An unusual presentation of acute abdomen: infarcted peritoneal cyst-a probable asbestos-related benign cystic mesothelioma

ABSTRACT This is a report of a rare case of an infarcted pelvic intra-abdominal cyst, having no mesenteric connection presenting as an acute abdomen. The patient had significant asbestos exposure. The cyst was treated successfully by surgical excision. Histopathology showed an infarcted cyst; the lining was destroyed, precluding marker studies. A diagnosis of benign cystic peritoneal mesothelioma (BCPM) was made by excluding other causes of solitary pelvic intra-abdominal cysts. BCPM has been classified as an asbestos-related neoplasm and is usually seen in the pelvis adjunct to the urinary bladder. One-year post-surgery, there was no recurrence. The case report shows that infarcted pelvic mesothelial cysts can present as an acute abdomen and can be treated successfully by total excision with no recurrence.

Overview of Human Lineage Genetic Marker Studies in Bosnia and Herzegovina: Y chromosome story

– Modern Bosnia and Herzegovina is a state consisting of multiple ethnicities and regions located in the Western Balkan, with a very complex history. The earliest historical findings show that its area was inhabited since the Paleolithic. From that time, this part of Europe, especially the region of the Modern Bosnia and Herzegovina, could be recognized as the crossroad for the different human migration and the meeting point for different cultures, religions and gene pools. Mitochondrial DNA is being used for maternal lineage testing, while the Y chromosome is being used for paternal lineage testing. Therefore, these markers are being referred to as lineage markers. Lineage markers are often used for parental lineage monitoring in population genetics, human genetics, as well as in forensic genetics. The main intention of this paper is to construct a short overview of the Y chromosome studies performed in Bosnia and Herzegovina within the last two decades.

MIPMLP – Microbiome Preprocessing Machine Learning Pipeline

Abstract16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML. We checked multiple preprocessing steps and tested the optimal combination for 16S sequencing-based classification tasks. We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification. We show that the log of the feature counts is much more informative than the relative counts. We further show that merging features associated with the same taxonomy at a given level, through a dimension reduction step for each group of bacteria improves the AUC. Finally, we show that z-scoring has a very limited effect on the results. These preprocessing steps are integrated into the MIPMLP - Microbiome Preprocessing Machine Learning Pipeline, which is available as a stand alone version at https://github.com/louzounlab/microbiome/tree/master/Preprocess or as a service at http://mip-mlp.math.biu.ac.il/HomeImportanceMicrobiome composition has been proposed as a biomarker (mic-marker) for multiple diseases. However, a clear analysis of the optimal way to represent the gene sequence counts is still lacking.We propose a simple and straight forward method that significantly improves the accuracy of mic-marker studies.This method can be of use to merge two of the most important advances in biology in the last decade: Microbiome analysis, and the introduction of machine learning methods to biological studies.

METAWORKS: A flexible, scalable bioinformatic pipeline for multi-marker biodiversity assessments

BackgroundMulti-marker metabarcoding is increasingly being used to generate biodiversity information across different domains of life from microbes to fungi to animals such as in ecological and environmental studies. Current popular bioinformatic pipelines support microbial and fungal marker analysis, while ad hoc methods are used to process animal metabarcode markers from the same study. The purpose of this paper is to introduce MetaWorks, a ‘meta’barcode pipeline that does ‘the works’ and supports the bioinformatic processing of various metabarcoding markers including rRNA and their spacers as well as protein coding loci.ResultsMetaWorks provides a Conda environment to quickly gather most of the programs and dependencies for the pipeline. MetaWorks is automated using Snakemake to ensure reproducibility and scalability. We have supplemented existing RDP-trained classifiers for SSU (prokaryotes), ITS (fungi), and LSU (fungi) with trained classifiers for COI (eukaryotes), rbcL (diatoms or eukaryotes), SSU (diatoms or eukaryotes), and 12S (fish). MetaWorks can process rRNA genes, but it can also properly handle ITS spacers by trimming flanking conserved rRNA gene regions, as well as handle protein coding genes by removing obvious pseudogenes.ConclusionsAs far as we are aware, MetaWorks is the first flexible multi-marker metabarcode pipeline that can accommodate rRNA genes, spacer, and protein coding markers in the same pipeline. This is ideal for large-scale, multi-marker studies to provide a harmonized processing environment, pipeline, and taxonomic assignment approach. Updates to MetaWorks will be made as needed to reflect advances in the underlying programs, reference databases, or hidden Markov model (HMM) profiles for pseudogene filtering. Future developments will include support for additional metabarcode markers, RDP trained reference databases, and HMM profiles for pseudogene filtering.

Importance of biochemical markers in routine oncological practice (according to materials of clinical recommendations)

Background. The diagnostics of neuroendocrine tumors (NET) is complex due to many factors such as the heterogeneity of the tumors themselves, different localization of the tumor process, and the presence of severe hormonal syndromes. A special place in the diagnostic search is given to the study of biochemical markers which are conditionally divided into universal and specific ones.Materials and methods. Chromogranin A (CGA) is a universal marker that in most cases identifies tumors of a neuroendocrine nature and is characterized by the best combination of diagnostic sensitivity and specificity. Pancreatic polypeptide (PP) and neuron-specific enolase (NSE) are determined in addition to CGA in pancreatic tumors and in low-grade forms of neuroendocrine cancer. Specific markers, serum serotonin and its metabolite in daily urine 5-hydroxyindolacetic acid (5-GIUC), are the generally recognized specific markers for diagnosing of carcinoid syndrome. Other specific markers such as gastrin, insulin, glucagon, and others, are associated with certain hyperfunctional syndromes and are being investigated to confirm their presence. The article presents generalized recommendations for the use of biochemical markers, taking into account the existing clinical signs, syndromes, and types of NETs. To monitor the course of the tumor process and evaluate the effectiveness of treatment in patients with an established diagnosis of NET, it is recommended to determine the biochemical markers with increased basal levels. At the same time, there is a need to standardize the survey timing. For more accurate monitoring and interpretation of data, serial marker studies should be performed with the same test systems used in the same specialized laboratory.