Browse Articles

The association between sex hormones and bone mineral density in US females

Investigating the role of gut microbiota in diabetic nephropathy through plasma proteome mediated analysis

Abstract Diabetic nephropathy (DN) is the leading cause of end-stage renal disease and poses significant threats to individuals with diabetes. The concept of gut–kidney axis has gained increasing attention in recent years and the in the occurrence and development of DN, alterations in the gut microbiota also plays a crucial and indispensable role. However, the specific causal relationships between various gut microbial communities and DN, as well as the underlying molecular mechanisms, remains unclear. This study utilized data from genome-wide association studies. After screening for qualified instrumental variables, mendelian randomization causal analyses were performed by inverse variance weighting, MR-Egger, weighted median, weighted mode and MR-RAPS methods. Additionally, sensitivity analyses such as heterogeneity, multiplicity, and the direction of the causal effect were carried out to ensure that the results were robust. After identifying significant gut microbiota, protein-proteomics mediation analysis was conducted on potential 3282 plasma proteins to determine those with mediating effects. Finally, Reactome enrichment analysis was performed to ascertain metabolic or signaling pathways with mediating effects. Mendelian randomization analysis indicated associations between 21 gut microbiota and DN. After adjusting significance levels, Catenibacterium and Parasutterella were found to have causal effects on the onset of DN. Subsequently, we identified 22 plasma proteins with mediating effects, along with 27 metabolic or signaling pathways including activated propionic acid metabolism. Increased in the abundance of Catenibacterium and Parasutterella intestinal bacteria are causative factors for DN. More importantly, the underlying mechanism by which the increased abundance of Catenibacterium and Parasutterella intestinal bacteria lead to DN were revealed, providing a blueprint for the involvement of gut–kidney axis in the pathogenesis of DN and paving the way for future studies.

The liver proteome of individuals with a natural UGT2B17 complete deficiency

Analysis of post-transcriptional regulatory signatures and immune cell subsets in premature ovarian insufficiency based on full-length transcriptome

Population shift in antibody immunity following the emergence of a SARS-CoV-2 variant of concern

Abstract Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) exhibit escape from pre-existing immunity and elicit variant-specific immune responses. In South Africa, the second wave of SARS-CoV-2 infections was driven by the Beta VOC, which coincided with the country-wide National COVID-19 Antibody Survey (NCAS). The NCAS was conducted between November 2020 and February 2021 to understand the burden of SARS-CoV-2 infection through seroprevalence. We evaluated 649 NCAS sera for spike binding and pseudovirus neutralizing antibodies. We classified individuals as ancestral or D614G neutralizers (114/649), Beta neutralizers (96/649), double neutralizers (375/649) or non-neutralizers (62/649). We observed a consistent decrease in preferential neutralization against the D614G variant from 68 to 18% of individuals over the four sampling months. Concurrently, samples with equivalent neutralization of both variants, or with enhanced neutralization of the Beta variant, increased from 32 to 82% of samples. Neutralization data showed that geometric mean titers (GMTs) against D614G dropped 2.4-fold, while GMTs against Beta increased 2-fold during this same period. A shift in population humoral immunity in favor of Beta-directed or cross-neutralizing antibody responses, paralleled the increase in genomic frequency of the Beta variant in South Africa. Understanding similar population immunity shifts could elucidate immunity gaps that drive SARS-CoV-2 evolution.

Actively forming microbial mats provide insight into the development of microdigitate stromatolites

Abstract Stromatolites can be traced back to ∼3.5 billion years. They were widespread in the shorelines of ancient oceans and seas. However, they are uncommon nowadays, and basic information is lacking about how these unique carbonate structures developed. Here we study the unusually thick (3–5 cm) biofilms of the 79.2 °C outflow from Köröm thermal well (Hungary) and demonstrate that its microbial mat – carbonate architecture is similar to fossilized microdigitate stromatolites. Our observations reveal vertically oriented fibrous mineral fabrics, typical of stromatolites, in the red biofilm and clotted mesostructures, typical of thrombolites, in the green biofilm. These layers contain carbonate peloids and show network structures, formed by filamentous microbes. The 16S rRNA gene-based amplicon sequencing implies that numerous undescribed taxa may contribute to the carbonate mineralisation. The biofilms abundantly contain the phyla Bacteroidota, Pseudomonadota and Cyanobacteria. Geitlerinema PCC-8501 and Raineya are characteristic for the green biofilm, whereas uncultured Oxyphotobacteria, unc. Saprospiraceae and unc. Cytophagales are abundant in the red biofilm. A hydrogen-oxidizing Hydrogenobacter within the phylum Aquificota and unclassified Bacteria together with the phylum Deinococcota dominate the water and carbonate samples. The morphological structure and taxonomic composition of Köröm biofilm is a unique representation of the development processes of microbialite formations.

Research on the evolutionary game of reversed online public opinion based on the dual-helix structure mechanism

Citation manipulation through citation mills and pre-print servers

Reducing inference cost of Alzheimer’s disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners

Abstract While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET) data have shown promise in the accurate identification of Alzheimer’s disease, their clinical applicability is hindered by the assumption that both modalities are always available during model inference. In practice, clinicians adjust diagnostic tests based on available information and specific clinical contexts. We propose a novel MRI- and FDG PET-based multi-modal deep learning approach that mimics clinical decision-making by incorporating uncertainty estimates of an MRI-based model (generated using Monte Carlo dropout and evidential deep learning) to determine the necessity of an FDG PET scan, and only inputting the FDG PET to a multi-modal model when required. This approach significantly reduces the reliance on FDG PET scans, which are costly and expose patients to radiation. Our approach reduces the need for FDG PET by up to 92% without compromising model performance, thus optimizing resource use and patient safety. Furthermore, using a global model explanation technique, we provide insights into how anatomical changes in brain regions—such as the entorhinal cortex, amygdala, and ventricles—can positively or negatively influence the need for FDG PET scans in alignment with clinical understanding of Alzheimer’s disease.

Advancing green hydrogen production in Algeria with opportunities and challenges for future directions

Optimal surface defect detector design based on deep learning for 3D geometry

Further evidence that averageness and femininity, rather than symmetry and masculinity, predict facial attractiveness judgments

Abstract Facial attractiveness influences important social outcomes and most studies investigating possible predictors of facial attractiveness have tested for effects of shape symmetry, averageness (i.e., the converse of distinctiveness), and sexual dimorphism (i.e., masculinity–femininity). These studies have typically either tested for these possible effects by experimentally manipulating shape characteristics in faces images or have tested only for bivariate correlations between shape characteristics and attractiveness judgments. However, these two approaches have been criticised for lacking ecological validity and providing little insight into the independent contributions of symmetry, averageness, and sexual dimorphism, respectively. Moreover, the few studies that have investigated the independent contributions of symmetry, averageness, and sexual dimorphism have reported mixed results. Here we measured shape symmetry, averageness, and sexual dimorphism from face images and assessed their independent contribution to attractiveness ratings. Linear mixed effects models showed that facial attractiveness was significantly predicted by averageness in male and female faces and femininity in female faces, but not by masculinity in male faces or symmetry. These results are consistent with other recent work suggesting that averageness and femininity, rather than symmetry and masculinity, predict facial attractiveness.

Hyperchaotic hashing: a chaotic hash function based on 2D linear cross-coupled map with parallel feedback structure

Postoperative outcomes and their risk factors in left pancreatectomy with and without multivisceral resection

Abstract The aim of the present study was to identify risk factors associated with postoperative morbidity and mortality in patients undergoing isolated left pancreatectomy and those undergoing left pancreatic resection as part of a multivisceral resection. We performed a retrospective analysis of 296 adult patients who underwent elective left pancreatectomy from 2005 to 2022 at the University Hospital Erlangen. Patient demographics, pre- and intraoperative findings, along with postoperative outcomes, were collected and tested as predictive factors for various short-term postoperative parameters. Isolated left pancreatectomy (LP) was performed in 173 patients, while 123 patients underwent left pancreatectomy as part of a multivisceral resection (multivisceral LP). Multivisceral LP was associated with a higher rate of major morbidity (27% vs. 17%, p = 0.043) and mortality (7% vs. 2%, p = 0.046) compared to LP. Independent risk factors for major morbidity included the need for intraoperative blood transfusion and oncological lymphadenectomy in the LP group and longer operative time in the multivisceral LP group. CR-POPF was associated with the indication for surgery in the LP group. Independent risk factors for re-surgery included intraoperative blood transfusion in the LP group and ASA III or IV in the multivisceral LP group. Cardiovascular diseases were associated with higher mortality in the LP group, while COPD was the only risk factor for mortality in the multivisceral LP group. Multivisceral left pancreatectomy is associated with worse outcomes compared to isolated left pancreatectomy. In both groups, relevant risk factors predict postoperative complications. Patients with these identified risk factors should receive close monitoring during the postoperative course to anticipate outcomes with an increased risk of complications.

Publisher Correction: Transcriptomic profiling and machine learning reveal novel RNA signatures for enhanced molecular characterization of Hashimoto’s thyroiditis

Circulating extracellular vesicles as potential biomarkers and mediators of acute respiratory distress syndrome in sepsis

New approach to improve COP and heat recovery in transcritical CO2 refrigeration system for milk processing application

Abstract In hot climates, subcooling or after-cooling is an effective method to enhance the coefficient of performance (COP) of CO2 transcritical refrigeration system. This study investigates improvement of two contemporary subcooling arrangements: Integrated mechanical subcooling (IMS) and dedicated mechanical subcooling (DMS) and evaporative cooling arrangement to gascooler by introduction of gravity-fed evaporator in a dual evaporator parallel compression system suitable for milk processing. Using location-specific average meteorological data, the performance of the proposed systems is evaluated for Pune, India. Comparative analysis is conducted against a baseline transcritical CO2 system with flash gas bypass but lacking any subcooling arrangement. A considerable improvement in COP is observed when subcooling is combined with parallel compression. Incorporation of evaporative cooling with parallel compression yields 62.3% improvement in COP over the flash gas bypass system. However, heat recovery potential is considerably reduced by adopting evaporative cooling. Additionally, the study quantifies a potential reduction in water consumption of 45.6% over a system using flash gas bypass with an indirect evaporative cooling arrangement, and a reduction of 34.3% over a system employing parallel compression with a split gas cooler indirect evaporative cooling arrangement.

Exploring the potential mechanism of B-phycoerythrin on DSS-induced colitis and colitis-associated bone loss based on network pharmacology, molecular docking, and experimental validation

The association between age and acute pain sensitivity in patients with Herpes Zoster

Process optimization of jet electrodeposition Ni–Co–P alloy coating using response surface methodology and the tribological behavior of Ni–Co–P nanocomposite coatings

Abstract A novel Ni–Co–P alloy coating and Ni–Co–P nanocomposite coating were prepared by jet electrodeposition. The influence of jet electrodeposition processing parameters on the microhardness and wear track width of the Ni–Co–P alloy coating was investigated. Additionally, the cross-section morphology, EDS spectra, XRD patterns, microhardness and wear resistance of the coatings under the optimum jet electrodeposition parameters were evaluated. The BBD analysis results revealed that the established mathematical model was reliable. Furthermore, the optimum Ni–Co–P alloy coating parameters optimized through the response surface method were as follows: jet voltage: 12.14 V, plating solution temperature: 61.60 °C, reciprocating sweep speed: 173.19 mm·s−1, jet gap: 2.05 mm, pulse frequency: 4.06 kHz and duty cycle: 0.81. Under the optimum jet electrodeposition parameters, the results revealed that the significant influence of nano BN(h) and Al2O3 particles on the coatings’ thickness, Co contents, crystallite size, microhardness and wear resistance of Ni–Co–P nanocomposite coating. In addition, compared with the Ni–Co–P alloy coating and Ni–Co–P–BN(h) nanocomposite coating, the Ni–Co–P–Al2O3 composite coating exhibited a larger thickness (18.16 µm) and Co element contents (39.51 wt·%), a smaller crystallite size (16.440 nm), a higher microhardness (676.5 HV0.2), a more excellent wear resistance (402.9 µm).