Prevalence of the cancer-associated germline variants in Russian adults and long-living individuals: using the ACMG recommendations and computational interpreters for pathogenicity assessment.

Background: Population studies are essential for gathering critical disease prevalence data. Automated pathogenicity assessment tools enhance the capacity to interpret and annotate large amounts of genetic data. In this study, we assessed the prevalence of cancer-associated germline variants in Russia using a semiautomated variant interpretation algorithm. Methods: We examined 74,996 Russian adults (Group 1) and 2,872 long-living individuals aged ≥ 90 years (Group 2) for variants in 28 ACMG-recommended cancer-associated genes in three steps: InterVar annotation; ClinVar interpretation; and a manual review of the prioritized variants based on the available data. Using the data on the place of birth and the region of residence, we determined the geographical distribution of the detected variants and tracked the migration dynamics of their carriers. Results: We report 175 novel del-VUSs. We detected 232 pathogenic variants, 46 likely pathogenic variants, and 216 del-VUSs in Group 1 and 19 pathogenic variants, 2 likely pathogenic variants, and 16 del-VUSs in Group 2. For each detected variant, we provide a description of its functional significance and geographical distribution. Conclusion: The present study offers extensive genetic data on the Russian population, critical for future genetic research and improved primary cancer prevention and genetic screening strategies. The proposed hybrid assessment algorithm streamlines variant prioritization and pathogenicity assessment and offers a reliable and verifiable way of identifying variants of uncertain significance that need to be manually reviewed.

Linking the composition of cryoconite prokaryotic communities in the Arctic, Antarctic, and Central Caucasus with their chemical characteristics.

Cryoconites are the deposits on the surface of glaciers that create specific ecological niches for the development of microorganism communities. The sediment material can vary in origin, structure, and nutrient content, creating local variations in the growth conditions. An additional factor of variability is the location of the glaciers, as they are found in different climatic zones in the high mountain regions and closer to the poles. Here, using the analysis of amplicon sequencing of the 16S rRNA gene, we studied the taxonomic composition of the prokaryotic communities from glaciers from remote regions, including the Arctic (Mushketova on the Severnaya Zemlya, IGAN in Polar Ural), Antarctic (Pimpirev on the Livingstone Island) and Central Caucasus (Skhelda and Garabashi) and connected it with the variation of the physicochemical characteristics of the substrate: pH, carbon, nitrogen, macro- and microelements. The cryoconite microbiomes were comprised of specific for this environment phyla (mostly Pseudomonadota, Cyanobacteria, Bacteroidota, Acidobacteriota, and Actinobacteriota), but each glacier had a unique taxonomic imprint. The core microbiome between regions was composed of only a few ASVs, among which the most likely globally distributed ones attributed to Polaromonas sp., Rhodoferax sp., Cryobacterium sp., and Hymenobacter frigidus. The WGSNA defined clusters of co-occurring ASVs between microbiomes, that significantly change their abundance corresponding with the variation of chemical parameters of cryoconites, but do not fully coincide with their regional separation. Thus, our work demonstrates that the chemical characteristics of the sediment material can explain the variation in the cryoconite prokaryotic community which is not always linked to geographic isolation.

Inductive detection of temperature-induced magnetization dynamics of molecular spin systems.

The development and technological applications of molecular spin systems require versatile experimental techniques to characterize and control their static and dynamic magnetic properties. In the latter case, bulk spectroscopic and magnetometric techniques, such as AC magnetometry and pulsed electron paramagnetic resonance, are usually employed, showing high sensitivity, wide dynamic range, and flexibility. They are based on creating a nonequilibrium state either by changing the magnetic field or by applying resonant microwave radiation. Another possible source of perturbation is a laser pulse that rapidly heats the sample. This approach has proven to be one of the most useful techniques for studying the kinetics and mechanism of chemical and biochemical reactions. Inspired by these works, we propose an inductive detection of temperature-induced magnetization dynamics as applied to the study of molecular spin systems and describe the general design and construction of a particular induction probehead, taking into account the constraints imposed by the cryostat and electromagnet. To evaluate the performance, several coordination compounds of VO2+, Co2+, and Dy3+ were investigated using low-energy pulses of a terahertz free electron laser of the Novosibirsk free electron laser facility as a heat source. All measured magnetization dynamics were qualitatively or quantitatively described using a proposed basic theoretical model and compared with the data obtained by alternating current magnetometry. Based on the results of the research, the possible scope of applications of inductive detection and its advantages and disadvantages in comparison with standard methods are discussed.

Gravitational Raman Scattering in Effective Field Theory: A Scalar Tidal Matching at O(G

We present a framework to compute amplitudes for the gravitational analog of the Raman process, a quasielastic scattering of waves off compact objects, in worldline effective field theory. As an example, we calculate third post-Minkowskian order [O(G^{3})], or two-loop, phase shifts for the scattering of a massless scalar field including all tidal effects and dissipation. Our calculation unveils two sources of the classical renormalization-group flow of dynamical Love numbers: a universal running independent of the nature of the compact object, and a running self-induced by tides. Restricting to the black hole case, we find that our effective field theory phase shifts agree exactly with those from general relativity, provided that the relevant static Love numbers are set to zero. In addition, we carry out a complete matching of the leading scalar dynamical Love number required to renormalize a universal short scale divergence in the S wave. Our results pave the way for systematic calculations of gravitational Raman scattering at higher post-Minkowskian orders.

Cognitive Impairment in Nonagenarians: Potential Metabolic Mechanisms Revealed by the Synergy of In Silico Gene Expression Modeling and Pathway Enrichment Analysis.

Previous studies examining the molecular and genetic basis of cognitive impairment, particularly in cohorts of long-living adults, have mainly focused on associations at the genome or transcriptome level. Dozens of significant dementia-associated genes have been identified, including APOE, APOC1, and TOMM40. However, most of these studies did not consider the intergenic interactions and functional gene modules involved in cognitive function, nor did they assess the metabolic changes in individual brain regions. By combining functional analysis with a transcriptome-wide association study, we aimed to address this gap and examine metabolic pathways in different areas of the brain of older adults. The findings from our previous genome-wide association study in 1155 older adults, 179 of whom had cognitive impairment, were used as input for the PrediXcan gene prediction algorithm. Based on the predicted changes in gene expression levels, we conducted a transcriptome-wide association study and functional analysis using the KEGG and HALLMARK databases. For a subsample of long-living adults, we used logistic regression to examine the associations between blood biochemical markers and cognitive impairment. The functional analysis revealed a significant association between cognitive impairment and the expression of NADH oxidoreductase in the cerebral cortex. Significant associations were also detected between cognitive impairment and signaling pathways involved in peroxisome function, apoptosis, and the degradation of lysine and glycan in other brain regions. Our approach combined the strengths of a transcriptome-wide association study with the advantages of functional analysis. It demonstrated that apoptosis and oxidative stress play important roles in cognitive impairment.

A Review of the Use of Extracellular Vesicles in the Treatment of Neonatal Diseases: Current State and Problems with Translation to the Clinic.

Neonatal disorders, particularly those resulting from prematurity, pose a major challenge in health care and have a significant impact on infant mortality and long-term child health. The limitations of current therapeutic strategies emphasize the need for innovative treatments. New cell-free technologies utilizing extracellular vesicles (EVs) offer a compelling opportunity for neonatal therapy by harnessing the inherent regenerative capabilities of EVs. These nanoscale particles, secreted by a variety of organisms including animals, bacteria, fungi and plants, contain a repertoire of bioactive molecules with therapeutic potential. This review aims to provide a comprehensive assessment of the therapeutic effects of EVs and mechanistic insights into EVs from stem cells, biological fluids and non-animal sources, with a focus on common neonatal conditions such as hypoxic-ischemic encephalopathy, respiratory distress syndrome, bronchopulmonary dysplasia and necrotizing enterocolitis. This review summarizes evidence for the therapeutic potential of EVs, analyzes evidence of their mechanisms of action and discusses the challenges associated with the implementation of EV-based therapies in neonatal clinical practice.

Development of a bottom-up coarse-grained model for interactions of lipids with TiO 2 nanoparticles.

Understanding interactions of inorganic nanoparticles with biomolecules is important in many biotechnology, nanomedicine, and toxicological research, however, the size of typical nanoparticles makes their direct modeling by atomistic simulations unfeasible. Here, we present a bottom-up coarse-graining approach for modeling titanium dioxide (TiO 2 ) nanomaterials in contact with phospholipids that uses the inverse Monte Carlo method to optimize the effective interactions from the structural data obtained in small-scale all-atom simulations of TiO 2 surfaces with lipids in aqueous solution. The resulting coarse-grained models are able to accurately reproduce the structural details of lipid adsorption on different titania surfaces without the use of an explicit solvent, enabling significant computational resource savings and favorable scaling. Our coarse-grained simulations show that small spherical TiO 2 nanoparticles ( r = 2 nm) can only be partially wrapped by a lipid bilayer with phosphoethanolamine headgroups, however, the lipid adsorption increases with the radius of the nanoparticle. The current approach can be used to study the effect of the size and shape of TiO 2 nanoparticles on their interactions with cell membrane lipids, which can be a determining factor in membrane wrapping as well as the recently discovered phenomenon of nanoquarantining, which involves the formation of layered nanomaterial-lipid structures.

Synthesis and photoinduced behavior of DPP-anchored nitronyl nitroxides: a multifaceted approach.

Understanding and controlling spin dynamics in organic dyes is of significant scientific and technological interest. The investigation of 2,5-dihydropyrrolo[4,3-c]pyrrolo-1,4-dione derivatives (DPPs), one of the most widely used dyes in many fields, has so far been limited to closed-shell molecules. We present a comprehensive joint experimental and computational study of DPP derivatives covalently linked to two nitronyl nitroxide radicals (DPPTh-NN2). Synthesis, single crystal X-ray diffraction study, photophysical properties, magnetic properties established using steady-state and pulse EPR, fast spin dynamics, and computational modelling using density functional theory and ab initio methods of electronic structure and spectroscopic properties of DPPTh-NN2 are presented. The single-crystal X-ray diffraction analysis of DPPTh-NN2 and computational modeling of its electronic structure suggest that effective conjugation along the backbone leads to noticeable spin-polarization transfer. Calculations using ab initio methods predict a weak exchange interaction of radical centers through a singlet ground state of DPPTh with a small singlet-triplet splitting (ΔEST) of about 25 cm-1 (∼0.07 kcal mol-1). In turn, a strong ferromagnetic exchange interaction between the triplet state of DPPTh chromophore and nitronyl nitroxides (with J ∼ 250 cm-1) was predicted.

Machine Learning-Based Decision-Making in Geriatrics: Aging Phenotype Calculator and Survival Prognosis.

Aging is a natural process with varying effects. As we grow older, our bodies become more susceptible to aging-associated diseases. These diseases, individually or collectively, lead to the formation of distinct aging phenotypes. Identifying these aging phenotypes and understanding the complex interplay between coexistent diseases would facilitate more personalized patient management, a better prognosis, and a prolonged lifespan. Many studies distinguish between successful aging and frailty. However, this simple distinction fails to reflect the diversity of underlying causes. In this study, we sought to establish the underlying causes of frailty and determine the patterns in which these causes converge to form aging phenotypes. We conducted a comprehensive geriatric examination, cognitive assessment, and survival analysis of 2,688 long-living adults (median age = 92 years). The obtained data were clustered and used as input data for the Aging Phenotype Calculator, a multiclass classification model validated on an independent dataset of 96 older adults. The accuracy of the model was assessed using the receiver operating characteristic curve and the area under the curve. Additionally, we analyzed socioeconomic factors that could contribute to specific aging patterns. We identified five aging phenotypes: non-frailty, multimorbid frailty, metabolic frailty, cognitive frailty, and functional frailty. For each phenotype, we determined the underlying diseases and conditions and assessed the survival rate. Additionally, we provided management recommendations for each of the five phenotypes based on their distinct features and associated challenges. The identified aging phenotypes may facilitate better-informed decision-making. The Aging Phenotype Calculator (ROC AUC = 92%) may greatly assist geriatricians in patient management.

Mitochondrial Dysfunction in Dopaminergic Neurons Derived from Patients with LRRK2- and SNCA-Associated Genetic Forms of Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease. Some cases of PD may be caused by genetic factors, among which mutations in the LRRK2 and SNCA genes play an important role. To develop effective neuroprotective strategies for PD, it is important to diagnose the disease at the earliest stages of the neurodegenerative process. Therefore, the detection of diagnostic and prognostic markers of Parkinson's disease (PD) is an urgent medical need. Advances in induced pluripotent stem cell (iPSC) culture technology provide new opportunities for the search for new biomarkers of PD and its modeling in vitro. In our work, we used a new technology for multiplex profiling of gene expression using barcoding on the Nanostring platform to assess the activity of mitochondrial genes on iPSC-derived cultures of dopaminergic neurons obtained from patients with LRRK2- and SNCA-associated genetic forms PD and a healthy donor. Electron microscopy revealed ultrastructural changes in mitochondria in both LRRK2 and SNCA mutant cells, whereas mitochondria in cells from a healthy donor were normal. In a culture with the SNCA gene mutation, the ratio of the area occupied by mitochondria to the total area of the cytoplasm was significantly lower than in the control and in the line with the LRRK2 gene mutation. Transcriptome analysis of 105 mitochondria proteome genes using the Nanostring platform revealed differences between the diseased and normal cells in the activity of genes involved in respiratory complex function, the tricarboxylic acid cycle, ATP production, mitochondria-endoplasmic reticulum interaction, mitophagy, regulation of calcium concentration, and mitochondrial DNA replication.

Coarse-Grained Modeling Using Neural Networks Trained on Structural Data.

We propose a method of bottom-up coarse-graining, in which interactions within a coarse-grained model are determined by an artificial neural network trained on structural data obtained from multiple atomistic simulations. The method uses ideas of the inverse Monte Carlo approach, relating changes in the neural network weights with changes in average structural properties, such as radial distribution functions. As a proof of concept, we demonstrate the method on a system interacting by a Lennard-Jones potential modeled by a simple linear network and a single-site coarse-grained model of methanol-water solutions. In the latter case, we implement a nonlinear neural network with intermediate layers trained by atomistic simulations carried out at different methanol concentrations. We show that such a network acts as a transferable potential at the coarse-grained resolution for a wide range of methanol concentrations, including those not included in the training set.

Filtration-based technologies for isolation, purification and analysis of extracellular vesicles.

The involvement of extracellular vesicles (EVs) in cellular communication with multifactorial and multifaceted biological activity has generated significant interest, highlighting their potential diagnostic and therapeutic applications. EVs are found in nearly all biological fluids creating a broad spectrum of where potential disease markers can be found for liquid biopsy development and what subtypes can be used for treatment of diseases. Complexity of biological fluids has generated a variety of different approaches for EV isolation and identification that may in one way or another be most optimal for research studies or clinical use. Each approach has its own advantages and disadvantages, significance of which can be evaluated depending on the end goal of the study. One of the methods is based on filtration which has received attention in the past years due its versatility, low cost and other advantages. Introduction of different approaches for EV capture and analysis that are based on filtration gave rise to new subcategories of filtration techniques which are presented in this overview. Miniaturization and combination of filtration-based approaches with microfluidics is also highlighted due its future prospects in healthcare, especially point-of-need technologies.

GWAS reveals genetic basis of a predisposition to severe COVID-19 through in silico modeling of the FYCO1 protein.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is heavily reliant on its natural ability to "hack" the host's genetic and biological pathways. The genetic susceptibility of the host is a key factor underlying the severity of the disease. Polygenic risk scores are essential for risk assessment, risk stratification, and the prevention of adverse outcomes. In this study, we aimed to assess and analyze the genetic predisposition to severe COVID-19 in a large representative sample of the Russian population as well as to build a reliable but simple polygenic risk score model with a lower margin of error. Another important goal was to learn more about the pathogenesis of severe COVID-19. We examined the tertiary structure of the FYCO1 protein, the only gene with mutations in its coding region and discovered changes in the coiled-coil domain. Our findings suggest that FYCO1 may accelerate viral intracellular replication and excessive exocytosis and may contribute to an increased risk of severe COVID-19. We found significant associations between COVID-19 and LZTFL1, FYCO1, XCR1, CCR9, TMLHE-AS1, and SCYL2 at 3p21.31. Our findings further demonstrate the polymorphic nature of the severe COVID-19 phenotype.

Effects of Zwitterions on Structural Anomalies in Ionic Liquid Glasses Studied by EPR.

Ionic liquids (ILs) form a variety of nanostructures due to their amphiphilic nature. Recently, unusual structural phenomena have been found in glassy ILs near their glass transition temperatures; however, in all studied cases, IL cations and anions were in the form of separate moieties. In this work, we investigate for the first time such structural anomalies in zwitterionic IL glasses (ZILs), where the cation and anion are bound in a single molecule. Such binding reasonably restricts mutual diffusion of cations and anions, leading to modification of nano-ordering and character of structural anomalies in these glassy nanomaterials, as has been investigated using Electron Paramagnetic Resonance (EPR) spectroscopy. In particular, the occurrence of structural anomalies in ZIL glasses was revealed, and their characteristic temperatures were found to be higher compared to common ILs of a similar structure. Altogether, this work broadens the scope of structural anomalies in ionic liquid glasses and indicates new routes to tune their properties.

Verification of Data from Supersensitive Detector of Hydrosphere Pressure Variations.

The paper describes experimental research and the results of these studies carried out in various bays of the Primorsky Territory of Russia using a supersensitive detector of hydrosphere pressure variations and a sound velocity profiler with pressure and temperature sensors. In all experiments, instruments, rigidly fixed to each other, were placed on the bottom at a depth of up to 10 m. Comparison of in-situ data from these instruments allowed us to experimentally calculate the coefficient of data conversion of the supersensitive detector of hydrosphere pressure variations when registering sea waves with periods ranging from several seconds to tens of minutes.

Neutron scattering study of polyamorphic THF·17(H2O) - toward a generalized picture of amorphous states and structures derived from clathrate hydrates.

From crystalline tetrahydrofuran clathrate hydrate, THF-CH (THF·17H2O, cubic structure II), three distinct polyamorphs can be derived. First, THF-CH undergoes pressure-induced amorphization when pressurized to 1.3 GPa in the temperature range 77-140 K to a form which, in analogy to pure ice, may be called high-density amorphous (HDA). Second, HDA can be converted to a densified form, VHDA, upon heat-cycling at 1.8 GPa to 180 K. Decompression of VHDA to atmospheric pressure below 130 K produces the third form, recovered amorphous (RA). Results from neutron scattering experiments and molecular dynamics simulations provide a generalized picture of the structure of amorphous THF hydrates with respect to crystalline THF-CH and liquid THF·17H2O solution (∼2.5 M). Although fully amorphous, HDA is heterogeneous with two length scales for water-water correlations (less dense local water structure) and guest-water correlations (denser THF hydration structure). The hydration structure of THF is influenced by guest-host hydrogen bonding. THF molecules maintain a quasiregular array, reminiscent of the crystalline state, and their hydration structure (out to 5 Å) constitutes ∼23H2O. The local water structure in HDA is reminiscent of pure HDA-ice featuring 5-coordinated H2O. In VHDA, the hydration structure of HDA is maintained but the local water structure is densified and resembles pure VHDA-ice with 6-coordinated H2O. The hydration structure of THF in RA constitutes ∼18 H2O molecules and the water structure corresponds to a strictly 4-coordinated network, as in the liquid. Both VHDA and RA can be considered as homogeneous.

A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems.

With the increasing severity of environmental problems, low-carbon development has become an inevitable choice. Nowadays, low-carbon green sustainable development is influenced by a variety of factors such as social, environmental, technological, and economic development levels, making its development complex, which in turn imposes challenges on decision-makers. In this context, the application of multi-criteria decision-making (MCDM) in different areas of sustainable development engineering has become a hot topic. Although many reviews of MCDM techniques already exist, there is a lack of holistic review efforts on MCDM in the field of low-carbon transport and green logistics. Considering these shortcomings in the state of the art, this paper systematically reviews more than 190 papers from 2010 to 2022, constructs a general structure of MCDM techniques for this research topic, provides a comprehensive review and analysis of it, and clarifies the current practices. Furthermore, future directions for the development of MCDM techniques for green logistics and low-carbon transportation systems are presented as well.

Mortality and survival in nonagenarians during the COVID-19 pandemic: Unstable equilibrium of aging.

Introduction: Aging puts the human body under an immense stress and makes it extremely susceptible to many diseases, often leading to poor outcomes and even death. Long-living individuals represent a unique group of people who withstood the stress of time and offer an abundance of information on the body's ability to endure the pressure of aging. In this study, we sought to identify predictors of overall one-year mortality in 1641 long-living individuals. Additionally, we analyzed risk factors for COVID-19-related morality, since statistics demonstrated an extreme vulnerability of older adults. Methods: We conducted a two-stage evaluation, including a comprehensive geriatric assessment for major aging-associated: frailty, cognitive impairment, frontal lobe dysfunction, chronic pain, anxiety, risk of falls, sensory deficit, depression, sarcopenia, risk of malnutrition, fecal and urinary incontinence, dependence in Activities of Daily Living, dependence in Instrumental Activities of Daily Living, polypragmasia, and orthostatic hypotension; extensive blood testing, a survey, and a one-year follow-up interview. Results: The most reliable predictors of overall mortality were cognitive impairment, malnutrition, frailty, aging-associated diseases and blood markers indicating malnutrition-induced metabolic dysfunctions (decreased levels of protein fractions, iron, 25-hydroxyvitamin D, and HDL), and aging biomarkers, such as IGF-1 and N-terminal pro b-type natriuretic peptide. In post-COVID 19 participants, the most significant mortality predictors among geriatric syndromes were depression, frontal lobe dysfunction and frailty, and similar to overall mortality blood biomarkers - 25-hydroxyvitamin D, IGF-1, HDL as well as high white blood cell, neutrophils counts and proinflammatory markers. Based on the results, we built a predictive model of overall mortality in the long-living individuals with f-score=0.76. Conclusion: The most sensitive and reliable predictors of mortality were modifiable. This is another evidence of the critical importance of proper geriatric care and support for individuals in their "golden years". These results could facilitate geriatric institutions in their pursuit for providing improved care and could aid physicians in detecting early signs of potentially deadly outcomes. Additionally, our findings could be used in developing day-to-day care guidelines, which would greatly improve prevention statistics.

Vanishing of Black Hole Tidal Love Numbers from Scattering Amplitudes.

We extract the black hole (BH) static tidal deformability coefficients (Love numbers) and their spin-0 and spin-1 analogs by comparing on-shell amplitudes for fields to scatter off a spinning BH in the worldline effective field theory and in general relativity. We point out that the general relativity amplitudes due to tidal effects originate entirely from the BH potential region. Thus, they can be separated from gravitational nonlinearities in the wave region, whose proper treatment requires higher order effective field theory loop calculations. In particular, the elastic scattering in the near field approximation is produced exclusively by tidal effects. We find this contribution to vanish identically, which implies that the static Love numbers of Kerr BHs are zero for all types of perturbations. We also reproduce the known behavior of scalar Love numbers for higher-dimensional BHs. Our results are manifestly gauge invariant and coordinate independent, thereby providing a valuable consistency check for the commonly used off-shell methods.

Environment-oriented disassembly planning for end-of-life vehicle batteries based on an improved northern goshawk optimisation algorithm.

Due to environmental pollution and resource shortages, the electric vehicle industry has been developing swiftly, and the market demand for batteries, as an essential part of electric vehicles, has also surged. Proper disassembly of end-of-life vehicle batteries (ELV batteries) is necessary to achieve the integrity and closure of their life cycle, promote the development of green remanufacturing, effectively reduce the pollution of the environment caused by metal ion leakage, and reduce people's dependence on natural resources to a certain extent. To schedule the disassembly operations of ELV batteries more rationally and further promote their disassembly quality and efficiency, this paper proposes a dual-objective disassembly sequence planning (DSP) optimisation model, which aims to minimise the hazard index and energy cost during ELV battery disassembly operations. Since the proposed model is a complex NP-hard optimisation problem, this study develops an efficient metaheuristic algorithm for solving this model based on the northern goshawk optimisation algorithm. The main algorithm adds two types of discrete recombination operators and a local search operator. At the same time, the predatory behaviour of the goshawk is optimised by combining the characteristics of the disassembly sequence planning problem to improve its performance. Finally, the disassembly of the battery of a Tesla Model 1 is used as a case study to demonstrate the effectiveness and feasibility of the proposed method.