Nevin Manimala

Eur Urol Open Sci. 2021 Sep 22;33:11-18. doi: 10.1016/j.euros.2021.08.007. eCollection 2021 Nov.

ABSTRACT

BACKGROUND: Considering the relatively high 5-yr survival rate (76.9%) for bladder cancer (BC), its overall prevalence will probably continue to increase. Therefore, it is important to understand the effects of BC diagnosis and management, including psychological sequelae.

OBJECTIVE: To determine the prevalence of depression among elderly patients with BC and identify patient characteristics associated with depression.

DESIGN SETTING AND PARTICIPANTS: Survey responses from a population-based sample of 5787 patients older than 65 yr with a history of BC were retrieved from the Surveillance, Epidemiology and End Results-Medicare Health Outcomes Survey registry, spanning 1999-2014.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome measured is the prevalence of a positive depression screen. Cancer characteristics and demographic, socioeconomic, health-related, and activities of daily living (ADL)-related data were reviewed. Univariate analysis was conducted to identify correlation between a positive depression screen and patient characteristics. Multivariate analysis was performed to identify independent predictors of depression.

RESULTS AND LIMITATIONS: The prevalence of a positive depression screen was 14.0%. Poor general health (p < 0.001), impairment of ADL (p < 0.001), greater number of comorbidities (p < 0.001), and income <$30 000 (p < 0.001) were identified as correlates of depression. Univariate analysis found no association between a positive depression screen and time since the initial cancer diagnosis (p = 0.858) or cancer stage (p = 0.90). Multivariate analysis showed higher levels of education (p = 0.0097), increasing age (p = 0.0027), and marriage (p < 0.0001) were protective against the development of depression. Limitations include the lack of consideration of treatment outcomes and whether patients have active disease or only a history of cancer.

CONCLUSIONS: Depression affects a substantial percentage (14%) of elderly patients with BC. Poor general health and impaired ability to complete ADL were the greatest risk factors for depression. Acknowledgment of sociodemographic factors may improve awareness of depression in patients with BC and a potential need for psychosocial support.

PATIENT SUMMARY: Depression affects a significant proportion of patients with bladder cancer. Social and demographic factors influence a patient’s risk of depression. Acknowledgment of these factors may improve the detection of depression and a possible need for intervention.

PMID:34723216 | PMC:PMC8546926 | DOI:10.1016/j.euros.2021.08.007

Eur Urol Open Sci. 2021 Sep 20;33:1-10. doi: 10.1016/j.euros.2021.08.005. eCollection 2021 Nov.

ABSTRACT

BACKGROUND: Radical cystectomy (RC) is associated with high morbidity.

OBJECTIVE: To evaluate healthcare and surgical factors associated with high-quality RC surgery.

DESIGN SETTING AND PARTICIPANTS: Patients within the prospective British Association of Urological Surgeons (BAUS) registry between 2014 and 2017 were included in this study.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: High-quality surgery was defined using pathological (absence of positive surgical margins and a minimum of a level I lymph node dissection template with a minimum yield of ten or more lymph nodes), recovery (length of stay ≤10 d), and technical (intraoperative blood loss <500 ml for open and <300 ml for minimally invasive RC) variables. A multilevel hierarchical mixed-effect logistic regression model was utilised to determine the factors associated with the receipt of high-quality surgery and index admission mortality.

RESULTS AND LIMITATIONS: A total of 4654 patients with a median age of 70.0 yr underwent RC by 152 surgeons at 78 UK hospitals. The median surgeon and hospital operating volumes were 23.0 and 47.0 cases, respectively. A total of 914 patients (19.6%) received high-quality surgery. The minimum annual surgeon volume and hospital volume of ≥20 RCs/surgeon/yr and ≥68 RCs/hospital/yr, respectively, were the thresholds determined to achieve better rates of high-quality RC. The mixed-effect logistic regression model found that recent surgery (odds ratio [OR]: 1.22, 95% confidence interval [CI]: 1.11-1.34, p < 0.001), laparoscopic/robotic RC (OR: 1.85, 95% CI: 1.45-2.37, p < 0.001), and higher annual surgeon operating volume (23.1-33.0 cases [OR: 1.54, 95% CI: 1.16-2.05, p = 0.003]; ≥33.1 cases [OR: 1.64, 95% CI: 1.18-2.29, p = 0.003]) were independently associated with high-quality surgery. High-quality surgery was an independent predictor of lower index admission mortality (OR: 0.38, 95% CI: 0.16-0.87, p = 0.021).

CONCLUSIONS: We report that annual surgeon operating volume and use of minimally invasive RC were predictors of high-quality surgery. Patients receiving high-quality surgery were independently associated with lower index admission mortality. Our results support the role of centralisation of complex oncology and implementation of a quality assurance programme to improve the delivery of care.

PATIENT SUMMARY: In this registry study of patients treated with surgical excision of the urinary bladder for bladder cancer, we report that patients treated by a surgeon with a higher annual operative volume and a minimally invasive approach were associated with the receipt of high-quality surgery. Patients treated with high-quality surgery were more likely to be discharged alive following surgery.

PMID:34723215 | PMC:PMC8546928 | DOI:10.1016/j.euros.2021.08.005

SN Comput Sci. 2021;2(4):330. doi: 10.1007/s42979-021-00723-4. Epub 2021 Jun 7.

ABSTRACT

Machine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the linguistic difference and variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography’s influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts using cognate information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction.

PMID:34723204 | PMC:PMC8550410 | DOI:10.1007/s42979-021-00723-4

SN Soc Sci. 2021;1(10):257. doi: 10.1007/s43545-021-00264-w. Epub 2021 Oct 6.

ABSTRACT

Research in several Latin American countries points to violence, loss of traditional territories, and seeking education, health, and wage labor as key variables in triggering rural-urban migration among Indigenous people. This study presents an analysis of the migration patterns of Indigenous people in Brazil, compared to non-indigenous people, based on data from the most recent national census, conducted in 2010. Migration characteristics related to lifetime migration and recent migration were investigated by means of descriptive and multivariable logistic regression analyses. The findings pointed to complex mobility scenarios according to migrants’ Indigenous status and geographical regions of origin and destination. Indigenous people living in urban areas presented high levels of mobility (approximately 50% lived in different municipalities from those where they were born), which were more pronounced than those of non-Indigenous people. Indigenous people living in rural areas presented the lowest levels of migration (approximately 90% residing in their municipality of birth). Statistical modeling confirmed the patterns observed in descriptive analysis, highlighting the marked mobility of Indigenous subjects in urban areas. We emphasize the limitations of using census data for characterizing Indigenous mobility profiles, although no other nationally representative data are available. The finding that the Indigenous population living in urban areas presents rates of migration higher than their non-Indigenous counterparts is particularly important for the planning and implementation of a broad range of public policies aimed at ethnic minorities in the country, including health, education, and housing initiatives.

PMID:34723200 | PMC:PMC8549969 | DOI:10.1007/s43545-021-00264-w

Front Sports Act Living. 2021 Oct 14;3:722305. doi: 10.3389/fspor.2021.722305. eCollection 2021.

ABSTRACT

The purpose of this study was to investigate the relationship between volume regulatory biomarkers and the estrogen to progesterone ratio (E:P) prior to and following varying methods and degrees of dehydration. Ten women (20 ± 1 year, 56.98 ± 7.25 kg, 164 ± 6 cm, 39.59 ± 2.96 mL•kg•min^-1) completed four intermittent exercise trials (1.5 h, 33.8 ± 1.3°C, 49.5 ± 4.3% relative humidity). Testing took place in two hydration conditions, dehydrated via 24-h fluid restriction (Dehy, USG > 1.020) and euhydrated (Euhy, USG ≤ 1.020), and in two phases of the menstrual cycle, the late follicular phase (days 10-13) and midluteal phase (days 18-22). Change in body mass (%BMΔ), serum copeptin concentration, and plasma osmolality (P_osm) were assessed before and after both dehydration stimuli (24-h fluid restriction and exercise heat stress). Serum estrogen and progesterone were analyzed pre-exercise only. Estrogen concentration did not differ between phases or hydration conditions. Progesterone was significantly elevated in luteal compared to follicular in both hydration conditions (Dehy-follicular: 1.156 ± 0.31, luteal: 5.190 ± 1.56 ng•mL^-1, P < 0.05; Euhy-follicular: 0.915 ± 0.18, luteal: 4.498 ± 1.38 ng·mL^-1, P < 0.05). As expected, E:P was significantly greater in the follicular phase compared to luteal in both hydration conditions (Dehy-F:138.94 ± 89.59, L: 64.22 ± 84.55, P < 0.01; Euhy-F:158.13 ± 70.15, L: 50.98 ± 39.69, P < 0.01, [all •10³]). Copeptin concentration was increased following 24-h fluid restriction and exercise heat stress (mean change: 18 ± 9.4, P < 0.01). We observed a possible relationship of lower E:P and higher copeptin concentration following 24-h fluid restriction (r = -0.35, P = 0.054). While these results did not reach the level of statistical significance, these data suggest that the differing E:P ratio may alter fluid volume regulation during low levels of dehydration but have no apparent impact after dehydrating exercise in the heat.

PMID:34723178 | PMC:PMC8551666 | DOI:10.3389/fspor.2021.722305

Front Artif Intell. 2021 Oct 15;4:757864. doi: 10.3389/frai.2021.757864. eCollection 2021.

ABSTRACT

This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly data from 1976 to 2020, we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series analysis. We explore the inclusion of predictors such as the yield curve, its latent factors, and a set of macroeconomic variables in order to increase the level of forecasting accuracy. The predictions result to be improved only when considering long forecast horizons. The use of machine learning algorithm provides additional guidance for data-driven decision making.

PMID:34723174 | PMC:PMC8554645 | DOI:10.3389/frai.2021.757864

Front Artif Intell. 2021 Oct 14;4:681915. doi: 10.3389/frai.2021.681915. eCollection 2021.

ABSTRACT

Algorithmic scoring methods are widely used in the finance industry for several decades in order to prevent risk and to automate and optimize decisions. Regulatory requirements as given by the Basel Committee on Banking Supervision (BCBS) or the EU data protection regulations have led to an increasing interest and research activity on understanding black box machine learning models by means of explainable machine learning. Even though this is a step into a right direction, such methods are not able to guarantee for a fair scoring as machine learning models are not necessarily unbiased and may discriminate with respect to certain subpopulations such as a particular race, gender, or sexual orientation-even if the variable itself is not used for modeling. This is also true for white box methods like logistic regression. In this study, a framework is presented that allows analyzing and developing models with regard to fairness. The proposed methodology is based on techniques of causal inference and some of the methods can be linked to methods from explainable machine learning. A definition of counterfactual fairness is given together with an algorithm that results in a fair scoring model. The concepts are illustrated by means of a transparent simulation and a popular real-world example, the German Credit data using traditional scorecard models based on logistic regression and weight of evidence variable pre-transform. In contrast to previous studies in the field for our study, a corrected version of the data is presented and used. With the help of the simulation, the trade-off between fairness and predictive accuracy is analyzed. The results indicate that it is possible to remove unfairness without a strong performance decrease unless the correlation of the discriminative attributes on the other predictor variables in the model is not too strong. In addition, the challenge in explaining the resulting scoring model and the associated fairness implications to users is discussed.

PMID:34723172 | PMC:PMC8552888 | DOI:10.3389/frai.2021.681915

EClinicalMedicine. 2021 Nov;41:101172. doi: 10.1016/j.eclinm.2021.101172. Epub 2021 Oct 24.

ABSTRACT

BACKGROUND: By the end of July 2021 Zimbabwe, has reported over 100,000 SARS-CoV-2 infections. The true number of SARS-CoV-2 infections is likely to be much higher. We conducted a seroprevalence survey to estimate the prevalence of past SARS-CoV-2 in three high-density communities in Harare, Zimbabwe before and after the second wave of SARS-CoV-2.

METHODS: Between November 2020 and April 2021 we conducted a cross-sectional study of randomly selected households in three high-density communities (Budiriro, Highfield and Mbare) in Harare. Consenting participants answered a questionnaire and a dried blood spot sample was taken. Samples were tested for anti-SARS-CoV-2 nucleocapsid antibodies using the Roche e801 platform.

FINDINGS: A total of 2340 individuals participated in the study. SARS-CoV-2 antibody results were available for 70·1% (620/885) and 73·1% (1530/2093) of eligible participants in 2020 and 2021. The median age was 22 (IQR 10-37) years and 978 (45·5%) were men. SARS-CoV-2 seroprevalence was 19·0% (95% CI 15·1-23·5%) in 2020 and 53·0% (95% CI 49·6-56·4) in 2021. The prevalence ratio was 2·47 (95% CI 1·94-3·15) comparing 2020 with 2021 after adjusting for age, sex, and community. Almost half of all participants who tested positive reported no symptoms in the preceding six months.

INTERPRETATION: Following the second wave, one in two people had been infected with SARS-CoV-2 suggesting high levels of community transmission. Our results suggest that 184,800 (172,900-196,700) SARS-CoV-2 infections occurred in these three communities alone, greatly exceeding the reported number of cases for the whole city. Further seroprevalence surveys are needed to understand transmission during the current third wave despite high prevalence of past infections.

FUNDING: GCRF, Government of Canada, Wellcome Trust, Bavarian State Ministry of Sciences, Research, and the Arts.

PMID:34723165 | PMC:PMC8542175 | DOI:10.1016/j.eclinm.2021.101172

Complex Analysis Synerg. 2021;7(3-4):25. doi: 10.1007/s40627-021-00083-y. Epub 2021 Jul 15.

ABSTRACT

We consider compact Leviflat homogeneous Cauchy-Riemann (CR) manifolds. In this setting, the Levi-foliation exists and we show that all its leaves are homogeneous and biholomorphic. We analyze separately the structure of orbits in complex projective spaces and parallelizable homogeneous CR-manifolds in our context and then combine the projective and parallelizable cases. In codimensions one and two, we also give a classification.

PMID:34723129 | PMC:PMC8550170 | DOI:10.1007/s40627-021-00083-y

J Nutr Sci Vitaminol (Tokyo). 2021;67(5):266-272. doi: 10.3177/jnsv.67.266.

ABSTRACT

Low 25 hydroxyvitamin D (25(OH)D) levels are closely associated with the risk of cardiovascular disease. Vitamin D deficiency is more common in patients with type 2 diabetes mellitus than in the general population. In addition, vitamin D status is lower in patients with the metabolic syndrome than in those without the syndrome. Therefore, we examined the association between lipid profiles and 25(OH)D levels. In this case control study, 285 type 2 diabetic patients who attended the Manda Memorial Hospital from March to October 2017 were selected and 25(OH)D, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglyceride (TG) levels, were obtained. Multiple regression analysis revealed that the association between 25(OH)D concentrations and TG levels was statistically significant (p<0.01) after adjusting for age, sex, body mass index, estimated glomerular flow rate (eGFR), insulin use, duration of diabetes mellitus, glycosylated hemoglobin (HbA1c), alcohol consumption, current smoking, and sampling timing. The serum 25(OH)D level was inversely associated with the TG level after the adjustment for the characteristics of Japanese patients with type 2 diabetes mellitus.

PMID:34719611 | DOI:10.3177/jnsv.67.266