Biological Relationships Research Articles

Relationship of Social Deprivation Among Living Kidney Donor–Recipient Pairs

Background. Living kidney transplant is the most effective renal replacement therapy for patients with end-stage kidney disease. Community-level factors contribute to pervasive socioeconomic and racial disparities in access to living donor kidney transplantation. Little is known about social and environmental conditions between living donors and recipients. Further understanding of these relationships may enhance opportunities for transplantation. Methods. From 2010 to 2020, 59 575 living kidney donor–recipient pairs (≥18 y old) were identified using the Scientific Registry of Transplant Recipients. Living donors and recipients were geocoded to area-level social deprivation index (SDI). The primary outcome was difference between recipient and donor SDI. We used multivariable logistic regression to examine recipient and donor characteristics association with residence in different SDI communities. Results. Living kidney donation occurs across all strata of social deprivation; including when donors, recipients or both reside in more disadvantaged communities. Donor–recipient race combination and biological relationship are associated with differences in SDI. When compared with White recipients of White donors, Black and Hispanic recipients were more likely to reside in more disadvantaged areas (odds ratio = 2.41 [2.19-2.66] and 1.97 [1.78-2.19]). Recipients in anonymous and paired donations were more likely to reside in areas of more disadvantage than their donors (odds ratio = 1.27 [1.15-1.40] and 1.32 [1.23-1.41] compared with biological); attenuating socioeconomic disparities in access to living donor transplantation. Conclusions. Findings illustrate the social and environmental relationships between living kidney donor–recipient pairs that are important to develop targeted approaches and address barriers to living kidney transplantation. Best practices from areas of high deprivation with successful living kidney transplantation can be shared.

A Comprehensive Review of the Effects of Glycemic Carbohydrates on the Neurocognitive Functions Based on Gut Microenvironment Regulation and Glycemic Fluctuation Control.

Improper glycemic carbohydrates (GCs) consumption can be a potential risk factor for metabolic diseases such as obesity and diabetes, which may lead to cognitive impairment. Although several potential mechanisms have been studied, the biological relationship between carbohydrate consumption and neurocognitive impairment is still uncertain. In this review, the main effects and mechanisms of GCs' digestive characteristics on cognitive functions are comprehensively elucidated. Additionally, healthier carbohydrate selection, a reliable research model, and future directions are discussed. Individuals in their early and late lives and patients with metabolic diseases are highly susceptible to dietary-induced cognitive impairment. It is well known that gut function is closely related to dietary patterns. Unhealthy carbohydrate diet-induced gut microenvironment disorders negatively impact cognitive functions through the gut-brain axis. Moreover, severe glycemic fluctuations, due to rapidly digestible carbohydrate consumption or metabolic diseases, can impair neurocognitive functions by disrupting glucose metabolism, dysregulating calcium homeostasis, oxidative stress, inflammatory responses, and accumulating advanced glycation end products. Unstable glycemic status can lead to more severe neurological impairment than persistent hyperglycemia. Slow-digested or resistant carbohydrates might contribute to better neurocognitive functions due to stable glycemic response and healthier gut functions than fully gelatinized starch and nutritive sugars.

Theoretical characterization and biological activity investigation of indirubins, cyclin dependent kinases inhibitors

Up to now, significant research efforts have been directed towards investigating indirubin and its derivatives as potential candidates for developing new compounds with multiple biological activities. In the present work, natural indirubin and numerous of its chemical derivatives referred to as indirubins have been investigated computationally using DFT method with the B3LYP/6-311 + G(d,p) level of theory, in order to reveal structure- biological activity relationship. We started with a structural properties description. Results analysis indicated that extra interaction sites were provided through the set of substitutions in compounds (1): Indirubin-3’-monoxime, (2): Indirubin-5-sulfonic acid, (3): 5-Nitro-indirubinoxime, (4): 5’-OH-5-nitro-indirubinoxime (AGM130), (5): 7-Bromo-5’-carboxyindirubin-3’-oxime, and (6): 7 BIO and consequently, extra hydrogen bonds may be formed with the active sites of molecular targets, such as GSK-3, CDKs, and Aurora kinases, as well as the aryl hydrocarbon receptor. Subsequently, to get more information on the electronic properties of indirubin and its analogues, HOMO, LUMO, Egap, and further electronic parameters were carried out. The indirubin derivatives showed an easier interaction with its environment than indirubin, the parent compound. The UV-Visible spectra of indirubin and compounds 1–6 were also produced using TD-DFT with B3LYP functional and 6-311 + G(2d,p) basis set. The relationship between absorption and chemical structure is discussed. Two phototoxic brominated compounds showed important absorption spectra modifications. It was also found that the main absorption bands of all compounds derived from π→π*(HOMO→LUMO) transitions. Communicated by Ramaswamy H. Sarma

The interplay between White Matter Hyperintensities and regional β‐Amyloid on Defaults Mode Network

AbstractBackgroundCerebral small vessel ischemic disease (SVID) has been suggested to contribute to the pathogenesis of Alzheimer’s disease (AD). Yet, the mechanistic role of SVID in potentially contributing to AD pathology is unclear. The main objective of this study was to test the hypothesis that axonal SVID influences amyloid β (Aβ) levels within connected default mode network (DMN) tracts and cortical regions in cognitively unimpaired older adults.MethodRegional Standard Uptake Value ratios (SUVr) from Aβ‐PET, and white matter hyperintensity (WMH) volumes from 3D MRI FLAIR images were analyzed across a sample of 72 clinically‐unimpaired (Mini‐Mental State Examination (MMSE) ≥ 26), aged participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI3). The association of WMH volumes in major fiber tracts projecting from cortical DMN regions and Aβ‐PET SUVr in the connected cortical DMN regions were analyzed using linear regression models adjusted for age, sex, ApoE and total brain volumes.ResultThe regression analyses demonstrate that increased WMH volumes in the superior longitudinal fasciculus (SLF) was associated with increased regional SUVr in the inferior parietal lobule (IPL) (p = 0.011).ConclusionThe findings suggest that the relation between Aβ parietal cortex is partially driven by SVID in downstream axonal pathways in pAD. The biological relationships and interplay between Aβ and white matter microstructure alterations that precede overt WMH development across the continuum of AD progression warrants further study.

Predictive Modeling Leveraging Electronic Health Records Identifies Early Predictors of Alzheimer’s Disease

AbstractBackgroundAlzheimer’s Disease (AD) is a neurodegenerative disorder of aging that is difficult to diagnose. Electronic health records (EHR) are a valuable source of longitudinal data for disease phenotyping and prediction. Knowledge graphs, such as SPOKE (Scalable Precision Medicine Open Knowledge Engine), help derive biological meaning from phenotypic data. Here we utilize machine learning on EHR to predict and better understand AD.MethodAD patients and controls were identified from the UCSF EHR. The index time was identified as the first dementia diagnosis or drug record among the AD patients, and 1 year before the last visit among controls. Controls matched on demographics and visit‐related factors were identified at five time points relative to the index time (‐7years, ‐5years, ‐3years, ‐1year, and ‐1day) at a 1:8 ratio. Random forest (RF) classification models for AD were trained utilizing clinical data (conditions, drugs, abnormal measurements) and evaluated with bootstrapped AUROCs on a 30% held‐out set. Top features mapped to phecodes were ranked based on average impurity decrease. Networks were created from SPOKE knowledge graph by identifying shortest paths between top 25 model features and AD, and associations were validated with genetic colocalization analysis on shared variants in the UK Biobank.ResultFrom the UCSF EHR, 749 AD patients and 250,545 Controls were identified with clinical concepts 7 years prior to each individual’s index time. RF trained on AD and matched control individuals’ data performed with AUROC of 0.58 (‐7years) to 0.77 (‐1day). Top phecode features from models include those that are important earlier (osteoarthritis, allergic rhinitis), progress in importance closer to index time (dizziness, vitamin D deficiency, memory loss), and persist in importance throughout time (hyperlipidemia) (Figure 1). Knowledge networks highlight shared biological relationships between top features (e.g., hyperlipidemia, osteoporosis), including identification of relevant genes (e.g., APOE, PSEN 1/2, INS, AKT1, HFE) (Figure 2). Colocalization analysis of LDL cholesterol and AD confirms a commonly associated SNP in APOE.ConclusionEHR together with knowledge networks provide an opportunity to identify early clinical predictors of AD diagnosis, with utility for early intervention, hypothesis generation and biological insight.

Abstract C052: Circumventing EGFR inhibitor resistance in NSCLC using transomics

Abstract Drug discovery continues to be refined to enhance efficiency, yet >80% of new drugs fail in the clinical trial stage. It is apparent that a novel approach is needed. COMPASS is a transomic analysis platform that integrates data across multiple omics layers; genomics, transcriptomics, proteomics, and phosphoproteomics. This analysis allows us to interpret complex biological relationships and provide a functional map of the biochemical drivers of disease. We have used EGFR inhibitor (EGFRi) resistance as a case study. EGFRi have been successful in treating non-small cell lung cancer (NSCLC) patients with activating EGFR mutations (EGFRm), however, this success is temporary. The emergence of resistance is a problem and limits long-term treatment for many patients, even with osimertinib, a third-generation inhibitor and current standard of care. As resistance to EGFRi occurs through mutations in EGFR we adopted a novel strategy using COMPASS to identify and validate alternative, non-EGFR targets that could potentially recapitulate the pharmacological effects of EGFRi in EGFRm NSCLC. The transomic signatures of EGFRi were generated using a NSCLC cell line containing the activating mutation ex19del. The drug concentration required to inhibit cell growth (IC50) was first determined. For transomic analyses, cells were incubated for 24 h with the drug IC50 concentration and harvested for genomic, transcriptomic, proteomic, and phosphoproteomic analyses. We have previously shown transomics analysis can differentiate between EGFRi that have been designed to treat EGFRm NSCLC with the transomics signature for osimertinib being significantly different from comparator drugs that failed during drug development. We employed our proprietary target prioritization algorithm to further analyze the omics data to identify and rank novel targets. Targets were then filtered to select those with an available pharmacological tool compound (PTC). The PTCs were used to evaluate the targets in a xenograft model of osimertinib resistant NSCLC, PC9-Del19/T790M/C797S; which contains the activating EGFR mutation (ex19del), and two mutations conferring EGFRi resistance, T790M gatekeeper mutation, and C797S which confers resistance to osimertinib. Of the targets tested 82% (9/11) were associated with >25% inhibition of tumor growth. Furthermore, 36% (4/11) targets were associated with >50% inhibition by the PTCs, with inhibition of one target giving >85% inhibition resulting in stasis of tumor growth. Future work will investigate targets using gene silencing, as PTCs were not available for many of the novel high-ranked targets. These data show that using COMPASS transomic analysis, it is possible to identify novel drug targets to treat difficult-to-treat cancers such as EGFRi-resistant NSCLC and provides validation of this discovery platform by disease-relevant in vivo pharmacology. Further validation studies are ongoing against other difficult-to-treat cancers to identify novel clinical targets and drug candidates. Citation Format: Christopher J Nicholson, Samuel J Roth, Arudhir Singh, Caitlin Brown, Simon P Fricker, Jon Hu, Samantha Dale Strasse. Circumventing EGFR inhibitor resistance in NSCLC using transomics [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C052.

Symbiotic Relationship of Comasterschlegelii (Crinoidea: Comatulidae) and Gymnolophus obscura (Ophiuroidea: Ophiotrichidae) Derived from Stable Isotope and Fatty Acid Analyses.

Coral reef community exhibits high species diversity and a broad range of biological relationships, including widespread symbiosis and complex food utilization patterns. In our study, we investigated the symbiotic relationship between the commonly crinoid host Comaster schlegelii and its ophiuroid obligatory symbiont Gymnolophus obscura. Using a combination of fatty acid biomarkers and stable isotopic compositions, we explored differences in their organic matter utilization strategies and nutritional relationships. The result of stable isotopes revealed that G. obscura had higher δ15N values than its crinoid host. Particulate organic matter and phytoplankton were identified as the primary food sources for both species, however C. schlegelii showed a higher proportional contribution from benthic microalgae. Fatty acid markers showed that C. schlegelii was more dependent on benthic microalgae such as diatoms, and less on debritic organic matter and bacteria than G. obscura. Elevated δ15N values of G. obscura and similar food source contribution rates between the host and symbiont suggest that ophiuroid feeds on materials filtered by crinoids and have similar diet to the host. Our results provide insights into the symbiotic patterns of crinoids and ophiuroids, while also supplying foundational data on how symbiotic reef species select organic matter utilization strategies to adapt to their environment.

Natural and anthropogenic organic brominated compounds in the southwestern Atlantic ocean: Bioaccumulation in coastal and oceanic dolphin species

Marine pollution is considered a current driver of change in the oceans and despite the urgency to develop more studies, there is limited information in the southern hemisphere. This study aimed to analyze the levels and profiles of natural (MeO-PBDEs) and anthropogenic (BFRs: PBDEs, HBB, PBEB) organic brominated compounds in adipose tissue of two species of dolphins with different distribution and trophic requirements from the Southwestern Atlantic Ocean; the short-beaked common dolphin (Delphinus delphis) and the Fraser's dolphin (Lagenodelphis hosei). In addition, we aim to investigate maternal transfer and biological pattern relationship (sex, age, sexual maturity) in short-beaked common dolphin bioaccumulation. The levels of both groups of contaminants were in the same order of magnitude as those reported for other marine mammals on both a regional and global scale. BFRs profiles were dominated by BDE 28 and BDE 47 in short-beaked common dolphin and Fraser's dolphin, respectively, whereas 2-MeO-BDE 68 was the most abundant natural compound in both species. Evidence of maternal transfer, temporary increase in BDE 154 levels and no influence of sex, age, or sexual maturity on brominated compound concentration was observed in short-beaked common dolphin. This study fills a gap in the knowledge of the Southwestern Atlantic Ocean providing new information on emerging organic pollutants bioavailability for dolphins and, therefore, for the different trophic webs. In addition, it serves as a baseline for further contamination assessments.

An in vitro coculture approach to study the interplay between dental pulp cells and Streptococcus mutans.

To develop a new coculture system that allows exposure of dental pulp cells (DPCs) to Streptococcus mutans and dentine matrix proteins (eDMP) to study cellular interactions in dentine caries. Dental pulp cells and S. mutans were cocultured with or without eDMP for 72 h. Cell proliferation and viability were assessed by cell counting and MTT assays, while bacterial growth and viability were determined by CFU and LIVE/DEAD staining. Glucose catabolism and lactate excretion were measured photometrically as metabolic indicators. To evaluate the inflammatory response, the release of cytokines and growth factors (IL-6, IL-8, TGF-β1, VEGF) was determined by ELISA. Non-parametric statistical analyses were performed to compare all groups and time points (Mann-Whitney U test or Kruskal-Wallis test; α = .05). While eDMP and especially S. mutans reduced the number and viability of DPCs (p ≤ .0462), neither DPCs nor eDMP affected the growth and viability of S. mutans during coculture (p > .0546). The growth of S. mutans followed a common curve, but the death phase was not reached within 72 h. S. mutans consumed medium glucose in only 30 h, whereas in the absence of S. mutans, cells were able to catabolize glucose throughout 72 h, resulting in the corresponding amount of l-lactate. No change in medium pH was observed. S. mutans induced IL-6 production in DPCs (p ≤ .0011), whereas eDMP had no discernible effect (p > .7509). No significant changes in IL-8 were observed (p > .198). TGF-β1, available from eDMP supplementation, was reduced by DPCs over time. VEGF, on the other hand, was increased in all groups during coculture. The results show that the coculture of DPCs and S. mutans is possible without functional impairment. The bacterially induced stimulation of proinflammatory and regenerative cytokines provides a basis for future investigations and the elucidation of molecular biological relationships in pulp defence against caries.

Non-smooth competitive systems and complex dynamics induced by linearly dependent feedback control

Competition is a common biological relationship in nature, especially for some fish species. For different situations and different purposes in a two populations competitive system, we propose three novel mathematical models of two-fish competition with linear correlation feedback control. The first scenario considers undesirable competition in the system, and the purpose of control is to avoid the extinction of the inferior population. By discussing the existence and stability of the order-1 periodic solution, an effective method is provided for the realization of the fixed-period control and stabilization of the system, and the parameter optimization design is realized with the goal of minimizing the control cost. The second scenario considers the coexistence of two fish populations, and the purpose of control is transformed into harvesting of both populations. By analyzing the existence of the order-1 and order-2 periodic solutions, the dynamic characteristics and complexity of the control system are enriched, and the optimal design of parameters is realized with the goal of maximizing the economic benefits. The third scenario considers the phenomenon of equal competition between the two populations. The purpose of control is to avoid the extinction of one of the populations due to the large number of one population. By analyzing the existence of the order-1 and order-2 periodic solutions, the dynamical characteristics of the system are further enriched. Finally, numerical simulations are carried out for the three scenarios to illustrate the theoretical results and the feasibility of the control. The control strategy based on linear dependent feedback proposed in this paper provides an effective way to realize the coexistence of two populations competing systems.

67 Leveraging Ai/Ml to Address Critical Challenges in Livestock Research

Abstract In recent years the scientific community has witnessed extraordinary growth in the development and application of techniques from Artificial Intelligence/Machine Learning (AI/ML) to address pressing societal challenges. Contrary to more traditional linear models, some AI/ML approaches suffer from a general lack of interpretability and in disciplines/industries where understanding biological relationships among predictor and predictand is important, utility and even trust in resulting predictions is a potential issue. Regardless, the livestock research community has shown considerable interest in the utilization of AI/ML methods, coupled with advanced cyberinfrastructure, to address large-scale data challenges from the use of sensors and internet of things (IoT) to solve problems related to animal identification, welfare, behavior detection, genome-to-phenome, and data quality concerns from large-scale non-research sources (to name a few). The effective application of AI/ML to problems in livestock research requires an understanding of what methods are available; the benefits and pitfalls of these methods; what resources are required including data and metadata, cyberinfrastructure, and interdisciplinary skills; best practices; and how AI/ML methods compare with alternative approaches. Awareness of the above-mentioned items is critical to avoid reproducibility issues and the possibility of using overly complex models for simple tasks. Each of these topics will be addressed, including a gentle introduction to AI/ML, in the context of examples from livestock research and a view towards future developments and application.

SLASER and PRESS perform similarly at revealing metabolite-age correlations at 3 T.

To compare the respective ability of PRESS and sLASER to reveal biological relationships, using age as a validation covariate at 3 T. MRS data were acquired from 102 healthy volunteers using PRESS and sLASER in centrum semiovale and posterior cingulate cortex (PCC). Acquisition parameters included TR/TE = 2000/30 ms, 96 transients, and 2048 datapoints sampled at 2 kHz. Spectra were analyzed using Osprey. SNR, FWHM linewidth of total creatine, and metabolite concentrations were extracted. A linear model was used to compare SNR and linewidth. Paired t-tests were used to assess differences in metabolite measurements between PRESS and sLASER. Correlations were used to evaluate the relationship between PRESS and sLASER metabolite estimates, as well as the strength of each metabolite-age relationship. Coefficients of variation were calculated to assess inter-subject variability in each metabolite measurement. SNR and linewidth were significantly higher (p < 0.01) for sLASER than PRESS in PCC. Paired t-tests showed significant differences between PRESS and sLASER in most metabolite measurements. PRESS-sLASER measurements were significantly correlated (p < 0.05) for most metabolites. Metabolite-age relationships were consistently identified using both methods. Similar coefficients of variation were observed for most metabolites. The study results suggest strong agreement between PRESS and sLASER in identifying relationships between brain metabolites and age in centrum semiovale and PCC data acquired at 3 T. sLASER is technically desirable due to the reduced chemical shift displacement artifact; however, PRESS performed similarly in homogeneous brain regions at clinical field strength.

Flowering shift due to climate warming in cool temperate Japan

Biological processes can be disrupted by global climate warming, causing phenological mismatching and mistiming. On Hokkaido Island (Japan), which has a cool temperate climate, warmer and shorter winters affect plant flowering phenology. Rapid changes in flowering phenology suggest that shorter winters will result in earlier flowering. Flowering phenology patterns were recorded for nine years (2013–2021), and nine species were selected for analysis. I analyzed climate variables that affect plant flowering phenology, including annual changes in aerial temperature, snow depth, and snowmelt timing. The results indicated that winters are getting shorter and that the accumulated degree hours of aerial temperature have not significantly changed over nine years for these plant species except one species. Plant responses to these changes differed between species, with the first flowering day occurring earlier (1.5–1.8 days per year) for two species, although that of the other seven not. If climate warming continues, it will have diverse, complex, and unpredictable effects on various individual organisms and biological relationships among species. Detailed studies are needed to link climate change predictions to the predicted degree of mismatch in species interactions and networks.

New insights on Celtic migration in Hungary and Italy through the analysis of non-metric dental traits.

The Iron Age is characterized by an extended interweaving of movements by Celts in Europe. Several waves of Celts from Western and Central Europe migrated southeast and west from the core area of the La Téne culture (between Bourgogne and Bohemia). Through the analysis of non-metric dental traits, this work aims to understand the biological relationship among Celtic groups arrived in Italy and the Carpathian Basin, as well as between local populations and Celtic newcomers. A total of 10 non-metric dental traits were analyzed to evaluate biological affinities among Celts (Sopron-Krautacker and Pilismarót-Basaharc) and Scythians-related populations from Hungary (Tápiószele), Celts from continental Europe (Switzerland and Austria), two Iron Age Etruscan-Celtic sites from northern Italy (Monterenzio Vecchio and Monte Bibele), 13 Iron Age central-southern Italic necropolises, and the northern Italian Bronze Age necropolis of Scalvinetto. Strontium isotopes were measured on individuals from the necropolis of Monte Bibele to infer their local or non-local origin. Results highlight the existence of statistically significant differences between Celts and autochthonous Italian groups. Celtic groups from Hungary and Italy (i.e., non-local individuals of Monterenzio Vecchio and Monte Bibele) share a similar biological background, supporting the historical records mentioning a common origin for Celts migrated to the eastern and southern borders of today's Europe. The presence of a supposed Steppean ancestry both in Celts from Hungary and Celts from northern Italy corroborates the hypothesis of the existence of a westward migration of individuals and genes from the Steppe towards northern Italy during the Bronze and Iron Age, which contributed to the biological variability of pre-Celtic and later Celtic populations, respectively. Conversely, individuals from central-southern Italy show an autochthonous pre-Iron Age background. Lastly, this work supports the existence of Celtic migratory routes in northern Italy, as shown by biological and cultural admixture between Celts and Italics living together.

The Human Microbiome and Its Role in Musculoskeletal Disorders.

Musculoskeletal diseases (MSDs) are characterized as injuries and illnesses that affect the musculoskeletal system. MSDs affect every population worldwide and are associated with substantial global burden. Variations in the makeup of the gut microbiota may be related to chronic MSDs. There is growing interest in exploring potential connections between chronic MSDs and variations in the composition of gut microbiota. The human microbiota is a complex community consisting of viruses, archaea, bacteria, and eukaryotes, both inside and outside of the human body. These microorganisms play crucial roles in influencing human physiology, impacting metabolic and immunological systems in health and disease. Different body areas host specific types of microorganisms, with facultative anaerobes dominating the gastrointestinal tract (able to thrive with or without oxygen), while strict aerobes prevail in the nasal cavity, respiratory tract, and skin surfaces (requiring oxygen for development). Together with the immune system, these bacteria have coevolved throughout time, forming complex biological relationships. Changes in the microbial ecology of the gut may have a big impact on health and can help illnesses develop. These changes are frequently impacted by lifestyle choices and underlying medical disorders. The potential for safety, expenses, and efficacy of microbiota-based medicines, even with occasional delivery, has attracted interest. They are, therefore, a desirable candidate for treating MSDs that are chronic and that may have variable progression patterns. As such, the following is a narrative review to address the role of the human microbiome as it relates to MSDs.

Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables.

The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

The Practice of Muhallil Marriage for a Wife who Has been Divorced Three Times According to the Perspective of Islamic Law in North Kuripan Community, West Lombok District

Marriage essentially aims to form a household that is Sakinah Mawaddah, Warohmah and must be registered at the KUA (religious affairs office) to be recognized by the state, but this tahlil marriage is not registered at the religious affairs. KUA office is only carried out with the intention of divorcing her after having biological relationship. He is not intended to form a family. Under such conditions, it is necessary to disseminate information to the public, especially regarding the practice of muhalil marriage in accordance with Islamic law. The activity methods are lectures, discussions, and questions and also answer. The results of this activity can be concluded that there is still a lack of knowledge and insight in the community regarding marriage so that this activity can help the community.

The Role of Extracellular Traps in HIV Infection.

Human immunodeficiency virus (HIV) infection is still an important public health problem, which justifies the research of new therapies to combat it. Recent studies show that Extracellular Traps (ETs) are cellular mechanisms useful in the capture and destruction of some viruses, such as the HIV. Here, we show that neutrophils from peripheral blood, genital tissues, and placenta are activated when exposed to human immunodeficiency virus type 1 (HIV-1) and release Neutrophil Extracellular Traps (NETs). The NETs can capture, neutralize, and inactivate the virus and, also, protect other target cells from HIV infection, as long as the DNA and other constituents of the NETs remain intact. Further, the review indicates that the immunoprotective role of NETs in the context of HIV-1 infection is a promising finding for the development of new antiviral therapies. It is necessary, however, the development of studies that evaluate the tissue injury that NETs can cause and the biological relationships with other cells to improve them as therapeutic targets.

Hyperbolic hierarchical knowledge graph embeddings for biological entities

Predicting relationships between biological entities can greatly benefit important biomedical problems. Previous studies have attempted to represent biological entities and relationships in Euclidean space using embedding methods, which evaluate their semantic similarity by representing entities as numerical vectors. However, the limitation of these methods is that they cannot prevent the loss of latent hierarchical information when embedding large graph-structured data into Euclidean space, and therefore cannot capture the semantics of entities and relationships accurately. Hyperbolic spaces, such as Poincaré ball, are better suited for hierarchical modeling than Euclidean spaces. This is because hyperbolic spaces exhibit negative curvature, causing distances to grow exponentially as they approach the boundary. In this paper, we propose HEM, a hyperbolic hierarchical knowledge graph embedding model to generate vector representations of bio-entities. By encoding the entities and relations in the hyperbolic space, HEM can capture latent hierarchical information and improve the accuracy of biological entity representation. Notably, HEM can preserve rich information with a low dimension compared with the methods that encode entities in Euclidean space. Furthermore, we explore the performance of HEM in protein–protein interaction prediction and gene-disease association prediction tasks. Experimental results demonstrate the superior performance of HEM over state-of-the-art baselines. The data and code are available at : https://github.com/Nan-ll/HEM.