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Abstract The phase diagram for the quasi-binary Tl2Se–Bi2Te3 system has been established based on the results of phase studies by differential thermal analysis (DTA) and X-ray diffraction (XRD). The diagram for the title system presented in this paper has been compared with that previously published by other authors. As a result of the research, a significant correction of the former phase diagram was made, because it was found that the components of the studied system formed three new chemical compounds. Obtained phase diagram has been compared with other thallium(I)–bismuth(III) chalcogenide systems.

Abstract Background Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Methods Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. Results scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Conclusions We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately.

Abstract Background Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. Methods We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. Results Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. Conclusions We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Abstract Mother’s milk is widely recommended as complete food for the offspring in earliest postnatal time. However, the knowledge about detailed composition and the physiological role of bioactive components of breast milk is incomplete. Therefore, the aim of our study was to determine the content of kynurenine (KYN) in human breast milk during lactation and to explore the effects exerted by intragastric KYN administration from birth to weaning on physical and psychomotor development of adult rats. We found that KYN is consistently present in human milk and its content gradually increased from day 4 to 28 after delivery and that it is present in commercial baby formulas in amounts noticeably exceeding its physiological range. Animal studies showed that KYN supplementation resulted in a marked elevation of absorptive surface of rat intestine and in enhanced expression of both, aryl hydrocarbon receptor and G protein-coupled receptor 35 in the intestinal tissue in rats. Moreover, we discovered that KYN administration from birth to weaning resulted in neurobehavioral changes in adult rats. Therefore, we postulate that further research is required to thoroughly understand the function of KYN in early developmental stages of mammals and to ensure the safety of its presence in baby food products.

Abstract Background This paper describes a unique case—the first case of multiple fractures of the thoracic vertebrae caused by a low-voltage electric shock. Case presentation A 22-year-old male patient was diagnosed with compression fractures of Th2–Th6 caused by a muscle spasm resulting from an electric shock. The patient was treated conservatively using a cervico-thoracic support corset. After rehabilitation, the patient has regained his physiological movement of the spine without any back pain. Conclusions Albeit vertebral fractures caused by electric shock injury are extremely rare, clinicians should always keep in mind this diagnosis, especially when clinical symptoms such as pain and limitation of movement are present.

Abstract Early diagnosis of hepatitis C virus (HCV) infection is essential for prompt initiation of treatment and prevention of transmission, yet several logistical barriers continue to limit access to HCV testing. Dried blood spot (DBS) technology involves a simple fingerstick that eliminates the need for trained personnel, and DBS can be stored and transported at room temperature. We evaluated the use of DBS whole blood samples in the modified Abbott ARCHITECT anti-HCV assay, comparing assay performance against the standard assay run using DBS and venous plasma samples. 144 HCV positive and 104 HCV negative matched venous plasma and whole blood specimens were selected from a retrospective study with convenience sampling in Cameroon. Results obtained using a modified volume DBS assay were highly correlated to the results of the standard assay run with plasma on clinical samples and dilution series (R2 = 0.71 and 0.99 respectively). The ARCHITECT Anti-HCV assay with input volume modification more accurately detects HCV antibodies in DBS whole blood samples with 100% sensitivity and specificity, while the standard assay had 90.97% sensitivity. The use of DBS has the potential to expand access to HCV testing to underserved or marginalized populations with limited access to direct HCV care.

Lake sediments store metal contaminants from historic pesticide and herbicide use and mining operations. Historical regional smelter operations in the Puget Sound lowlands have resulted in arsenic concentrations exceeding 200 μg As g-1 in urban lake sediments. Prior research has elucidated how sediment oxygen demand, warmer sediment temperatures, and alternating stratification and convective mixing in shallow lakes results in higher concentrations of arsenic in aquatic organisms when compared to deeper, seasonally stratified lakes with similar levels of arsenic pollution in profundal sediments. In this study we examine the trophic pathways for arsenic transfer through the aquatic food web of urban lakes in the Puget Sound lowlands, measuring C and N isotopes-to determine resource usage and trophic level-and total and inorganic arsenic in primary producers and primary and secondary consumers. Our results show higher levels of arsenic in periphyton than in other primary producers, and higher concentrations in snails than zooplankton or insect macroinvertebrates. In shallow lakes arsenic concentrations in littoral sediment are similar to deep profundal sediments due to arsenic remobilization, mixing, and redeposition, resulting in direct arsenic exposure to littoral benthic organisms such as periphyton and snails. The influence of littoral sediment on determining arsenic trophic transfer is evidenced by our results which show significant correlations between total arsenic in littoral sediment and total arsenic in periphyton, phytoplankton, zooplankton, snails, and fish across multiple lakes. We also found a consistent relationship between percent inorganic arsenic and trophic level (determined by δ15N) in lakes with different depths and mixing regimes. Cumulatively, these results combine to provide a strong empirical relationship between littoral sediment arsenic levels and inorganic arsenic in edible species that can be used to screen lakes for potential human health risk using an easy, inexpensive sampling and analysis method.

Oleanolic acid (OA), as a ubiquitous compound in the plant kingdom, is studied for both its neuroprotective and neurotoxic properties. The mechanism of acetylcholinesterase (AChE) inhibitory potential of OA is investigated using molecular dynamic simulations (MD) and docking as well as biomimetic tests. Moreover, the in vitro SH-SY5Y human neuroblastoma cells and the in vivo zebrafish model were used. The inhibitory potential towards the AChE enzyme is examined using the TLC-bioautography assay (the IC50 value is 9.22 μM). The CH-π interactions between the central fragment of the ligand molecule and the aromatic cluster created by the His440, Phe288, Phe290, Phe330, Phe331, Tyr121, Tyr334, Trp84, and Trp279 side chains are observed. The results of the in vitro tests using the SH-SY5Y cells indicate that the viability rate is reduced to 71.5%, 61%, and 43% at the concentrations of 100 µg/mL, 300 µg/mL, and 1000 µg/mL, respectively, after 48 h of incubation, whereas cytotoxicity against the tested cell line with the IC50 value is 714.32 ± 32.40 µg/mL. The in vivo tests on the zebrafish prove that there is no difference between the control and experimental groups regarding the mortality rate and morphology (p > 0.05).

Abstract Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

Abstract Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis.

Abstract Background Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model. Conclusions Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.

Abstract Background The biomarkers alpha-fetoprotein (AFP) and protein induced by vitamin K absence/antagonist-II (PIVKA-II) may be useful for detecting early-stage hepatocellular carcinoma (HCC). We evaluated the performance of AFP and PIVKA-II levels, alone and in combination with clinical factors, for the early detection of HCC. Methods In a case–control study, serum AFP and PIVKA-II were measured using the ARCHITECT immunoassay analyzer system in a cohort of 119 patients with HCC, 215 patients with non-malignant liver disease, and 34 healthy subjects. Five predictive models for detecting HCC were developed based on age, gender, AFP, and/or PIVKA-II levels; the best model was validated in an independent cohort of 416 patients with HCC and 412 control subjects with cirrhosis. Results In both cohorts, AFP and PIVKA-II concentrations were higher in patients with HCC compared to healthy controls and patients with non-malignant liver disease. The model that combined AFP and PIVKA-II, age, and gender had the highest AUC of 0.95 (0.95, 95% CI 0.93–0.98), with a sensitivity of 93% and a specificity of 84% in the development cohort, and an AUC of 0.87 (95% CI 0.85–0.90), sensitivity of 74%, and specificity of 85% in the validation cohort. When limiting the validation cohort to only early-stage HCC, the AUC was 0.85 (95% CI 0.81–0.88), sensitivity was 70%, and specificity was 86%. Conclusions Compared to each biomarker alone, the combination of AFP and PIVKA-II with age and gender improved the accuracy of detecting HCC and differentiating HCC from non-malignant liver disease.

MicroRNAs (miRNAs, miRs) are small non-coding RNA (ncRNA) molecules capable of regulating post-transcriptional gene expression. Imbalances in the miRNA network have been associated with the development of many pathological conditions and diseases, including cancer. Recently, miRNAs have also been linked to the phenomenon of multidrug resistance (MDR). MiR-7 is one of the extensively studied miRNAs and its role in cancer progression and MDR modulation has been highlighted. MiR-7 is engaged in multiple cellular pathways and acts as a tumor suppressor in the majority of human neoplasia. Its depletion limits the effectiveness of anti-cancer therapies, while its restoration sensitizes cells to the administered drugs. Therefore, miR-7 might be considered as a potential adjuvant agent, which can increase the efficiency of standard chemotherapeutics.

An amendment to this paper has been published and can be accessed via the original article.

The aim of the study was to assess plasma concentration of catecholamines and asymmetric dimethyl arginine levels and a possible relationship to predict the mortality rates among hemodialysis patients. The study population comprised 27 subjects, aged 65-70 years. Each patient underwent dialysis thrice a week. Furthermore, the median duration of hemodialysis was 3.5 years. Based on the conducted research, it can be concluded that the concentrations of adrenaline and the level of asymmetric dimethylarginine have predictive value of mortality among hemodialysis patients. Of note, lowering plasma asymmetric dimethylarginine concentration may represent therapeutic target for prevention of progressive renal damage.

Considering the nutritional values, breadstuff plays a big part in covering human nourishment needs and constitutes a base of all day diet. Moreover, bread is an excellent source of numerous vitamins and minerals the abundance of which depends on the degree of grinding. Thus, it seems to be very important to know the composition and level of bio-elements. That is why the main target of this study was to evaluate the concentration of selected trace elements: chromium (Cr), nickel (Ni), iron (Fe) and manganese (Mn) in wheat grain, wheat bran, different wheat and rye flour types and variety of breadstuff also with addition of grains and seeds from different bakeries and mills. Another task was to analyze if the technological process has an influence on secondary despoil of bread goods with heavy metal elements. The analyzed trace elements were measured with a precise and accurate atomic absorption spectrophotometric method (AAS) and the results were expressed in mg/kg of selected sample. Obtained results show that bread and grain products are a good source of trace elements like chromium, nickel, iron and manganese. However, the higher levels of chromium and nickel in bread goods could rather be an effect of impurity caused by a technological process in mill and bakeries.

BACKGROUND:Motor Unit Number Index (MUNIX) is a neurophysiological measure that provides an index of the number of lower motor neurons in a muscle. Its performance across centres in healthy subjects and patients with Amyotrophic Lateral Sclerosis (ALS) has been established, but inter-rater variability between multiple raters in one single subject has not been investigated. OBJECTIVE:To assess reliability in a set of 6 muscles in a single subject among 12 examiners (6 experienced with MUNIX, 6 less experienced) and to determine variables associated with variability of measurements. METHODS:Twelve raters applied MUNIX in six different muscles (abductor pollicis brevis (APB), abductor digiti minimi (ADM), biceps brachii (BB), tibialis anterior (TA), extensor dig. brevis (EDB), abductor hallucis (AH)) twice in one single volunteer on consecutive days. All raters visited at least one training course prior to measurements. Intra- and inter-rater variability as determined by the coefficient of variation (COV) between different raters and their levels of experience with MUNIX were compared. RESULTS:Mean intra-rater COV of MUNIX was 14.0% (±6.4) ranging from 5.8 (APB) to 30.3% (EDB). Mean inter-rater COV was 18.1 (±5.4) ranging from 8.0 (BB) to 31.7 (AH). No significant differences of variability between experienced and less experienced raters were detected. CONCLUSION:We provide evidence that quality control for neurophysiological methods can be performed with similar standards as in laboratory medicine. Intra- and inter-rater variability of MUNIX is muscle-dependent and mainly below 20%. Experienced neurophysiologists can easily adopt MUNIX and adequate teaching ensures reliable utilization of this method.