Advancing Open Science

Oxaliplatin-triggered chemotherapy-induced peripheral neuropathy (CIPN) is a common and debilitating side effect of cancer treatment that limits the efficacy of chemotherapy and negatively impacts patients’ quality of life dramatically. To better understand the mechanisms of CIPN and to screen for potential therapeutic targets, it is critical to have reliable in vitro assays that effectively mirror the neuropathy in vivo. In this study, we established a dorsal root ganglia (DRG) explant model. This model displayed dose-dependent inhibition of neurite outgrowth in response to oxaliplatin, while oxalic acid exhibited no significant impact on the regrowth of DRG. The robustness of this assay was further demonstrated by the inhibition of OCT2 transporter, which facilitates oxaliplatin accumulation in neurons, largely restoring the neurite regrowth capacity. Using this model, we revealed that oxaliplatin triggered a substantial increase of oxidative stress in DRG. Notably, inhibition of TXNIP with verapamil reduced oxidative stress levels. Our results demonstrated the use of DRG explants as an efficient model to study the mechanisms of CIPN and screen for potential treatments.

Ion channels in biological membranes can form spatially localized clusters that exhibit cooperative gating behavior. In this mode, the activity of one channel modulates the opening probability of its neighbors. Understanding such inter-channel interactions is key to elucidating the molecular mechanisms underlying electrochemical signaling and advancing channel-targeted pharmacology. In this study, we introduce a simplified stochastic model of multi-channel gating that allows for systematic analysis of cooperative behavior under controlled conditions. Two information-theoretic metrics, i.e., Shannon entropy and Sample Entropy, are applied to simulated multi-channel datasets, including idealized total current traces and dwell-time sequences of cluster states, to quantify inter-channel cooperativity. We show that the entropic measures display a strong dependency on the strength and type of cooperation (non-, positive, or negative cooperation). The proposed entropy-based framework offers a generalizable and quantitative approach for biomedical data analysis, demonstrating effectiveness in interpreting multi-channel recordings and uncovering cooperative mechanisms in ion channel behavior. The underlying mechanisms by which entropy reflects cooperativity are expected to appear in real recordings, where deviations can further aid in characterizing individual channel features in future work.

Using 16S rRNA gene amplicon sequencing and FEAST microbial source tracking, this study characterized the bacterial communities in tissues (roots, stems, leaves, seeds) and associated soils (rhizosphere and bulk soil) of Vernonia anthelmintica, an important Uyghur medicinal plant endemic to arid southern Xinjiang. We found significantly higher bacterial diversity in soil than in plant tissues, with Pseudomonadota-dominated plant-associated communities and Actinobacteria co-dominating in soils. Bacterial community structures varied across plant compartments, with soil communities exhibiting greater stability and broader niche breadth. Critically, FEAST source tracking revealed that rhizosphere soil contributed 23.8%, 13.4%, 17.9%, and 10.8% of the bacterial communities in roots, stems, leaves, and seeds, respectively, identifying soil as the primary source of endophytic bacteria. These findings highlight the pivotal role of arid-region soil microbial diversity in shaping the unique endophytic microbiome of V. anthelmintica, providing a scientific basis for conserving soil microbial health to support its standardized cultivation and sustainable utilization in Uyghur medicine.

This paper proposes a novel solution to suppress backward wave oscillation (BWO) in high-order mode (HOM) sheet beam (SB) slow-wave structures (SWSs) and designs an isolator between cavities based on a Bragg resonator. This method can cut-off the backward wave signal path without interrupting the operating signal path, thereby eliminating BWO while maintaining high circuit gain. Simulation results show that the S21 parameter of the isolator is less than −20 dB from 175 GHz to 228 GHz. To verify the method’s performance, particle-in-cell (PIC) simulation was conducted based on a HOM SB SWS—a T-slot coupled-cavity (TSCC) SWS. Results indicate that this method can effectively suppress BWO and shows significant improvement in gain and output power compared to traditional methods such as sever or lossy loading. Under operating conditions of 34.4 kV and 0.35 A, the circuit achieves a maximum output power of 527 W at 216 GHz, a maximum gain of 36.39 dB at 214.4 GHz, and a bandwidth of 3 GHz where the output power exceeds 300 W.

The agricultural sector in the world and Sub-Saharan Africa faces the pressing challenge of meeting the growing food demand driven by the exponential population growth. With smallholder poultry farming playing a critical role in food and nutritional security, this systematic review synthesizes literature from the past two decades to assess the adoption of Information and Communication Technologies (ICTs) among smallholder poultry farmers in Sub-Saharan Africa (SSA). The review focuses on the benefits and barriers impacting this adoption. Following the PRISMA methodology, 19 peer-reviewed studies were analyzed to explore how ICT facilitates market participation, enhances information exchange, and improves producer livelihoods. The included studies in this review were sourced from four major academic databases: Science Direct, Web of Science, Wiley online library, and EBSCOhost. The findings reveal that ICT adoption significantly reduces information asymmetry, enables farmers to access market and production knowledge, and thus improves their profitability and inclusion in informal and formal market platforms. The review underscores the potential of targeted policy interventions and digital platforms to empower smallholder poultry farmers, enhance their commercialization, and contribute towards agricultural sustainability in the region. This study highlights the critical need for increased ICT accessibility, capacity building, and infrastructural improvements to support the digital transformation of smallholder poultry farming in Sub-Saharan Africa.

(1) Background: Bees of the genus Apis play a fundamental role in ecosystems thanks to their pollination activities and their long evolutionary history. This has resulted in species diversifying and spreading across Asia, Africa, and Europe. This review contextualises the genus within biogeographic and evolutionary frameworks, emphasising the importance of understanding the origins, adaptations, distribution and differences between species. (2) Methods: Recent studies on the biology, taxonomy and ecology of Apis species were analysed, including research on social behaviour, communication, genetics, morphology and environmental adaptations, as well as contributions using modern evolutionary and phylogeographic analytical methods. (3) Results: The gathered evidence shows that anthropogenic factors, including climate change, habitat loss, intensive agriculture, pollutants, competition with other bees and the spread of parasites and pathogens, significantly affect the stability of Apis populations and increase the vulnerability of wild species. (4) Conclusions: This review emphasises the importance of integrating ecological, genetic and management knowledge to develop effective conservation strategies that aim to reduce the impact of human activities and preserve the resilience of Apis species and the vital ecosystem services they provide.

Standard SAM-based approaches in medical imaging typically rely on explicit geometric prompts, such as bounding boxes or points. However, these rigid spatial constraints are often insufficient for capturing the complex, deformable boundaries of medical structures, where localization noise easily propagates into segmentation errors. To overcome this, we propose the Localization Distillation-Enhanced Feature Prompting SAM (LDFSAM), a novel framework that shifts from discrete coordinate inputs to a latent feature prompting paradigm. We employ a lightweight prompt generator, refined via Localization Distillation (LD), to inject multi-scale features into the SAM decoder as complementary Dense Feature Prompts (DFPs) and Sparse Feature Prompts (SFPs). This effectively guides segmentation without explicit box constraints. Extensive experiments on four public benchmarks (3D CBCT Tooth, ISIC 2018, MMOTU, and Kvasir-SEG) demonstrate that LDFSAM outperforms both prior SAM-based baselines and conventional networks, achieving Dice scores exceeding 0.91. Further validation on an in-house cohort demonstrates its robust generalization capabilities. Overall, our method outperforms both prior SAM-based baselines and conventional networks, with particularly strong gains in low-data regimes, providing a reliable solution for automated medical image segmentation.

This cross-sectional study aimed to assess the relationship between the level of physical activity and the severity of depressive symptoms in adult women, taking into account the mediating role of perceived stress and differences in this relationship across levels of depressive symptom severity. The study included 200 women aged 18–65 years. Physical activity was assessed using the International Physical Activity Questionnaire (IPAQ), depressive symptom severity was measured with the Beck Depression Inventory (BDI), and perceived stress was evaluated using a standardized stress scale. Descriptive statistics, non-parametric tests, hierarchical regression, mediation analysis with bootstrapping, and quantile regression (Q25, Q50, Q75) were applied, controlling for selected sociodemographic variables. The results showed that perceived stress was the strongest predictor of depressive symptom severity. Physical activity was not a significant independent predictor of depression after adjustment for stress; however, it demonstrated a significant indirect effect through stress reduction. Quantile regression analysis revealed that the protective effect of physical activity was more pronounced in the lower and middle quantiles of depressive symptom severity and attenuated at higher levels of severity. These findings indicate that the relationship between physical activity and depressive symptoms is predominantly indirect and conditional, supporting the integration of physical activity promotion with stress-reduction–focused interventions.