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Throughout the United States, cancer remains the second leading cause of death. Traditional treatments induce significant medical toxic effects and unpleasant adverse reactions, making them inappropriate for long-term use. Consequently, anticancer-drug resistance and relapse are frequent in certain situations. Thus, there is an urgent necessity to find effective antitumor medications that are specific and have few adverse consequences. Curcumin is a polyphenol derivative found in the turmeric plant (Curcuma longa L.), and provides chemopreventive, antitumor, chemo-, and radio-sensitizing properties. In this paper, we summarize the new nano-based formulations of polyphenolic curcumin because of the growing interest in its application against cancers and tumors. According to recent studies, the use of nanoparticles can overcome the hydrophobic nature of curcumin, as well as improving its stability and cellular bioavailability in vitro and in vivo. Several strategies for nanocurcumin production have been developed, each with its own set of advantages and unique features. Because the majority of the curcumin-based nanoformulation evidence is still in the conceptual stage, there are still numerous issues impeding the provision of nanocurcumin as a possible therapeutic option. To support the science, further work is necessary to develop curcumin as a viable anti-cancer adjuvant. In this review, we cover the various curcumin nanoformulations and nanocurcumin implications for therapeutic uses for cancer, as well as the current state of clinical studies and patents. We further address the knowledge gaps and future research orientations required to develop curcumin as a feasible treatment candidate.

Background Principal Component Analysis (PCA) is widely used in population genetics to visualize genetic relationships and population structures. In ancient genomics, genotype information may in parts remain unresolved due to the low abundance and degraded quality of ancient DNA. While methods like SmartPCA allow the projection of ancient samples despite missing data, they do not quantify projection uncertainty. The reliability of PCA projections for often very sparse ancient genotype samples is not well understood. Ignoring this uncertainty may lead to overconfident conclusions about the observed genetic relationships and population structure. Results This study systematically investigates the impact of missing loci on PCA projections using both simulated and real ancient human genotype data. Through extensive simulations with high-coverage ancient samples, we demonstrate that increasing levels of missing data can lead to less accurate SmartPCA projections, highlighting the importance of considering uncertainty when interpreting PCA results from ancient samples. To address this, we developed a probabilistic framework to quantify the uncertainty in PCA projections due to missing data. By applying our methodology to modern and ancient West Eurasian genotype samples from the Allen Ancient DNA Resource database, we could show a high concordance between our predicted projection and empirically derived distributions. Applying this framework to real-world data, we demonstrate its utility in predicting and visualizing embedding uncertainties for ancient samples of varying SNP coverages. Conclusion Our results emphasize the importance of accounting for projection uncertainty in ancient population studies. We therefore make our probabilistic model available through TrustPCA, a user-friendly web tool that provides researchers with uncertainty estimates alongside PCA projections, facilitating data exploration in ancient human genomic studies and enhancing transparency in data quality reporting.

Artificial intelligence (AI) in medicine and digital assistance systems such as chatbots will play an increasingly important role in future doctor - patient communication. To benefit from the potential of this technical innovation and ensure optimal patient care, future physicians should be equipped with the appropriate skills. Accordingly, a suitable place for the management and adaptation of digital assistance systems must be found in the medical education curriculum. To determine the existing levels of knowledge of medical students about AI chatbots in particular in the healthcare setting, this study surveyed medical students of the University of Luebeck and the University Hospital of Tuebingen. Using standardized quantitative questionnaires and qualitative analysis of group discussions, the attitudes of medical students toward AI and chatbots in medicine were investigated. From this, relevant requirements for the future integration of AI into the medical curriculum could be identified. The aim was to establish a basic understanding of the opportunities, limitations, and risks, as well as potential areas of application of the technology. The participants (N = 12) were able to develop an understanding of how AI and chatbots will affect their future daily work. Although basic attitudes toward the use of AI were positive, the students also expressed concerns. There were high levels of agreement regarding the use of AI in administrative settings (83.3%) and research with health-related data (91.7%). However, participants expressed concerns that data protection may be insufficiently guaranteed (33.3%) and that they might be increasingly monitored at work in the future (58.3%). The evaluations indicated that future physicians want to engage more intensively with AI in medicine. In view of future developments, AI and data competencies should be taught in a structured way during the medical curriculum and integrated into curricular teaching.

Three 1,000-year-old mycobacterial genomes from Peruvian human skeletons reveal that a member of the Mycobacterium tuberculosis complex derived from seals caused human disease before contact in the Americas. Tuberculosis in the Americas Mycobacterium tuberculosis has a long history as a human pathogen, but how and when this unfortunate relationship began is not clear. Although the strains found in the Americas today are closely related to those in Europe, archaeological evidence suggests that the disease was present in the New World before contact with Europeans. Johannes Krause and colleagues sequenced three approximately 1,000-year-old M. tuberculosis genomes from human remains in Peru, proving that the pathogen caused human disease in the pre-contact New World. The ancient DNA is most closely related to that found in strains adapted to seals and sea lions. The authors hypothesize that these sea mammals may have contracted the disease from an African host species and carried it across the oceans where exploitation of marine resources by coastal peoples of South America allowed zoonotic transfer. This strain of tuberculosis may have then adapted to humans before being replaced by European strains introduced post-contact. Modern strains of Mycobacterium tuberculosis from the Americas are closely related to those from Europe, supporting the assumption that human tuberculosis was introduced post-contact 1 . This notion, however, is incompatible with archaeological evidence of pre-contact tuberculosis in the New World 2 . Comparative genomics of modern isolates suggests that M. tuberculosis attained its worldwide distribution following human dispersals out of Africa during the Pleistocene epoch 3 , although this has yet to be confirmed with ancient calibration points. Here we present three 1,000-year-old mycobacterial genomes from Peruvian human skeletons, revealing that a member of the M. tuberculosis complex caused human disease before contact. The ancient strains are distinct from known human-adapted forms and are most closely related to those adapted to seals and sea lions. Two independent dating approaches suggest a most recent common ancestor for the M. tuberculosis complex less than 6,000 years ago, which supports a Holocene dispersal of the disease. Our results implicate sea mammals as having played a role in transmitting the disease to humans across the ocean.

Mycobacterium lepromatosis is an uncultured human pathogen associated with diffuse lepromatous leprosy and a reactional state known as Lucio's phenomenon. By using deep sequencing with and without DNA enrichment, we obtained the near-complete genome sequence of M. lepromatosis present in a skin biopsy from a Mexican patient, and compared it with that of Mycobacterium leprae , which has undergone extensive reductive evolution. The genomes display extensive synteny and are similar in size (∼3.27 Mb). Protein-coding genes share 93% nucleotide sequence identity, whereas pseudogenes are only 82% identical. The events that led to pseudogenization of 50% of the genome likely occurred before divergence from their most recent common ancestor (MRCA), and both M. lepromatosis and M. leprae have since accumulated new pseudogenes or acquired specific deletions. Functional comparisons suggest that M. lepromatosis has lost several enzymes required for amino acid synthesis whereas M. leprae has a defective heme pathway. M. lepromatosis has retained all functions required to infect the Schwann cells of the peripheral nervous system and therefore may also be neuropathogenic. A phylogeographic survey of 227 leprosy biopsies by differential PCR revealed that 221 contained M. leprae whereas only six, all from Mexico, harbored M. lepromatosis . Phylogenetic comparisons indicate that M. lepromatosis is closer than M. leprae to the MRCA, and a Bayesian dating analysis suggests that they diverged from their MRCA approximately 13.9 Mya. Thus, despite their ancient separation, the two leprosy bacilli are remarkably conserved and still cause similar pathologic conditions. Significance Leprosy was thought to be exclusively caused by infection of humans by Mycobacterium leprae . In 2008, Han et al. proposed that Mycobacterium lepromatosis , a separate unculturable species, might be responsible for a rare yet severe form of the disease called diffuse lepromatous leprosy. Here, by using comparative genomics, we show that the two species are very closely related and derived from a common ancestor that underwent genome downsizing and gene decay. Since their separation 13.9 Mya, the two species have continued to lose genes, but from different regions of the genome, and M. leprae appears to be more recent. In a phylogeographic survey, by using differential PCR, we found that M. lepromatosis was scarce and restricted to patients from Mexico.

Campylobacter jejuni is currently the leading cause of bacterial gastroenteritis in humans. Comparison of multiple Campylobacter strains revealed a high genetic and phenotypic diversity. However, little is known about differences in transcriptome organization, gene expression, and small RNA (sRNA) repertoires. Here we present the first comparative primary transcriptome analysis based on the differential RNA-seq (dRNA-seq) of four C. jejuni isolates. Our approach includes a novel, generic method for the automated annotation of transcriptional start sites (TSS), which allowed us to provide genome-wide promoter maps in the analyzed strains. These global TSS maps are refined through the integration of a SuperGenome approach that allows for a comparative TSS annotation by mapping RNA-seq data of multiple strains into a common coordinate system derived from a whole-genome alignment. Considering the steadily increasing amount of RNA-seq studies, our automated TSS annotation will not only facilitate transcriptome annotation for a wider range of pro- and eukaryotes but can also be adapted for the analysis among different growth or stress conditions. Our comparative dRNA-seq analysis revealed conservation of most TSS, but also single-nucleotide-polymorphisms (SNP) in promoter regions, which lead to strain-specific transcriptional output. Furthermore, we identified strain-specific sRNA repertoires that could contribute to differential gene regulation among strains. In addition, we identified a novel minimal CRISPR-system in Campylobacter of the type-II CRISPR subtype, which relies on the host factor RNase III and a trans-encoded sRNA for maturation of crRNAs. This minimal system of Campylobacter, which seems active in only some strains, employs a unique maturation pathway, since the crRNAs are transcribed from individual promoters in the upstream repeats and thereby minimize the requirements for the maturation machinery. Overall, our study provides new insights into strain-specific transcriptome organization and sRNAs, and reveals genes that could modulate phenotypic variation among strains despite high conservation at the DNA level.

Egypt, located on the isthmus of Africa, is an ideal region to study historical population dynamics due to its geographic location and documented interactions with ancient civilizations in Africa, Asia and Europe. Particularly, in the first millennium BCE Egypt endured foreign domination leading to growing numbers of foreigners living within its borders possibly contributing genetically to the local population. Here we present 90 mitochondrial genomes as well as genome-wide data sets from three individuals obtained from Egyptian mummies. The samples recovered from Middle Egypt span around 1,300 years of ancient Egyptian history from the New Kingdom to the Roman Period. Our analyses reveal that ancient Egyptians shared more ancestry with Near Easterners than present-day Egyptians, who received additional sub-Saharan admixture in more recent times. This analysis establishes ancient Egyptian mummies as a genetic source to study ancient human history and offers the perspective of deciphering Egypt’s past at a genome-wide level. Archaeological and historical records had shown ancient Egypt before and after Ptolemaic and Roman periods to be a hub of human migration and exchange. Here, Schuenemann and colleagues analyse ancient mitochondrial and nuclear DNA to investigate the genetic history of Egypt.

BackgroundFor the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a classical machine learning algorithm. It demonstrated good clinical usability and a very accurate correlation of the volumes to the single area determination in a reproducible axial layer. This study aims to investigate whether these established segmentation algorithms can be transferred to new, more generalizable deep learning algorithms employing an extended transfer learning procedure and whether medically meaningful segmentation is possible.MethodsNinety-five routinely performed true FISP MRI sequences were retrospectively analyzed in 43 patients with pediatric hydrocephalus. Using a freely available and clinically established segmentation algorithm based on a hidden Markov random field model, four classes of segmentation (brain, cerebrospinal fluid (CSF), background, and tissue) were generated. Fifty-nine randomly selected data sets (10,432 slices) were used as a training data set. Images were augmented for contrast, brightness, and random left/right and X/Y translation. A convolutional neural network (CNN) for semantic image segmentation composed of an encoder and corresponding decoder subnetwork was set up. The network was pre-initialized with layers and weights from a pre-trained VGG 16 model. Following the network was trained with the labeled image data set. A validation data set of 18 scans (3289 slices) was used to monitor the performance as the deep CNN trained. The classification results were tested on 18 randomly allocated labeled data sets (3319 slices) and on a T2-weighted BrainWeb data set with known ground truth.ResultsThe segmentation of clinical test data provided reliable results (global accuracy 0.90, Dice coefficient 0.86), while the CNN segmentation of data from the BrainWeb data set showed comparable results (global accuracy 0.89, Dice coefficient 0.84). The segmentation of the BrainWeb data set with the classical FAST algorithm produced consistent findings (global accuracy 0.90, Dice coefficient 0.87). Likewise, the area development of brain and CSF in the long-term clinical course of three patients was presented.ConclusionUsing the presented methods, we showed that conventional segmentation algorithms can be transferred to new advances in deep learning with comparable accuracy, generating a large number of training data sets with relatively little effort. A clinically meaningful segmentation possibility was demonstrated.

Altitudinal gradients provide valuable information about the effects of environmental variables on changes in species richness and composition as well as the distribution of below ground fungal communities. Since most knowledge in this respect has been gathered on aboveground communities, we focused our study towards the characterization of belowground fungal communities associated with two different ages of Norway spruce (Picea abies) trees along an altitudinal gradient. By sequencing the internal transcribed spacer (ITS) region on the Illumina platform, we investigated the fungal communities in a floristically and geologically relatively well explored forest on the slope of Mt. Iseler of the Bavarian Alps. From fine roots and rhizosphere of a total of 90 of Norway spruce trees from 18 plots we detected 1285 taxa, with a range of 167 to 506 (average 377) taxa per plot. Fungal taxa are distributed over 96 different orders belonging to the phyla Ascomycota, Basidiomycota, Chrytridiomycota, Glomeromycota, and Mucoromycota. Overall the Agaricales (438 taxa) and Tremellales (81 taxa) belonging to the Basidiomycota and the Hypocreales (65 spp.) and Helotiales (61 taxa) belonging to the Ascomycota represented the taxon richest orders. The evaluation of our multivariate generalized mixed models indicate that the altitude has a significant influence on the composition of the fungal communities (p < 0.003) and that tree age determines community diversity (p < 0.05). A total of 47 ecological guilds were detected, of which the ectomycorrhizal and saprophytic guilds were the most taxon-rich. Our ITS amplicon Illumina sequencing approach allowed us to characterize a high fungal community diversity that would not be possible to capture with fruiting body surveys alone. We conclude that it is an invaluable tool for diverse monitoring tasks and inventorying biodiversity, especially in the detection of microorganisms developing very ephemeral and/or inconspicuous fruiting bodies or lacking them all together. Results suggest that the altitude mainly influences the community composition, whereas fungal diversity becomes higher in mature/older trees. Finally, we demonstrate that novel techniques from bacterial microbiome analyses are also useful for studying fungal diversity and community structure in a DNA metabarcoding approach, but that incomplete reference sequence databases so far limit effective identification.

Background DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files. Results We have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved. Conclusions We present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at http://microarray-analysis.org .