Metabolite-Sensing Receptor Ffar2 Regulates Colonic Group 3 Innate Lymphoid Cells and Gut Immunity.

Group 3 innate lymphoid cells (ILC3s) sense environmental signals that are critical for gut homeostasis and host defense. However, the metabolite-sensing G-protein-coupled receptors that regulate colonic ILC3s remain poorly understood. We found that colonic ILC3s expressed Ffar2, a microbial metabolite-sensing receptor, and that Ffar2 agonism promoted ILC3 expansion and function. Deficiency of Ffar2 in ILC3s decreased their in situ proliferation and ILC3-derived interleukin-22 (IL-22) production. This led to impaired gut epithelial function characterized by altered mucus-associated proteins and antimicrobial peptides and increased susceptibility to colonic injury and bacterial infection. Ffar2 increased IL-22+ CCR6+ ILC3s and influenced ILC3 abundance in colonic lymphoid tissues. Ffar2 agonism differentially activated AKT or ERK signaling and increased ILC3-derived IL-22 via an AKT and STAT3 axis. Our findings suggest that Ffar2 regulates colonic ILC3 proliferation and function, and they identify an ILC3-receptor signaling pathway modulating gut homeostasis and pathogen defense.

Genomes from historical Drosophila melanogaster specimens illuminate adaptive and demographic changes across more than 200 years of evolution.

The ability to perform genomic sequencing on long-dead organisms is opening new frontiers in evolutionary research. These opportunities are especially notable in the case of museum collections, from which countless documented specimens may now be suitable for genomic analysis-if data of sufficient quality can be obtained. Here, we report 25 newly sequenced genomes from museum specimens of the model organism Drosophila melanogaster, including the oldest extant specimens of this species. By comparing historical samples ranging from the early 1800s to 1933 against modern-day genomes, we document evolution across thousands of generations, including time periods that encompass the species' initial occupation of northern Europe and an era of rapidly increasing human activity. We also find that the Lund, Sweden population underwent local genetic differentiation during the early 1800s to 1933 interval (potentially due to drift in a small population) but then became more similar to other European populations thereafter (potentially due to increased migration). Within each century-scale time period, our temporal sampling allows us to document compelling candidates for recent natural selection. In some cases, we gain insights regarding previously implicated selection candidates, such as ChKov1, for which our inferred timing of selection favors the hypothesis of antiviral resistance over insecticide resistance. Other candidates are novel, such as the circadian-related gene Ahcy, which yields a selection signal that rivals that of the DDT resistance gene Cyp6g1. These insights deepen our understanding of recent evolution in a model system, and highlight the potential of future museomic studies.

Functional polyamine metabolic enzymes and pathways encoded by the virosphere.

Viruses produce more viruses by manipulating the metabolic and replication systems of their host cells. Many have acquired metabolic genes from ancestral hosts and use the encoded enzymes to subvert host metabolism. The polyamine spermidine is required for bacteriophage and eukaryotic virus replication, and herein, we have identified and functionally characterized diverse phage- and virus-encoded polyamine metabolic enzymes and pathways. These include pyridoxal 5'-phosphate (PLP)-dependent ornithine decarboxylase (ODC), pyruvoyl-dependent ODC and arginine decarboxylase (ADC), arginase, S-adenosylmethionine decarboxylase (AdoMetDC/speD), spermidine synthase, homospermidine synthase, spermidine N-acetyltransferase, and N-acetylspermidine amidohydrolase. We identified homologs of the spermidine-modified translation factor eIF5a encoded by giant viruses of the Imitervirales. Although AdoMetDC/speD is prevalent among marine phages, some homologs have lost AdoMetDC activity and have evolved into pyruvoyl-dependent ADC or ODC. The pelagiphages that encode the pyruvoyl-dependent ADCs infect the abundant ocean bacterium Candidatus Pelagibacter ubique, which we have found encodes a PLP-dependent ODC homolog that has evolved into an ADC, indicating that infected cells would contain both PLP- and pyruvoyl-dependent ADCs. Complete or partial spermidine or homospermidine biosynthetic pathways are found encoded in the giant viruses of the Algavirales and Imitervirales, and in addition, some viruses of the Imitervirales can release spermidine from the inactive N-acetylspermidine. In contrast, diverse phages encode spermidine N-acetyltransferase that can sequester spermidine into its inactive N-acetyl form. Together, the virome-encoded enzymes and pathways for biosynthesis and release or biochemical sequestration of spermidine or its structural analog homospermidine consolidate and expand evidence supporting an important and global role of spermidine in virus biology.

Functional identification of bacterial spermine, thermospermine, norspermine, norspermidine, spermidine, and N1-aminopropylagmatine synthases.

Spermine synthase is an aminopropyltransferase that adds an aminopropyl group to the essential polyamine spermidine to form tetraamine spermine, needed for normal human neural development, plant salt and drought resistance, and yeast CoA biosynthesis. We functionally identify for the first time bacterial spermine synthases, derived from phyla Bacillota, Rhodothermota, Thermodesulfobacteriota, Nitrospirota, Deinococcota, and Pseudomonadota. We also identify bacterial aminopropyltransferases that synthesize the spermine same mass isomer thermospermine, from phyla Cyanobacteriota, Thermodesulfobacteriota, Nitrospirota, Dictyoglomota, Armatimonadota, and Pseudomonadota, including the human opportunistic pathogen Pseudomonas aeruginosa. Most of these bacterial synthases were capable of synthesizing spermine or thermospermine from the diamine putrescine and so possess also spermidine synthase activity. We found that most thermospermine synthases could synthesize tetraamine norspermine from triamine norspermidine, that is, they are potential norspermine synthases. This finding could explain the enigmatic source of norspermine in bacteria. Some of the thermospermine synthases could synthesize norspermidine from diamine 1,3-diaminopropane, demonstrating that they are potential norspermidine synthases. Of 18 bacterial spermidine synthases identified, 17 were able to aminopropylate agmatine to form N1-aminopropylagmatine, including the spermidine synthase of Bacillus subtilis, a species known to be devoid of putrescine. This suggests that the N1-aminopropylagmatine pathway for spermidine biosynthesis, which bypasses putrescine, may be far more widespread than realized and may be the default pathway for spermidine biosynthesis in species encoding L-arginine decarboxylase for agmatine production. Some thermospermine synthases were able to aminopropylate N1-aminopropylagmatine to form N12-guanidinothermospermine. Our study reveals an unsuspected diversification of bacterial polyamine biosynthesis and suggests a more prominent role for agmatine.

HGTDR: Advancing drug repurposing with heterogeneous graph transformers.

MOTIVATION: Drug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still need to meet expectations. Therefore, it is crucial to offer a systematic approach for drug repurposing to achieve cost savings and enhance human lives. In recent years, using biological network-based methods for drug repurposing has generated promising results. Nevertheless, these methods have limitations. Primarily, the scope of these methods is generally limited concerning the size and variety of data they can effectively handle. Another issue arises from the treatment of heterogeneous data, which needs to be addressed or converted into homogeneous data, leading to a loss of information. A significant drawback is that most of these approaches lack end-to-end functionality, necessitating manual implementation and expert knowledge in certain stages. RESULTS: We propose a new solution, Heterogeneous Graph Transformer for Drug Repurposing (HGTDR), to address the challenges associated with drug repurposing. HGTDR is a three-step approach for knowledge graph-based drug repurposing: (1) constructing a heterogeneous knowledge graph, (2) utilizing a heterogeneous graph transformer network, and (3) computing relationship scores using a fully connected network. By leveraging HGTDR, users gain the ability to manipulate input graphs, extract information from diverse entities, and obtain their desired output. In the evaluation step, we demonstrate that HGTDR performs comparably to previous methods. Furthermore, we review medical studies to validate our method's top 10 drug repurposing suggestions, which have exhibited promising results. We also demonstrated HGTDR's capability to predict other types of relations through numerical and experimental validation, such as drug-protein and disease-protein inter-relations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/bcb-sut/HGTDR and http://git.dml.ir/BCB/HGTDR.

Model-Agnostic Binary Patch Grouping for Bone Marrow Whole Slide Image Representation.

Histopathology is the reference standard for pathology diagnosis, and has evolved with the digitization of glass slides [ie, whole slide images (WSIs)]. While trained histopathologists are able to diagnose diseases by examining WSIs visually, this process is time consuming and prone to variability. To address these issues, artificial intelligence models are being developed to generate slide-level representations of WSIs, summarizing the entire slide as a single vector. This enables various computational pathology applications, including interslide search, multimodal training, and slide-level classification. Achieving expressive and robust slide-level representations hinges on patch feature extraction and aggregation steps. This study proposed an additional binary patch grouping (BPG) step, a plugin that can be integrated into various slide-level representation pipelines, to enhance the quality of slide-level representation in bone marrow histopathology. BPG excludes patches with less clinical relevance through minimal interaction with the pathologist; a one-time human intervention for the entire process. This study further investigated domain-general versus domain-specific feature extraction models based on convolution and attention and examined two different feature aggregation methods, with and without BPG, showing BPG's generalizability. The results showed that using BPG boosts the performance of WSI retrieval (mean average precision at 10) by 4% and improves WSI classification (weighted-F1) by 5% compared to not using BPG. Additionally, domain-general large models and parameterized pooling produced the best-quality slide-level representations.

Sequential Patching Lattice for Image Classification and Enquiry: Streamlining Digital Pathology Image Processing.

Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of whole-slide images (WSIs), demand is growing for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this article, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a collage of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting nonredundant representative features. In search and match applications, SPLICE showed improved accuracy, reduced computation time, and storage requirements compared with existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduced storage requirements for representing tissue images by 50%. This reduction can enable numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Biological Magnetic Resonance Data Bank.

The Biological Magnetic Resonance Data Bank (BMRB, https://bmrb.io) is the international open data repository for biomolecular nuclear magnetic resonance (NMR) data. Comprised of both empirical and derived data, BMRB has applications in the study of biomacromolecular structure and dynamics, biomolecular interactions, drug discovery, intrinsically disordered proteins, natural products, biomarkers, and metabolomics. Advances including GHz-class NMR instruments, national and trans-national NMR cyberinfrastructure, hybrid structural biology methods and machine learning are driving increases in the amount, type, and applications of NMR data in the biosciences. BMRB is a Core Archive and member of the World-wide Protein Data Bank (wwPDB).

CNVDeep: deep association of copy number variants with neurocognitive disorders.

BACKGROUND: Copy number variants (CNVs) have become increasingly instrumental in understanding the etiology of all diseases and phenotypes, including Neurocognitive Disorders (NDs). Among the well-established regions associated with ND are small parts of chromosome 16 deletions (16p11.2) and chromosome 15 duplications (15q3). Various methods have been developed to identify associations between CNVs and diseases of interest. The majority of methods are based on statistical inference techniques. However, due to the multi-dimensional nature of the features of the CNVs, these methods are still immature. The other aspect is that regions discovered by different methods are large, while the causative regions may be much smaller. RESULTS: In this study, we propose a regularized deep learning model to select causal regions for the target disease. With the help of the proximal [20] gradient descent algorithm, the model utilizes the group LASSO concept and embraces a deep learning model in a sparsity framework. We perform the CNV analysis for 74,811 individuals with three types of brain disorders, autism spectrum disorder (ASD), schizophrenia (SCZ), and developmental delay (DD), and also perform cumulative analysis to discover the regions that are common among the NDs. The brain expression of genes associated with diseases has increased by an average of 20 percent, and genes with homologs in mice that cause nervous system phenotypes have increased by 18 percent (on average). The DECIPHER data source also seeks other phenotypes connected to the detected regions alongside gene ontology analysis. The target diseases are correlated with some unexplored regions, such as deletions on 1q21.1 and 1q21.2 (for ASD), deletions on 20q12 (for SCZ), and duplications on 8p23.3 (for DD). Furthermore, our method is compared with other machine learning algorithms. CONCLUSIONS: Our model effectively identifies regions associated with phenotypic traits using regularized deep learning. Rather than attempting to analyze the whole genome, CNVDeep allows us to focus only on the causative regions of disease.

Tailoring transfection for cardiomyocyte cell lines: balancing efficiency and toxicity in lipid versus polymer-based transfection methods in H9c2 and HL-1 cells.

Cardiovascular research relies heavily on the veracity of in vitro cardiomyocyte models, with H9c2 and HL-1 cell lines at the forefront due to their cardiomyocyte-like properties. However, the variability stemming from nonstandardized culturing and transfection methods poses a significant challenge to data uniformity and reliability. In this study, we introduce meticulously crafted protocols to enhance the culture and transfection of H9c2 and HL-1 cells, emphasizing the reduction of cytotoxic effects while improving transfection efficiency. Through the examination of polymer-based and lipid-based transfection methods, we offer a comparative analysis that underscores the heightened efficiency and reduced toxicity of these approaches. Our research provides an extensive array of step-by-step procedures designed to foster robust cell cultures and outlines troubleshooting practices to rectify issues of low transfection rates. We discuss the merits and drawbacks of both transfection techniques, equipping researchers with the knowledge to choose the most fitting method for their experimental goals. By offering a definitive guide to these cell lines' culturing and transfection, our work seeks to set a new standard in procedural consistency, ensuring that the cardiovascular research community can achieve more dependable and reproducible results, thereby pushing the boundaries of current methodologies toward impactful clinical applications.NEW & NOTEWORTHY We have developed standardized protocols that significantly reduce cytotoxicity and enhance transfection efficiency in H9c2 and HL-1 cardiomyocyte cell lines. Our detailed comparative analysis of polymer-based and lipid-based transfection methods has identified optimized approaches with superior performance. Accompanying these protocols are comprehensive troubleshooting strategies to address common issues related to low transfection rates. Implementing these protocols is expected to yield more consistent and reproducible results, driving the field of cardiovascular research toward impactful clinical breakthroughs.

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks.

Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through "majority vote" and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Measurement of microRNA-106b as a gastric cancer biomarker based on Zn-BTC MOF label-free genosensor.

Due to the late detection of stomach cancer, this cancer usually causes high mortality. The development of an electrochemical genosensor to measure microRNA 106b (miR-106b), as a gastric cancer biomarker, is the aim of this effort. In this regard, first, 1,3,5-benzenetricarboxylate (BTC) metal-organic frameworks (Zn-BTC MOF) were self-assembled on the glassy carbon electrode and then the probe (ssDNA) was immobilized on it. The morphology Zn-BTC MOF was characterized by SEM, FT-IR, Raman and X-Ray techniques. Zn-BTC MOF as a biosensor substrate has strong interaction with ssDNA. Quantitative measurement of miR-106b was performed by electrochemical impedance spectroscopy (EIS). To perform this measurement, the difference of the charge transfer resistances (ΔRct) of Nyquist plots of the ssDNA probe modified electrode before and after hybridization with miR-106b was obtained and used as an analytical signal. Using the suggested genosensor, it is possible to measure miR-106b in the concentration range of 1.0 fM to 1.0 µM with a detection limit of 0.65 fM under optimal conditions. Moreover, at the genosensor surface, miR-106b can be detected from a non-complementary and a single base mismatch sequence. Also, the genosensor was used to assess miR-106b in a human serum sample and obtained satisfactory results.

DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.

The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene drives disease, known as "gene prioritization," is not entirely understood. Genome-wide searches for gene-disease associations are still underdeveloped due to reliance on previous discoveries and evidence sources with false positive or negative relations. This paper introduces DeepGenePrior, a model based on deep neural networks that prioritizes candidate genes in genetic diseases. Using the well-studied Variational AutoEncoder (VAE), we developed a score to measure the impact of genes on target diseases. Unlike other methods that use prior data to select candidate genes, based on the "guilt by association" principle and auxiliary data sources like protein networks, our study exclusively employs copy number variants (CNVs) for gene prioritization. By analyzing CNVs from 74,811 individuals with autism, schizophrenia, and developmental delay, we identified genes that best distinguish cases from controls. Our findings indicate a 12% increase in fold enrichment in brain-expressed genes compared to previous studies and a 15% increase in genes associated with mouse nervous system phenotypes. Furthermore, we identified common deletions in ZDHHC8, DGCR5, and CATG00000022283 among the top genes related to all three disorders, suggesting a common etiology among these clinically distinct conditions. DeepGenePrior is publicly available online at http://git.dml.ir/z_rahaie/DGP to address obstacles in existing gene prioritization studies identifying candidate genes.

A Dual CQD-Catalysis and H-Bond Acceptor for Controlling Product Selectivity and Regioselectivity in Symmetric/Unsymmetric Azoxy Arenes.

Azoxy arenes are valuable compounds in different areas of chemistry, such as organic chemistry, medicinal chemistry, and natural product chemistry. Despite their value, the regioselective synthesis of unsymmetric azoxybenzenes has remained a real challenge in the field. Herein, the product selectivity in oxidative homocoupling of anilines into symmetric azoxybenzenes was first achieved by an asparagine-functionalized CQD catalyst. Subsequently, in the cross-coupling of anilines into the unsymmetric azoxybenzenes via an ortho H-bond acceptor (HBA) on one of the coupling anilines, the regioselectivity was effectively controlled. It was demonstrated that ortho-HBA could mechanistically establish a six-membered intramolecular hydrogen-bonded ring on an N,N'-dihydroxy intermediate. The formed hydrogen bond makes the nearby nitrogen eminently suitable for the slow dehydration step. As a result, the functional oxygen of the azoxy compound is placed far from the HBA. The o-HBA mechanism also controls the regioselectivity ratio in which 1:0 (with an intramolecular H-bonded hexagonal ring), 2:1 (with an intramolecular H-bonded pentagonal ring), and 1:1 (without an ortho-HBA) isomeric mixtures could be achieved. The HBA mechanism was exploited by different substituted anilines, and various unsymmetric azoxybenzenes were synthesized. Finally, with the aid of mechanistic studies, a plausible mechanism for the reaction was proposed.

Monitoring antimicrobial resistance in Campylobacter isolates of chickens and turkeys at the slaughter establishment level across the United States, 2013-2021.

Foodborne infections with antimicrobial-resistant Campylobacter spp. remain an important public health concern. Publicly available data collected by the National Antimicrobial Resistance Monitoring System for Enteric Bacteria related to antimicrobial resistance (AMR) in Campylobacter spp. isolated from broiler chickens and turkeys at the slaughterhouse level across the United States between 2013 and 2021 were analysed. A total of 1,899 chicken-origin (1,031 Campylobacter coli (C. coli) and 868 Campylobacter jejuni (C. jejuni)) and 798 turkey-origin (673 C. coli and 123 C. jejuni) isolates were assessed. Chicken isolates exhibited high resistance to tetracycline (43.65%), moderate resistance to ciprofloxacin (19.5%), and low resistance to clindamycin (4.32%) and azithromycin (3.84%). Turkey isolates exhibited very high resistance to tetracycline (69%) and high resistance to ciprofloxacin (39%). The probability of resistance to all tested antimicrobials, except for tetracycline, significantly decreased during the latter part of the study period. Turkey-origin Campylobacter isolates had higher odds of resistance to all antimicrobials than isolates from chickens. Compared to C. jejuni isolates, C. coli isolates had higher odds of resistance to all antimicrobials, except for ciprofloxacin. The study findings emphasize the need for poultry-type-specific strategies to address differences in AMR among Campylobacter isolates.

Development of an electrochemical sensitive aptasensor based on a zeolite imidazolate framework-8 and gold nanoparticles for the determination of Staphylococcus aureus bacteria.

Staphylococcus aureus (S. aureus) is one of the most important pathogens that cause illness and food poisoning. In this research, using a glassy carbon electrode (GCE) modified with zeolite imidazolate framework-8 (ZIF 8) and gold nanoparticles (AuNPs), a sensitive electrochemical aptasensor has been made for the detection of the S. aureus bacteria. The morphology of the prepared AuNPs-ZIF 8 nanocomposite has been carefully characterized by means of transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), and energy-dispersive X-ray spectroscopy (EDS). In the manufacturing process, the S. aureus aptamer is immobilized on the AuNPs-ZIF 8 surface. Electrochemical impedance spectroscopy (EIS) method has been used for quantitative determination of S. aureus bacteria. The changes in the charge transfer resistance (Rct) of the aptamer due to the change in the concentration of bacteria are considered as the analytical signals. The proposed aptasensor has linear response in the concentration range of 1.5 × 101 to 1.5 × 107 CFU mL-1 of S. aureus bacteria. The detection limit of the method is 3.4 CFU mL-1. Using the developed aptasensor, it is possible to determine S. aureus bacteria in water and milk samples.

The McCusker Subjective Cognitive Impairment Inventory (McSCI): a novel measure of perceived cognitive decline.

BACKGROUND: Subjective cognitive decline (SCD), i.e. self/other-reported concerns on one's cognitive functioning without objective evidence of significant decline, is an indicator of dementia risk. There is little consensus on reliability and validity of the available SCD measures. Therefore, introducing a novel and psychometrically sound measure of SCD is timely. OBJECTIVE: The psychometric properties of a new SCD measure, the McCusker Subjective Cognitive Impairment Inventory-Self-Report (McSCI-S), are reported. METHODS: Through review of previously published measures as well as our clinical and research data on people with SCD, we developed a 46-item self-report questionnaire to assess concerns on six cognitive domains, namely, memory, language, orientation, attention and concentration, visuoconstruction abilities and executive function. The McSCI-S was examined in a cohort of 526 participants using factor analysis, item response theory analysis and receiver operating characteristic (ROC) curve. RESULTS: A unidimensional model provided acceptable fit (CFI = 0.94, TLI = 0.94, RMSEA [90% CI] = 0.052 [.049, 0.055], WRMR = 1.45). The McSCI-S internal consistency was excellent (.96). A cut-off score of ≥24 is proposed to identify participants with SCDs. Higher McSCI-S scores were associated with poorer general cognition, episodic verbal memory, executive function and greater memory complaints and depressive scores (P < .001), controlling for age, sex and education. CONCLUSIONS: Excellent reliability and construct validity suggest the McSCI-S estimates SCDs with acceptable accuracy while capturing self-reported concerns for various cognitive domains. The psychometric analysis indicated that this measure can be used in cohort studies as well as on individual, clinical settings to assess SCDs.

The relationship between Empathy and listening styles is complex: implications for doctors in training.

BACKGROUND: Effective communication is the key to a successful relationship between doctors and their patients. Empathy facilitates effective communication, but physicians vary in their ability to empathize with patients. Listening styles are a potential source of this difference. We aimed to assess empathy and listening styles among medical students and whether students with certain listening styles are more empathetic. METHODS: In this cross-sectional study, 97 medical students completed the Jefferson scale of Empathy (JSE) and the revised version of the Listening Styles Profile (LSP-R). The relationship between empathy and listening styles was assessed by comparing JSE scores across different listening styles using ANOVA in SPSS software. A p-value less than 0.05 was considered significant. RESULTS: Overall, the students showed a mean empathy score of 103 ± 14 on JSE. Empathy scores were lower among clinical students compared to preclinical students. Most of the medical students preferred the analytical listening style. The proportion of students who preferred the relational listening style was lower among clinical students compared to preclinical students. There was no significant relationship between any of the listening styles with empathy. CONCLUSION: Our results do not support an association between any particular listening style with medical students' empathic ability. We propose that students who have better empathetic skills might shift between listening styles flexibly rather than sticking to a specific listening style.