Search

After independence the Government of India planned to replace the leadership systems prevalent in the rural areas by new systems. The aim of this study is to analyse the problems involved in introducing modern leadership in the rural social systems of India dominated by traditional leadership. What forms the background of the present work is the hypothesis, namely, that changes in leadership are dependent on changes in other social spheres which are not directly connected with the sphere of leadership. One such sphere is the occupational structure of a microsocial system, which is seen here as one crucial independent variable for change in rural leadership structure. This hypothesis is particularly interesting, because its empirical derivation, could be rechecked. Occupational diversification has been seen as a dependent variable in the process of change in economic behaviour on the one hand and on the other as an independent variable in the process of change in leadership behaviour.

Introduction: Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV)., Methods: An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed., Results: The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan., Conclusion: Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application., (Crown Copyright © 2023 Published by Elsevier Inc. on behalf of the International Society of Nephrology.)

Objective: To test the performance of a novel machine learning-based breast density tool. The tool utilizes a convolutional neural network to predict the BI-RADS based density assessment of a study. The clinical density assessments of 33,000 mammographic examinations (164,000 images) from one academic medical center (Site A) were used for training., Materials and Methods: This was an IRB approved HIPAA compliant study performed at two academic medical centers. The validation data set was composed of 500 studies from one site (Site A) and 700 from another (Site B). At Site A, each study was assessed by three breast radiologists and the majority (consensus) assessment was used as truth. At Site B, if the tool agreed with the clinical reading, then it was considered to have correctly predicted the clinical reading. In cases where the tool and the clinical reading disagreed, then the study was evaluated by three radiologists and the consensus reading was used as the clinical reading., Results: For the classification into the four categories of the Breast Imaging Reporting and Data System (BI-RADS®), the AI classifier had an accuracy of 84.6% at Site A and 89.7% at Site B. For binary classification (dense vs. non-dense), the AI classifier had an accuracy of 94.4% at Site A and 97.4% at Site B. In no case did the classifier disagree with the consensus reading by more than one category., Conclusions: The automated breast density tool showed high agreement with radiologists' assessments of breast density., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sven Schoenherr, Martin Seebass, MingDe Lin and Malte Westerhoff are employees of Visage Imaging, owner and potential distributor of the described software tool., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The benefits of large-scale genetic studies for healthcare of the populations studied are well documented, but these genetic studies have traditionally ignored people from some parts of the world, such as South Asia. Here we describe whole genome sequence (WGS) data from 4806 individuals recruited from the healthcare delivery systems of Pakistan, India and Bangladesh, combined with WGS from 927 individuals from isolated South Asian populations. We characterize population structure in South Asia and describe a genotyping array (SARGAM) and imputation reference panel that are optimized for South Asian genomes. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of rare homozygotes that reach 100 times that seen in outbred populations. Founder effects increase the power to associate functional variants with disease processes and make South Asia a uniquely powerful place for population-scale genetic studies., (© 2023. The Author(s).)

Background: Lipoprotein(a) (Lp(a)) concentrations are a major independent risk factor for coronary artery disease (CAD) and are mainly determined by variation in LPA. Up to 70% of the LPA coding sequence is located in the hypervariable kringle IV type 2 (KIV-2) region. It is hardly accessible by conventional technologies, but may contain functional variants., Objectives: This study sought to investigate the new, very frequent splicing variant KIV-2 4733G>A on Lp(a) and CAD., Methods: We genotyped 4733G>A in the GCKD (German Chronic Kidney Disease) study (n = 4,673) by allele-specific polymerase chain reaction, performed minigene assays, identified proxy single nucleotide polymorphisms and used them to characterize its effect on CAD by survival analysis in UK Biobank (n = 440,234). Frequencies in ethnic groups were assessed in the 1000 Genomes Project., Results: The 4733G>A variant (38.2% carrier frequency) was found in most isoform sizes. It reduces allelic expression without abolishing protein production, lowers Lp(a) by 13.6 mg/dL (95% CI: 12.5-14.7; P < 0.0001) and is the strongest variance-explaining factor after the smaller isoform. Splicing of minigenes was modified. Compound heterozygosity (4.6% of the population) for 4733G>A and 4925G>A, another KIV-2 splicing mutation, reduces Lp(a) by 31.8 mg/dL and most importantly narrows the interquartile range by 9-fold (from 42.1 to 4.6 mg/dL) when compared to the wild type. In UK Biobank 4733G>A alone and compound heterozygosity with 4925G>A reduced HR for CAD by 9% (95% CI: 7%-11%) and 12% (95% CI: 7%-16%) (both P < 0.001). Frequencies in ethnicities differ notably., Conclusions: Functional variants in the previously inaccessible LPA KIV-2 region cooperate in determining Lp(a) variance and CAD risk. Even a moderate but lifelong genetic Lp(a) reduction translates to a noticeable CAD risk reduction., Competing Interests: Funding Support and Author Disclosures The study was supported by the Austrian Science Fund (FWF) project P31458-B34 and the D•A•CH Advancement Award Lipidology 2015 (supported by the Christine Katharine Schmitz Foundation) of the D•A•CH-Society for Prevention of Cardiovascular Diseases (to S.C.). The GCKD study is supported by the German Ministry of Education and Research (Bundesministerium für Bildung und Forschung, grants 01ER 0804, 01ER 0818, 01ER 0819, 01ER 0820, and 01ER 0821) and the KfH Foundation for Preventive Medicine (Kuratorium für Heimdialyse und Nierentransplantation e.V.–Stiftung Präventivmedizin) and corporate sponsors (see the GCKD website). Ms Schachtl-Riess has received support from the Dr Legerlotz Foundation. Dr Köttgen has received support from the German Research Foundation (grant KO 3598/5-1). Dr Kronenberg has received support from the Austrian Science Fund (project W-1253DK HOROS); has received lecture fees from Novartis, Amgen, and Kaneka; and has served on the advisory boards of Amgen and Kaneka. Drs Kronenberg and Coassin have received support from the Lipoprotein(a) Center And Research InstitutE [Lp(a)CARE] for their lipoprotein(a) research. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)