Distinct developmental reprogramming footprint of macrophages during acute kidney injury across species.

Acute kidney injury (AKI) is a common finding in hospitalized patients, particularly those who are critically ill. The development of AKI is associated with several adverse outcomes including mortality, morbidity, progression to chronic kidney disease, and an increase in healthcare expenditure. Despite the well-established negative impact of AKI and rigorous efforts to better define, identify, and implement targeted therapies, the overall approach to the treatment of AKI continues to principally encompass supportive measures. This enduring challenge is primarily due to the heterogeneous nature of insults that activate many independent and overlapping molecular pathways. Consequently, it is evident that the identification of common mechanisms that mediate the pathogenesis of AKI, independent of etiology and engaged pathophysiological pathways, is of paramount importance and could lead to the identification of novel therapeutic targets. To better distinguish the commonly modulated mechanisms of AKI, we explored the transcriptional characteristics of human kidney biopsies from patients with acute tubular necrosis (ATN), and acute interstitial nephritis (AIN) using a NanoString inflammation panel. Subsequently, we used publicly available single-cell transcriptional resources to better interpret the generated transcriptional findings. Our findings identify robust acute kidney injury (AKI-induced) developmental reprogramming of macrophages (MΦ) with the expansion of C1Q+, CD163+ MΦ that is independent of the etiology of AKI and conserved across mouse and human species. These results would expand the current understanding of the pathophysiology of AKI and potentially offer novel targets for additional studies to enhance the translational transition of AKI research.NEW & NOTEWORTHY Our findings identify robust acute kidney injury (AKI)-induced developmental reprogramming of macrophages (MΦ) with the expansion of C1Q+, CD163+ MΦ that is independent of the etiology of AKI and conserved across mouse and human species.

Synergistic rescue of temperature-sensitive p53 mutants by hypothermia and arsenic trioxide.

The p53 tumor suppressor is inactivated by mutations in about 50% of tumors. Rescuing the transcriptional function of mutant p53 has potential therapeutic benefits. Approximately 15% of p53 mutants are temperature sensitive (TS) and regain maximal activity at 32°C. Proof of concept study showed that induction of 32°C hypothermia in mice restored TS mutant p53 activity and inhibited tumor growth. However, 32°C is the lower limit of therapeutic hypothermia procedures for humans. Higher temperatures are preferable but result in suboptimal TS p53 activation. Recently, arsenic trioxide (ATO) was shown to rescue the conformation of p53 structural mutants by stabilizing the DNA binding domain. We examined the responses of 17 frequently observed p53 TS mutants to functional rescue by temperature shift and ATO. The results showed that ATO only rescued mild p53 TS mutants with high basal activity at 37°C. Mild TS mutants showed a common feature of regaining significant activity at the semi-permissive temperature of 35°C and could be further stimulated by ATO at 35°C. TS p53 rescue by ATO was antagonized by the cellular redox mechanism and was rapidly reversible. Inhibition of glutathione (GSH) biosynthesis enhanced ATO rescue efficiency and sustained p53 activity after ATO washout. The results suggest that mild TS p53 mutants are uniquely responsive to functional rescue by ATO due to small thermostability deficits and inherent potential to regain active conformation. Combining mild hypothermia and ATO may provide an effective and safe procedure for targeting tumors with p53 TS mutations.

Numerical Simulation Study on Combustion of Low Calorific Value Waste Blended with Biomass.

Numerical simulations of a 600 t/day waste incinerator was carried out using the fluid dynamic incinerator code and Fluent to evaluate the effect of biomass blending on furnace temperature, pollutant generation, and selective noncatalytic-reduction (SNCR) denitrification when treating low calorific-value waste. The results show that as the biomass blending ratio increases, the water content gradually decreases, the calorific value increases, and the maximum temperature of the incinerator gradually increases from 1227 to 1408 K, while the content of exported NOx increases from 579 to 793 mg/Nm3; during the combustion of low-quality waste, the residence time of the flue gas in the high-temperature region (above 1123 K) is 1.62 s. When the biomass blending ratio exceeds 20%, the residence time of the flue gas in the high-temperature region is more than 2 s, which can effectively curb the generation of dioxin. When the biomass blending ratio is 20%, and the normalized stoichiometric ratio (2nurea/nNO) of urea injected into the SNCR is 1.1, the NOx concentration at the outlet is 230.08 mg/Nm3, which satisfies the NOx emission standard of less than 250 mg/Nm3.

An in-depth evaluation of federated learning on biomedical natural language processing for information extraction.

Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: (1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data; (2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. (3) FL models significantly outperformed pre-trained LLMs with few-shot prompting.

A non-linear partial least squares based on monotonic inner relation.

A novel regression model, monotonic inner relation-based non-linear partial least squares (MIR-PLS), is proposed to address complex issues like limited observations, multicollinearity, and nonlinearity in Chinese Medicine (CM) dose-effect relationship experimental data. MIR-PLS uses a piecewise mapping function based on monotonic cubic splines to model the non-linear inner relations between input and output score vectors. Additionally, a new weight updating strategy (WUS) is developed by leveraging the properties of monotonic functions. The proposed MIR-PLS method was compared with five well-known PLS variants: standard PLS, quadratic PLS (QPLS), error-based QPLS (EB-QPLS), neural network PLS (NNPLS), and spline PLS (SPL-PLS), using CM dose-effect relationship datasets and near-infrared (NIR) spectroscopy datasets. Experimental results demonstrate that MIR-PLS exhibits general applicability, achieving excellent predictive performances in the presence or absence of significant non-linear relationships. Furthermore, the model is not limited to CM dose-effect relationship research and can be applied to other regression tasks.

Feature selection based on neighborhood rough sets and Gini index.

Neighborhood rough set is considered an essential approach for dealing with incomplete data and inexact knowledge representation, and it has been widely applied in feature selection. The Gini index is an indicator used to evaluate the impurity of a dataset and is also commonly employed to measure the importance of features in feature selection. This article proposes a novel feature selection methodology based on these two concepts. In this methodology, we present the neighborhood Gini index and the neighborhood class Gini index and then extensively discuss their properties and relationships with attributes. Subsequently, two forward greedy feature selection algorithms are developed using these two metrics as a foundation. Finally, to comprehensively evaluate the performance of the algorithm proposed in this article, comparative experiments were conducted on 16 UCI datasets from various domains, including industry, food, medicine, and pharmacology, against four classical neighborhood rough set-based feature selection algorithms. The experimental results indicate that the proposed algorithm improves the average classification accuracy on the 16 datasets by over 6%, with improvements exceeding 10% in five. Furthermore, statistical tests reveal no significant differences between the proposed algorithm and the four classical neighborhood rough set-based feature selection algorithms. However, the proposed algorithm demonstrates high stability, eliminating most redundant or irrelevant features effectively while enhancing classification accuracy. In summary, the algorithm proposed in this article outperforms classical neighborhood rough set-based feature selection algorithms.