Acute Eosinophilic Pneumonia Induced by Immune Checkpoint Inhibitor and Anti-TIGIT Therapy.

BACKGROUND Immune checkpoint inhibitors (ICIs) have been linked to various immune-related adverse events, including pneumonitis, necessitating early recognition and potential treatment discontinuation. Acute eosinophilic pneumonia (AEP) induced by ICIs, particularly with no reported cases involving anti-TIGIT therapy, is rare. This report describes a case of AEP following treatment with pembrolizumab and anti-TIGIT therapy. CASE REPORT A 46-year-old woman with lung adenoid cystic carcinoma and chronic hypoxemic respiratory failure on long-term oxygen therapy presented with fever, cough, and shortness of breath. She underwent left pneumonectomy and radiation therapy at diagnosis 9 years earlier. She was participating in a clinical trial using pembrolizumab and anti-TIGIT EOS-448, due to cancer progression. After starting therapy, she developed stable peripheral eosinophilia and a skin rash, suggestive of a drug reaction. On admission, she was in acute-on-chronic hypoxemic respiratory failure, febrile, with an elevated eosinophil count and new multifocal infiltrates in the right lung. Despite broad antibiotics coverage for pneumonia, she developed worsening respiratory symptoms and eosinophilia. She was then empirically started on intravenous methylprednisolone for acute eosinophilic pneumonia without confirmatory bronchoscopy as she was at high risk with her previous pneumonectomy. She subsequently had rapid improvement in her symptoms. CONCLUSIONS AEP should be considered in patients treated with ICIs who develop immune-related adverse effects. Although bronchoscopy findings are part of AEP's diagnostic criteria, this case underscores the importance of clinical judgment in the prompt initiation of steroids, even without confirmatory bronchoscopy, in rapidly progressing cases. The role of anti-TIGIT therapy in this context remains uncertain.

Multiple algorithms highlight key brain genes driven by multiple anesthetics.

Anesthesia serves as a pivotal tool in modern medicine, creating a transient state of sensory deprivation to ensure a pain-free surgical or medical intervention. While proficient in alleviating pain, anesthesia significantly modulates brain dynamics, metabolic processes, and neural signaling, thereby impairing typical cognitive functions. Furthermore, anesthesia can induce notable impacts such as memory impairment, decreased cognitive function, and diminished intelligence, emphasizing the imperative need to explore the concealed repercussions of anesthesia on individuals. In this investigation, we aggregated gene expression profiles (GSE64617, GSE141242, GSE161322, GSE175894, and GSE178995) from public repositories following second-generation sequencing analysis of various anesthetics. Through scrutinizing post-anesthesia brain tissue gene expression utilizing Gene Set Enrichment Analysis (GSEA), Robust Rank Aggregation (RRA), and Weighted Gene Co-expression Network Analysis (WGCNA), this research aims to pinpoint pivotal genes, pathways, and regulatory networks linked to anesthesia. This undertaking not only enhances comprehension of the physiological changes brought about by anesthesia but also lays the groundwork for future investigations, cultivating new insights and innovative perspectives in medical practice.

A GalNAc-modified CaCO3 nano-immunomodulator for targeted and responsive immunotherapy against orthotopic liver cancer.

A nano-immunomodulator modified with N-acetylgalactosamine (GalNAc) on calcium carbonate (CaCO3) was prepared for targeted and responsive immunotherapy. And the immunologic adjuvant (CpG ODNs) and doxorubicin (DOX) were released to synergistically improve immune response for treating orthotopic liver cancer.

Reprogramming Tumor-Associated Macrophages with a Se-Based Core-Satellite Nanoassembly to Enhance Cancer Immunotherapy.

Tumor-associated macrophages (TAMs), as the most prevalent immune cells in the tumor microenvironment, play a pivotal role in promoting tumor development through various signaling pathways. Herein, we have engineered a Se@ZIF-8 core-satellite nanoassembly to reprogram TAMs, thereby enhancing immunotherapy outcomes. When the nanoassembly reaches the tumor tissue, selenium nanoparticles and Zn2+ are released in response to the acidic tumor microenvironment, resulting in a collaborative effort to promote the production of reactive oxygen species (ROS). The generated ROS, in turn, activate the nuclear factor κB (NF-κB) signaling pathway, driving the repolarization of TAMs from M2-type to M1-type, effectively eliminating cancer cells. Moreover, the nanoassembly can induce the immunogenic death of cancer cells through excess ROS to expose calreticulin and boost macrophage phagocytosis. The Se@ZIF-8 core-satellite nanoassembly provides a potential paradigm for cancer immunotherapy by reversing the immunosuppressive microenvironment.

Epigenetic modification of PHLDA2 is associated with tumor microenvironment and unfavorable outcome of immune checkpoint inhibitor-based therapies in clear cell renal cell carcinoma.

BACKGROUND: A substantial proportion of patients with metastatic clear cell renal cell carcinoma (ccRCC) cannot derive benefit from immune checkpoint inhibitor (ICI) plus anti-angiogenic agent combination therapy, making identification of predictive biomarkers an urgent need. The members of pleckstrin homology-like domain family A (PHLDA) play critical roles in multiple cancers, whereas their roles in ccRCC remain unknown. METHODS: Transcriptomic, clinical, genetic alteration and DNA methylation data were obtained for integrated analyses from TCGA database. RNA sequencing was performed on 117 primary tumors and 79 normal kidney tissues from our center. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis were performed to explore transcriptomic features. Data from three randomized controlled trials (RCT), including CheckMate025, IMmotion151, JAVELIN101, were obtained for validation. RESULTS: Members of PHLDA family were dysregulated in pan-cancer. Elevated PHLDA2 expression was associated with adverse clinicopathologic parameters and worse prognosis in ccRCC. Aberrant DNA hypomethylation contributed to up-regulation of PHLDA2. An immunosuppressive microenvironment featured by high infiltrates of Tregs and cancer-associated fibroblasts, was observed in ccRCC with higher PHLDA2 expression. Utilizing data from three RCTs, the association of elevated PHLDA2 expression with poor therapeutic efficacy of ICI plus anti-angiogenic combination therapy was confirmed. CONCLUSIONS: Our study revealed that elevated PHLDA2 expression regulated by DNA hypomethylation was correlated with poor prognosis and immunosuppressive microenvironment, and highlighted the role of PHLDA2 as a robust biomarker for predicting therapeutic efficacy of ICI plus anti-angiogenic agent combination therapy in ccRCC, which expand the dimension of precision medicine.

Arbuscular mycorrhizal fungi attenuate negative impact of drought on soil functions.

Although positive effects of arbuscular mycorrhizal (AM) fungi on plant performance under drought have been well documented, how AM fungi regulate soil functions and multifunctionality requires further investigation. In this study, we first performed a meta-analysis to test the potential role of AM fungi in maintaining soil functions under drought. Then, we conducted a greenhouse experiment, using a pair of hyphal ingrowth cores to spatially separate the growth of AM fungal hyphae and plant roots, to further investigate the effects of AM fungi on soil multifunctionality and its resistance against drought. Our meta-analysis showed that AM fungi promote multiple soil functions, including soil aggregation, microbial biomass and activities of soil enzymes related to nutrient cycling. The greenhouse experiment further demonstrated that AM fungi attenuate the negative impact of drought on these soil functions and thus multifunctionality, therefore, increasing their resistance against drought. Moreover, this buffering effect of AM fungi persists across different frequencies of water supply and plant species. These findings highlight the unique role of AM fungi in maintaining multiple soil functions by mitigating the negative impact of drought. Our study highlights the importance of AM fungi as a nature-based solution to sustaining multiple soil functions in a world where drought events are intensifying.

Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.

INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C2h), as an indicator of safety and efficacy, are important for optimizing therapy. OBJECTIVE: The objective of this study was to establish machine learning (ML) models to predict the C2h, that can be used for establishing an individualized dosing regimen in clinical practice. METHODS: Published population pharmacokinetic (PopPK) models for adults were searched based on PubMed and ultimately four reliable models were selected for simulating individual C2h datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C2h obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C2h. Real-world data were used for predictive performance evaluations. Virtual trials were used to compare ML-optimized doses with PopPK model-optimized doses. RESULTS: Categorical boosting (CatBoost) exhibited the highest prediction ability. Target C2h can be predicted using the ML model combined with the dosing regimen and three covariates (N-acetyltransferase 2 [NAT2] genotypes, weight and race [Asians and Africans]). Real-world data validation results showed that the ML model can achieve an overall prediction accuracy of 93.4%. Using the final ML model, the mean absolute prediction error value decreased by 45.7% relative to the average of PopPK models. Using the ML-optimized dosing regimen, the probability of target attainment increased by 43.7% relative to the PopPK model-optimized dosing regimens. CONCLUSION: Machine learning models were developed with great predictive performance, which can be used to determine the individualized initial dose of isoniazid in adult patients.

The inferior vena cava filter placement parameters may predict filter retrieval outcomes.

OBJECTIVE: We aimed to investigate the potential correlation between the placement factors of various retrievable inferior vena cava filters and retrieval outcomes. Additionally, we aimed to identify the factors affecting the placement tilt of the filter. MATERIALS AND METHODS: This retrospective study was conducted at a tertiary care center to investigate patients who had previously undergone retrievable filter placement at our center and who subsequently had their filters removed between January 2020 and December 2021. Patient characteristics and filter-related factors were recorded. Complex filter retrieval was defined as cases that required a minimum of 8 minutes of fluoroscopy or that involved advanced techniques. Regression models were used to explore patient- and placement procedure-related factors that could influence retrieval outcomes and the placement tilt angle. RESULTS: The study included 163 patients, and all filters were successfully retrieved. Thirty-seven (22.7%) retrievals were classified as complex retrievals. The mean diameter of the inferior vena cava in the preplacement position for the entire cohort was 16 ± 1.8 mm. The median filter tilt angles at placement and retrieval were 5.0° (IQR, 1.8°- 9°) and 4.6° (IQR, 2.1°-8.0°), respectively. The placement tilt angle was not significantly associated with complex retrieval (p=0.59). The filter hook abutment to the vena cava wall (OR, 10.76, p = 0.003), dwell time (OR, 1.02, p =0.029), and diameter of the vena cava (OR, 10.21, p < 0.001) were associated with complex retrieval. The diameter (p=0.049), age (p=0.049), and filter brand (p=0.001) were found to be significantly associated with placement tilt. CONCLUSIONS: The inferior vena cava diameter at the time of placement predicts difficulty in filter retrieval. In addition, the filter hook abutting the IVC wall and long indwelling time may complicate retrieval. The vena cava diameter is also closely related to the degree of filter tilt.

A Bioinspired Membrane with Ultrahigh Li+/Na+ and Li+/K+ Separations Enables Direct Lithium Extraction from Brine.

Membranes with precise Li+/Na+ and Li+/K+ separations are imperative for lithium extraction from brine to address the lithium supply shortage. However, achieving this goal remains a daunting challenge due to the similar valence, chemical properties, and subtle atomic-scale distinctions among these monovalent cations. Herein, inspired by the strict size-sieving effect of biological ion channels, a membrane is presented based on nonporous crystalline materials featuring structurally rigid, dimensionally confined, and long-range ordered ion channels that exclusively permeate naked Li+ but block Na+ and K+. This naked-Li+-sieving behavior not only enables unprecedented Li+/Na+ and Li+/K+ selectivities up to 2707.4 and 5109.8, respectively, even surpassing the state-of-the-art membranes by at least two orders of magnitude, but also demonstrates impressive Li+/Mg2+ and Li+/Ca2+ separation capabilities. Moreover, this bioinspired membrane has to be utilized for creating a one-step lithium extraction strategy from natural brines rich in Na+, K+, and Mg2+ without utilizing chemicals or creating solid waste, and it simultaneously produces hydrogen. This research has proposed a new type of ion-sieving membrane and also provides an envisioning of the design paradigm and development of advanced membranes, ion separation, and lithium extraction.

CRISPR-Cas12a-based ultrasensitive assay for visual detection of SARS-CoV-2 RNA.

The development of ultrasensitive and visual methods is of great significance for molecular diagnosis at the point-of-care. In this study, we have integrated recombinase polymerase amplification (RPA) with the CRISPR-Cas12a system to design an ultrasensitive strategy for visual nucleic acid testing. RPA is utilized to amplify the target nucleic acid, producing amplicons that activate the single-stranded DNase property of CRISPR-Cas12a. The activated CRISPR-Cas12a then degrades the single-stranded DNA on magnetic nanoparticles (MNPs), releasing immobilized GOx from the MNPs which catalyses the chromogenic substrate. The developed method exhibits remarkable sensitivity, successfully detecting as low as 10 aM (∼6 copies per µL) of the target nucleic acid by visual colour changes in solution. The instrumental limit of detection is calculated to be 2.86 aM (∼2 copies per µL), comparable to the sensitivity of polymerase chain reaction (PCR). Importantly, this approach only requires isothermal incubation operation and does not involve costly instruments. The method has been validated by visually detecting the SARS-CoV-2 RNA gene fragment within 50 minutes. With its ultrasensitivity, simplicity of operation, and potential for integration into a point-of-care detection kit, this strategy holds great promise for nucleic acid testing in various settings.

A lateral flow assay strip for simultaneous detection of miRNA and exosomes in liver cancer.

By employing an aptamer as the bridge and combining catalytic hairpin assembly with the Au aggregation amplification effect, a lateral flow assay (LFA) is designed for simultaneous detection of liver cancer-associated miRNA and exosomes. The LFA can differentiate between liver cancer patients and healthy individuals with simple operation and high accuracy.

Organelle-based immunotherapy strategies for fighting against cancer.

Destruction of subcellular organelles can cause dysfunction and even death of cells to elicit immune responses. In this review, the characteristics and functions of important organelles are mainly summarized. Then, the intelligent immunotherapeutic strategies and suggestions based on influencing the organelles are further highlighted. This review will provide ideas for developing novel and effective immunotherapy strategies and advance the development of cancer immunotherapy.

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors.

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Aqueous alternating electrolysis prolongs electrode lifespans under harsh operation conditions.

It is vital to explore effective ways for prolonging electrode lifespans under harsh electrolysis conditions, such as high current densities, acid environment, and impure water source. Here we report alternating electrolysis approaches that realize promptly and regularly repair/maintenance and concurrent bubble evolution. Electrode lifespans are improved by co-action of Fe group elemental ions and alkali metal cations, especially a unique Co2+-Na+ combo. A commercial Ni foam sustains ampere-level current densities alternatingly during continuous electrolysis for 93.8 h in an acidic solution, whereas such a Ni foam is completely dissolved in ~2 h for conventional electrolysis conditions. The work not only explores an alternating electrolysis-based system, alkali metal cation-based catalytic systems, and alkali metal cation-based electrodeposition techniques, and beyond, but demonstrates the possibility of prolonged electrolysis by repeated deposition-dissolution processes. With enough adjustable experimental variables, the upper improvement limit in the electrode lifespan would be high.

Transcriptome Analysis Reveals Antioxidant Defense Mechanisms in the Silkworm Bombyx mori after Exposure to Lead.

Lead (Pb) is a major source of heavy metal contamination, and poses a threat to biodiversity and human health. Elevated levels of Pb can hinder insect growth and development, leading to apoptosis via mechanisms like oxidative damage. The midgut of silkworms is the main organ exposed to heavy metals. As an economically important lepidopteran model insect in China, heavy metal-induced stress on silkworms causes considerable losses in sericulture, thereby causing substantial economic damage. This study aimed to investigate Pb-induced detoxification-related genes in the midgut of silkworms using high-throughput sequencing methods to achieve a deeper comprehension of the genes' reactions to lead exposure. This study identified 11,567 unigenes and 14,978 transcripts. A total of 1265 differentially expressed genes (DEGs) were screened, comprising 907 upregulated and 358 downregulated genes. Subsequently, Gene Ontology (GO) classification analysis revealed that the 1265 DEGs were distributed across biological processes, cellular components, and molecular functions. This suggests that the silkworm midgut may affect various organelle functions and biological processes, providing crucial clues for further exploration of DEG function. Additionally, the expression levels of 12 selected detoxification-related DEGs were validated using qRT-PCR, which confirmed the reliability of the RNA-seq results. This study not only provides new insights into the detoxification defense mechanisms of silkworms after Pb exposure, but also establishes a valuable foundation for further investigation into the molecular detoxification mechanisms in silkworms.

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.

Nickel-catalyzed hydrodefluorination/deuterodefluorination of CF3-alkenes with formic acid.

The synthesis of deuterated gem-difluoroalkenes via selective deuterodefluorination of ß-CF3-cinnamates using a nickel catalyst has been reported for the first time. Commercially available deuterated formic acid is a cheap and convenient deuterium source. The nickel-catalyst showed high selectivity for monodefluorination and avoided competitive reactions such as multiple defluorination or hydrogenation.

Sources and applications of endothelial seed cells: a review.

Endothelial cells (ECs) are widely used as donor cells in tissue engineering, organoid vascularization, and in vitro microvascular model development. ECs are invaluable tools for disease modeling and drug screening in fundamental research. When treating ischemic diseases, EC engraftment facilitates the restoration of damaged blood vessels, enhancing therapeutic outcomes. This article presents a comprehensive overview of the current sources of ECs, which encompass stem/progenitor cells, primary ECs, cell lineage conversion, and ECs derived from other cellular sources, provides insights into their characteristics, potential applications, discusses challenges, and explores strategies to mitigate these issues. The primary aim is to serve as a reference for selecting suitable EC sources for preclinical research and promote the translation of basic research into clinical applications.

Research on short-term photovoltaic power generation forecasting model based on multi-strategy improved squirrel search algorithm and support vector machine.

Solar photovoltaic (PV) power generation is susceptible to environmental factors, and redundant features can disrupt prediction accuracy. To achieve rapid and accurate online prediction, we propose a method that combines Principal Component Analysis (PCA) with a multi-strategy improved Squirrel Search Algorithm (SSA) to optimize Support Vector Machine (MISSA-SVM) for prediction. Initially, to mitigate the impact of redundant features on prediction accuracy, KPCA is employed for feature dimensionality reduction. Subsequently, SVM is suggested as the foundational algorithm for constructing the prediction model. Furthermore, to address the influence of hyperparameter selection on model performance, SSA is introduced for optimizing SVM hyperparameters, with the aim of establishing the optimal prediction model. Moreover, to enhance solution efficiency and accuracy, a multi-strategy approach termed MISSA is proposed, which integrates Population Initialization based on the Tent map, Nonlinear Predator Presence Probability, Chaotic-based Dynamic Opposition-based Learning, and Selection Strategy, to refine SSA. Finally, through case studies, the performance of MISSA optimization is assessed using challenging CEC2021 test functions, demonstrating its high optimization performance, stability, and significance. Subsequently, the performance of the prediction model is validated using two datasets, showcasing that the proposed prediction method achieves high accuracy and robust prediction stability.

DNAzyme-RCA-based colorimetric and lateral flow dipstick assays for the point-of-care testing of exosomal m5C-miRNA-21.

Methylation of microRNAs (miRNAs) is a post-transcriptional modification that affects miRNA activity by altering the specificity of miRNAs to target mRNAs. Abnormal methylation of miRNAs in cancer suggests their potential as a tumor marker. However, the traditional methylated miRNA detection mainly includes mass spectrometry, sequencing and others; complex procedures and reliance on large instruments greatly limit their application in point-of-care testing (POCT). Based on this, we developed DNAzyme-RCA-based gold nanoparticle (AuNP) colorimetric and lateral flow dipstick (LFD) assays to achieve convenient detection of exosomal 5-methylcytosine miRNA-21 (m5C-miRNA-21) for the first time. The two assays achieved specific recognition and linear amplification of m5C-miRNA-21 through the DNAzyme triggered RCA reaction and color output with low background interference through AuNP aggregation induced by base complementary pairing. The lowest concentration of m5C-miRNA-21 visible to the naked eye of the two assays can reach 1 pM and 0.1 pM, respectively. Detection of exosomal m5C-miRNA-21 in clinical blood samples showed that the expression level of m5C-miRNA-21 in colorectal cancer patients was significantly higher than that in healthy individuals. This approach not only demonstrates a new strategy for the detection of colorectal cancer but also provides a reference for the development of novel diagnostic tools for other miRNA methylation-related diseases.