. Projections to the year 2000 and beyond are given. Correlations with age support the Darwinian nature of the competition for Nobel prizes. Awards to women show peaks coincidental with outbursts of feminism.

Diagnostic Dilemmas: A Multi-Institutional Retrospective Analysis of Adrenal Incidentaloma Pathology Based on Radiographic Size

Introduction/Background Adrenal incidentalomas (AIs) are masses >1 cm found incidentally during radiographic imaging. They are present in up to 4.4% of patients undergoing CT scan, and incidence is increasing with usage and sensitivity of cross-sectional imaging. Most result in diagnosis of adrenal cortical adenoma, questioning guidelines recommending removal of all AIs with negative functional workup. This retrospective study analyzes histological outcome based on size of non-functional adrenal masses. Material and Methods 10 years of data was analyzed from two academic institutions. Exclusion criteria included patients with positive functional workups, those who underwent adrenalectomy during nephrectomy, <18 years, and incomplete records. AI radiologic and histologic size, histologic outcome, laterality, imaging modality, gender, and age were collected. T-test was used for comparison of continuous variables, and the two-sided Fisher’s exact or chi-square test were used to determine differences for categorical variables. Univariate analysis of each independent variable was performed using simple logistic regression. Results 73 adrenalectomies met the above inclusion criteria. 60 were detected on CT scan, 12 on MRI, and one on ultrasound. Eight of 73 cases resulted in malignant pathology, 3 of which were adrenocortical carcinoma (ACC). Each ACC measured >6 cm, with mean radiologic and pathologic sizes of 11.2 cm and 11.3 cm. Both radiologic and pathologic size were significant predictors of malignancy (p=0.008 and 0.011). Conclusions Our results question the generally-accepted 4 cm cutoff for excision of metabolically-silent AIs. They suggest a 6 cm threshold would suffice to avoid removal of benign lesions while maintaining sensitivity for ACC.

Delirium: Prevalence and outcome in the very old in 27 medical departments during a one-year prospective study

Objective The prevalence and effects of delirium in very old individuals aged ≥80 years have not yet been systematically evaluated. Therefore, this large single-center study of the one-year prevalence of delirium in 3,076 patients in 27 medical departments of the University Hospital of Zurich was conducted. Methods Patient scores on the Delirium Observation Screening scale, Intensive Care Delirium Screening Checklist, Diagnostic and Statistical Manual, 5th edition, and electronic Patient Assessment–Acute Care (nursing tool) resulted in the inclusion of 3,076 individuals in 27 departments. The prevalence rates were determined by simple logistic regressions, odds ratios (ORs), and confidence intervals. Results Of the 3,076 patients, 1,285 (41.8%) developed delirium. The prevalence rates in the 27 departments ranged from 15% in rheumatology (OR = 0.30) to 73% in intensive care (OR = 5.25). Delirious patients were more likely to have been admitted from long-term care facilities (OR = 2.26) or because of emergencies (OR = 2.24). The length of their hospital stay was twice as long as that for other patients. Some died before discharge (OR = 24.88), and others were discharged to nursing homes (OR = 2.96) or assisted living facilities (OR = 2.2). Conclusion This is the largest study to date regarding the prevalence of delirium in patients aged ≥80 years and the medical characteristics of these patients. Almost two out of five patients developed delirium, with a high risk of loss of independence and mortality.

Factors Affecting Pharmacy Students’ Decision to Study in Pharmacy Colleges in Saudi Arabia: A Cross-Sectional Questionnaire-Based Analysis

(1) Background: Many factors may play a role in deciding to opt for pharmacy as a major. However, no previous studies have been conducted in Saudi Arabia to explore these factors. This study aims to identify the potential factors that prompted students to join the pharmacy program. (2) Methods: A cross-sectional questionnaire was distributed among undergraduate pharmacy students in Saudi Arabia, addressing areas such as reasons that encourage them to choose pharmacy as a major, and students’ socio-demographic characteristics. Descriptive statistics were used to describe the study variables, and a simple logistic regression analysis was performed to identify the potential factors. (3) Results: A total of 491 students completed the questionnaire. Around 40% of them had chosen to study pharmacy as their first choice. Only gender, current GPA, and reasons related to the pharmacy field were found to have a statistically significant association with students selecting pharmacy as their first choice. (4) Conclusions: This study shows that pharmacy students have a future-oriented outlook and selected pharmacy as their first choice because it will develop them professionally, financially, and intellectually. Educating high school students about the characteristic of pharmacy would help attract more talented students to the pharmacy carrier.

Arrhythmia Detection using Deep Belief Network Extracted Features from ECG Signals

Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due to conduction failures of the electrical signals in the cardiac muscle. In recent years, research galore has been done towards accurate categorization of heartbeats and electrocardiogram (ECG)-based heartbeat processing. Accurate categorization of different heartbeats is an important step for diagnosis of arrhythmia. This paper primarily focuses on effective feature extraction of the ECG signals for model performance enhancement using an unsupervised Deep Belief Network (DBN) pipelined onto a simple Logistic Regression (LR) classifier. We compare and evaluate the results of data feature enrichment against plain, non-enriched data based on the metrics of precision, recall, specificity, and F1-score and report the extent of increase in performance. Also, we compare the performance of the DBN-LR pipeline with a 1D convolution technique and find that the DBN-LR algorithm achieves a 5% and 10% increase in accuracy when compared to 1D convolution and no feature extraction using DBN respectively.

Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals

Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due to conduction failures of the electrical signals in the cardiac muscle. In recent years, research galore has been done towards accurate categorization of heartbeats and electrocardiogram (ECG)-based heartbeat processing. Accurate categorization of different heartbeats is an important step for diagnosis of arrhythmia. This paper primarily focuses on effective feature extraction of the ECG signals for model performance enhancement using an unsupervised Deep Belief Network (DBN) pipelined onto a simple Logistic Regression (LR) classifier. We compare and evaluate the results of data feature enrichment against plain, non-enriched data based on the metrics of precision, recall, specificity, and F1-score and report the extent of increase in performance. Also, we compare the performance of the DBN-LR pipeline with a 1D convolution technique and find that the DBN-LR algorithm achieves a 5% and 10% increase in accuracy when compared to 1D convolution and no feature extraction using DBN respectively.

A Three-Dimensional Computed Tomography Radiographic Study -- Can We Predict Glenoid Width Based on Glenoid Height? (216)

Objectives: The purpose of this study was to investigate the relationship between glenoid width and other morphologic parameters using three-dimensional (3D) computed tomography (CT) images of native shoulders in hopes of generating a formula to predict glenoid width which will have utility in planning boney shoulder stabilization surgeries. Methods: 102 glenoid images were obtained for patients who underwent contralateral shoulder glenoid reconstruction for anterior shoulder instability between 2012 and 2020. Demographic data was obtained including age, gender and BMI. The subjects were excluded if they had a prior history of ipsilateral shoulder instability, shoulder fractures, or bone tumors. The following glenoid parameters were measured: width (W), height (H), ratio (W/H), anteroposterior (AP) depth, superior-inferior (SI) depth and version. The shape of the glenoid was also classified into pear, inverted comma or oval. Data was analyzed based on gender and age. Simple logistic regression, Kruskal Wallis Rank tests and Fisher Exact tests were performed. Results: There were 71 male and 25 females with a mean age of 39.74 ± 17.88 years. Pear morphotype accounted for most glenoid shapes (46%). The glenoid width was strongly correlated with the height (coefficient = 0.78) and a regression model equation was obtained: W (mm) = 3.4 + 0.68*H (mm). There was also strong correlation with gender (P<0.0001), age (P=0.0384), BMI (P<0.0001), glenoid shape (P=0.0036), height (P=0.0019), AP and SI depths (P<0.0001). Male gender was associated with higher measurement values for all parameters. Older age was significantly correlated with higher glenoid width values in both male and females group. (P=0.0015 and P=0.0104, respectively). Conclusions: The native glenoid width can be easily estimated using solely the glenoid height. This is particularly important for surgical decision making when facing anterior or posterior glenoid defects in patients with shoulder instability.

Faktor Risiko Dominan Mempengaruhi Kurang Energi Kronis (KEK) pada Ibu Hamil di Indonesia Tahun 2018

Chronic Energy Deficiency (CED) is one of the main problems that often occurs among pregnant women. This study aimed to describe the Dominant Risk Factors for CED pregnant women in Indonesia. This study used data from the 2018 Basic Health Research on all pregnant women in Indonesia. Data analysis used with simple logistic regression. The results of multivariate analysis showed that several factors had an effect on the incidence of CED, but the most significant factor was tuberculosis disease (p= 0.002; OR 6.770; 95% CI 1.964-23.341). It was concluded that pregnant women with tuberculosis had a 6.7 times increase risk for developing CED compared to those without tuberculosis. This variable was the most dominant variable related to CED in pregnant women in Indonesia in 2018. Keywords : risk factos, Chronic Energy Deficiency (CED), pregnant women Abstrak Kurang Energi Kronis (KEK) merupakan salah satu masalah utama yang masih sering terjadi pada Ibu hamil. Penelitian ini bertujuan untuk mengetahui gambaran faktor risiko dominan mempengaruhi KEK pada ibu hamil di Indonesia. Penelitian ini menggunakan data Riset Kesehatan Dasar Tahun 2018 pada seluruh ibu hamil di Indonesia. Analisis data yang digunakan dengan Simple Logistic Regression. Hasil analisis multivariate menunjukkan beberapa faktor berpengaruh terhadap kejadian KEK, akan tetapi faktor yang paling signifikan adalah penyakit infeksi tuberkulosis (p=0,002; OR 6,770; 95% CI 1,964-23,341). Disimpulkan ibu hamil dengan tuberculosis (TB) berisiko menjadi KEK sebesar 6,7 kali dibandingkan dengan tanpa tuberkulosis. Variabel ini meupakan variabel yang paling dominan berhubungan dengan KEK pada ibu hamil di Indonesia tahun 2018. Kata kunci: Faktor risiko, Kurang Energi Kronis (KEK), ibu hamil