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Heat transfer in metallic nanometre-sized gaps

Enhancing art creation through AI-based generative adversarial networks in educational auxiliary system

The South American MicroBiome Archive (saMBA): enriching the microbiome field by studying neglected populations

Abstract The human gut microbiome is associated with numerous health outcomes, often in a region-specific manner. Unfortunately, global microbiome research remains profoundly imbalanced: over 70% of sequenced human microbiomes originate from Europe and North America, which together represent only 15% of the world’s population. To address this disparity, we developed saMBA—the largest archive of gut microbiomes from South America, one of the world’s most microbiome-diverse regions but also among the least studied. The archive comprises 33 studies, ~73% of which had not been included in any previous compendium. A total of 3382 samples were reanalysed, of which 2913 were successfully included after applying quality filters. By leveraging this resource, we reveal both high within-population diversity and between-population uniqueness in the continent, expanding our current understanding of the gut microbiome to be more globally representative. Additionally, saMBA reveals that much of the region’s gut microbiome diversity remains undercharacterised, and provides guidance for future sampling efforts to more accurately capture regional biodiversity. The framework used to build saMBA is compatible with existing global resources and is openly available, thus promoting the inclusion of other underrepresented populations to accelerate microbiome research globally.

Encapsulated tri-band terahertz (THz) swiveled dielectric resonator antenna (DRA) with substrate integrated waveguide (SIW) and photonic band gap (PBG) crystal for gain enhancement

Nucleophilic addition promoted ring rearrangement-aromatization in aza-/thio-sesquiterpenoid biosynthesis

Efficacy and safety of anlotinib monotherapy for advanced hepatocellular carcinoma and clinical role of α-fetoprotein

Design of Ig-like binders targeting α-synuclein fibril for mitigating its pathological activities

Oral cancer detection via Vanilla CNN optimized by improved artificial protozoa optimizer

Effect of intrapartum azithromycin on early childhood gut mycobiota development: post hoc analysis of a double-blind randomized trial

Abstract Intrapartum azithromycin prophylaxis reduced maternal infections but showed no effect on neonatal sepsis and mortality. Although antibiotic exposure may indirectly alter the mycobiota (community of fungi that live in a given environment), there is no data available on how intrapartum azithromycin impacts gut mycobiota development. We hereby assess the impact of intrapartum azithromycin on gut mycobiota development from birth to the age of three years, by ITS2 gene profiling of rectal samples from 102 healthy Gambian infants selected from a double-blind randomized placebo-controlled clinical trial (PregnAnZI-2 – ClinicalTrials.org NCT03199547). In the trial, women received 2 g oral azithromycin or placebo (1:1) during labour with the intension of assessing effect on neonatal sepsis or mortality. Secondary objectives included effects on bacterial carriage and resistance, puerperal infections, and infant growth. Our analysis show that season and parity were key factors that influenced gut mycobiota development. Intrapartum azithromycin increased the abundance of Candida orthopsilosis but only in the wet season and did not show different effects by sex of the child. These data suggest that season and parity can be key factors influencing gut mycobiota development and may inform strategies for a wider implementation of intrapartum azithromycin intervention.

Optimization of helicopter and UAV coordinated SAR time

Highly customizable, ultrawide-temperature free-form flexible sensing electronic systems based on medium-entropy alloy paintings

Retrospective evaluation of the predictive value of acute pacing capture threshold for long-term outcomes in Chinese patients with leadless pacemakers

Mitigating emissions and costs through demand-side solutions in Chinese residential buildings

3D long time spatiotemporal convolution for complex transfer sequence prediction

Evidence triangulator: using large language models to extract and synthesize causal evidence across study designs

Synthesis of heterocycle based carboxymethyl cellulose conjugates as novel anticancer agents targeting HCT116, MCF7, PC3 and A549 cells

Abstract Toward developing anticancer agents, heterocycle-based carboxymethyl cellulose conjugates have been synthesized. 2-Cyano-N′-(aryl/heteroarylethylidene)acetohydrazides and ethyl 2-cyano-3-(heteryl)acrylates were utilized as precursors for the synthesis of pyridine-based compounds. The chemical structures of the synthesized derivatives were characterized using various spectroscopic techniques, including 1H-, 13C-NMR, Fourier transform infrared spectroscopy (FTIR), as well as scanning electron microscopy (SEM).The anticancer effects of compounds on HCT-116, MCF-7, PC3 and A549 cancer cell lines were investigated and their cytotoxicity against RPE-1 normal cells was estimated to determine their safety. Compounds 4b and 7c exhibit high selectivity toward cancer cells while maintaining a strong safety margin for normal cells. The results demonstrated that the novel heterocycle-based carboxymethyl cellulose conjugates are promising and can be further evaluated as a potential therapeutic agent.

Physical fitness status and associated determinants among Chinese children aged 9–12 years in Shandong province: a population-based cross-sectional study

Non-variational quantum random access optimization with alternating operator ansatz

Optical solutions to time-fractional improved (2+1)-dimensional nonlinear Schrödinger equation in optical fibers

Exploring the feasibility of AI-based analysis of histopathological variability in salivary gland tumours

Abstract This study uses artificial intelligence (AI) for differentiation between salivary gland tumours (SGT) using digitised Haematoxylin and Eosin stained whole-slide images (WSI). Machine learning (ML) classifiers were developed and tested using 320 scanned WSI. These included a benign versus malignant classifier (BvM) for automated identification of benign and malignant tumours, a malignant sub-typing (MST) classifier for subtyping four most common malignant SGT and a third classifier for malignant tumour grading. ML results were also compared with deep learning models. All ML classifiers showed an excellent accuracy. An F1 score of 0.95 was seen for benign vs. malignant and malignant subtyping tasks and 0.87 for automated grading. In comparison, the best performing DL models showed F1 scores of 0.80, 0.60 and 0.70 for the same tasks respectively. External validation on an independent cohort demonstrated good accuracy, with an F1 score of 0.87 for both the benign vs. malignant and grading classifiers. A notable association between cellularity, nuclear haematoxylin, cytoplasmic eosin, and nucleus/cell ratio (p < 0.01) were seen between tumours. Our novel findings show that AI can be used for automated differentiation between SGT. Analysis of larger multicentre cohorts is required to establish the significance and clinical usefulness of these findings.