Genetic Dissection of Diverse Seed Coat Patterns in Cowpea through a Comprehensive GWAS Approach.

This study investigates the genetic determinants of seed coat color and pattern variations in cowpea (Vigna unguiculata), employing a genome-wide association approach. Analyzing a mapping panel of 296 cowpea varieties with 110,000 single nucleotide polymorphisms (SNPs), we focused on eight unique coat patterns: (1) Red and (2) Cream seed; (3) White and (4) Brown/Tan seed coat; (5) Pink, (6) Black, (7) Browneye and (8) Red/Brown Holstein. Across six GWAS models (GLM, SRM, MLM, MLMM, FarmCPU from GAPIT3, and TASSEL5), 13 significant SNP markers were identified and led to the discovery of 23 candidate genes. Among these, four specific genes may play a direct role in determining seed coat pigment. These findings lay a foundational basis for future breeding programs aimed at creating cowpea varieties aligned with consumer preferences and market requirements.

Breaking Barriers: Nanomedicine-Based Drug Delivery for Cataract Treatment.

Cataract is a leading cause of blindness globally, and its surgical treatment poses a significant burden on global healthcare. Pharmacologic therapies, including antioxidants and protein aggregation reversal agents, have attracted great attention in the treatment of cataracts in recent years. Due to the anatomical and physiological barriers of the eye, the effectiveness of traditional eye drops for delivering drugs topically to the lens is hindered. The advancements in nanomedicine present novel and promising strategies for addressing challenges in drug delivery to the lens, including the development of nanoparticle formulations that can improve drug penetration into the anterior segment and enable sustained release of medications. This review introduces various cutting-edge drug delivery systems for cataract treatment, highlighting their physicochemical properties and surface engineering for optimal design, thus providing impetus for further innovative research and potential clinical applications of anti-cataract drugs.

Network-based approach for drug repurposing against mpox.

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

Global pattern and determinant for interaction of seasonal influenza viruses.

BACKGROUND: The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored. METHODS: Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM). RESULTS: A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1. CONCLUSION: The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.

Characterizing the antigenic evolution of pandemic influenza A (H1N1) pdm09 from 2009 to 2023.

The H1N1pdm09 virus has been a persistent threat to public health since the 2009 pandemic. Particularly, since the relaxation of COVID-19 pandemic mitigation measures, the influenza virus and SARS-CoV-2 have been concurrently prevalent worldwide. To determine the antigenic evolution pattern of H1N1pdm09 and develop preventive countermeasures, we collected influenza sequence data and immunological data to establish a new antigenic evolution analysis framework. A machine learning model (XGBoost, accuracy = 0.86, area under the receiver operating characteristic curve = 0.89) was constructed using epitopes, physicochemical properties, receptor binding sites, and glycosylation sites as features to predict the antigenic similarity relationships between influenza strains. An antigenic correlation network was constructed, and the Markov clustering algorithm was used to identify antigenic clusters. Subsequently, the antigenic evolution pattern of H1N1pdm09 was analyzed at the global and regional scales across three continents. We found that H1N1pdm09 evolved into around five antigenic clusters between 2009 and 2023 and that their antigenic evolution trajectories were characterized by cocirculation of multiple clusters, low-level persistence of former dominant clusters, and local heterogeneity of cluster circulations. Furthermore, compared with the seasonal H1N1 virus, the potential cluster-transition determining sites of H1N1pdm09 were restricted to epitopes Sa and Sb. This study demonstrated the effectiveness of machine learning methods for characterizing antigenic evolution of viruses, developed a specific model to rapidly identify H1N1pdm09 antigenic variants, and elucidated their evolutionary patterns. Our findings may provide valuable support for the implementation of effective surveillance strategies and targeted prevention efforts to mitigate the impact of H1N1pdm09.

Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data.

Background: Central precocious puberty (CPP) is a common endocrine disorder in children, and its diagnosis primarily relies on the gonadotropin-releasing hormone (GnRH) stimulation test, which is expensive and time-consuming. With the widespread application of artificial intelligence in medicine, some studies have utilized clinical, hormonal (laboratory) and imaging data-based machine learning (ML) models to identify CPP. However, the results of these studies varied widely and were challenging to directly compare, mainly due to diverse ML methods. Therefore, the diagnostic value of clinical, hormonal (laboratory) and imaging data-based ML models for CPP remains elusive. The aim of this study was to investigate the diagnostic value of ML models based on clinical, hormonal (laboratory) and imaging data for CPP through a meta-analysis of existing studies. Methods: We conducted a comprehensive search for relevant English articles on clinical, hormonal (laboratory) and imaging data-based ML models for diagnosing CPP, covering the period from the database creation date to December 2023. Pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) were calculated to assess the diagnostic value of clinical, hormonal (laboratory) and imaging data-based ML models for diagnosing CPP. The I2 test was employed to evaluate heterogeneity, and the source of heterogeneity was investigated through meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test. Results: Six studies met the eligibility criteria. The pooled sensitivity and specificity were 0.82 (95% confidence interval (CI) 0.62-0.93) and 0.85 (95% CI 0.80-0.90), respectively. The LR+ was 6.00, and the LR- was 0.21, indicating that clinical, hormonal (laboratory) and imaging data-based ML models exhibited an excellent ability to confirm or exclude CPP. Additionally, the SROC curve showed that the AUC of the clinical, hormonal (laboratory) and imaging data-based ML models in the diagnosis of CPP was 0.90 (95% CI 0.87-0.92), demonstrating good diagnostic value for CPP. Conclusion: Based on the outcomes of our meta-analysis, clinical and imaging data-based ML models are excellent diagnostic tools with high sensitivity, specificity, and AUC in the diagnosis of CPP. Despite the geographical limitations of the study findings, future research endeavors will strive to address these issues to enhance their applicability and reliability, providing more precise guidance for the differentiation and treatment of CPP.

Deficiency of Cbfß in articular cartilage leads to osteoarthritis-like phenotype through Hippo/Yap, TGFß, and Wnt/ß-catenin signaling pathways.

Osteoarthritis (OA) is the most prevalent degenerative joint disorder, causing physical impairments among the elderly. Core binding factor subunit ß (Cbfß) has a critical role in bone homeostasis and cartilage development. However, the function and mechanism of Cbfß in articular cartilage and OA remains unclear. We found that Cbfßf/fAggrecan-CreERT mice with Cbfß-deficiency in articular cartilage developed a spontaneous osteoarthritis-like phenotype with articular cartilage degradation. Immunofluorescence staining showed that Cbfßf/fAggrecan-CreERT mice exhibited a significant increase in the expression of articular cartilage degradation markers and inflammatory markers in the knee joints. RNA-sequencing analysis demonstrated that Cbfß orchestrated Hippo/Yap, TGFß/Smad, and Wnt/ß-catenin signaling pathways in articular cartilage, and Cbfß deficiency resulted in the abnormal expression of downstream genes involved in maintaining articular cartilage homeostasis. Immunofluorescence staining results showed Cbfß deficiency significantly increased active ß-catenin and TCF4 expression while reducing Yap, TGFß1, and p-Smad 2/3 expression. Western blot and qPCR validated gene expression changes in hip articular cartilage of Cbfß-deficient mice. Our results demonstrate that deficiency of Cbfß in articular cartilage leads to an OA-like phenotype via affecting Hippo/Yap, TGFß, and Wnt/ß-catenin signaling pathways, disrupting articular cartilage homeostasis and leading to the pathological process of OA in mice. Our results indicate that targeting Cbfß may be a potential therapeutic target for the design of novel and effective treatments for OA.

Applications of protein ubiquitylation and deubiquitylation in drug discovery.

The ubiquitin (Ub)-proteasome system (UPS) is the major machinery mediating specific protein turnover in eukaryotic cells. By ubiquitylating unwanted, damaged, or harmful proteins and driving their degradation, UPS is involved in many important cellular processes. Several new UPS-based technologies, including molecular glue degraders and PROTACs (proteolysis-targeting chimeras) to promote protein degradation, and DUBTACs (deubiquitinase-targeting chimeras) to increase protein stability, have been developed. By specifically inducing the interactions between different Ub ligases and targeted proteins that are not otherwise related, molecular glue degraders and PROTACs degrade targeted proteins via the UPS; in contrast, by inducing the proximity of targeted proteins to deubiquitinases, DUBTACs are created to clear degradable poly-Ub chains to stabilize targeted proteins. In this review, we summarize the recent research progress in molecular glue degraders, PROTACs, and DUBTACs and their applications. We discuss immunomodulatory drugs, sulfonamides, cyclin-dependent kinase-targeting molecular glue degraders, and new development of PROTACs. We also introduce the principle of DUBTAC and its applications. Finally, we propose a few future directions of these three technologies related to targeted protein homeostasis.

Single Extracellular Vesicle Imaging and Computational Analysis Identifies Inherent Architectural Heterogeneity.

Evaluating the heterogeneity of extracellular vesicles (EVs) is crucial for unraveling their complex actions and biodistribution. Here, we identify consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM), which has an inherent ability to image biological samples without harsh labeling methods while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources, such as cancer cells, normal cells, immortalized cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,576 individual EVs were imaged and quantified by our computational pipeline. Across all 7,576 independent EVs, the average eccentricity was 0.5366 ± 0.2, and the average equivalent diameter was 132.43 ± 67 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical, rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution images of EVs and offer insights into their potential biological impact.

ETV6-NTRK2 Fusion in a Patient With Metastatic Pulmonary Atypical Carcinoid Successfully Treated With Entrectinib: A Case Report and Review of the Literature.

Pulmonary atypical carcinoid (AC) is an extremely rare neuroendocrine tumor. The neurotrophic tropomyosin receptor kinase (NTRK) fusions are reported in only 0.5% of nonsmall cell lung cancer, and are more rare in AC with only one previously reported case. Currently, there is little established evidence on the optimal therapeutic strategies and prognosis for advanced cases. We present a female patient with metastatic AC after complete resection. Due to low expression of somatostatin receptor in this case, somatostatin analogs and peptide receptor radionuclide therapy were not available. After pursuing other alternative treatments, including chemotherapy (ie, carboplatin, etoposide, capecitabine, temozolomide, and paclitaxel), everolimus, and atezolizumab, she returned with significant progression, including innumerable subcutaneous nodules, left pleura metastasis, multiple bone metastases, and brain metastases. New biopsy analysis revealed an ETV6-NTRK2 fusion. She was immediately administered the first-generation tropomyosin receptor kinase inhibitor entrectinib at a dose of 600 mg q.d. A subsequent month of treatment resulted in a complete response in all of the metastatic lung lesions. To date, she has maintained sustained benefit for at least 1 year from initiation of entrectinib. Here, we present the first case of a female patient with metastatic AC harboring the ETV6-NTRK2 fusion, and successfully treated with entrectinib, providing evidence for the application of entrectinib in patients with NTRK-positive AC, and underscoring the critical role of molecular profiling for such cases.

Tackling Challenges in Assessing the Economic Value of Tumor-Agnostic Therapies: A Cost-Effectiveness Analysis of Pembrolizumab as a Case Study.

OBJECTIVES: Assessing the value of tumor-agnostic drugs (TAD) is challenging given the potential variability in treatment effects, trials with small sample sizes, different standards of care (SoC), and lack of comparative data from single-arm basket trials. Our study developed and applied novel methods to assess the value of pembrolizumab compared with SoC to inform coverage decisions. METHODS: We developed a partitioned survival model to evaluate the cost-utility of pembrolizumab for previously treated patients with 8 advanced or metastatic microsatellite instability-high or mismatch repair-deficient cancers from a US commercial payer perspective. Efficacy of pembrolizumab was based on data from trials directly or with adjustment using Bayesian hierarchical models. Eight chemotherapy-based external control arms were constructed from the TriNetX electronic health record databases. Tumor-specific health-state utility values were applied. All costs were adjusted to 2022 US dollars. RESULTS: At a lifetime horizon, pembrolizumab was associated with increased effectiveness compared with chemotherapies in colorectal (quality-adjusted life years [QALYs]: +0.64, life years [LYs]: +0.64), endometrial (QALYs: +3.79, LYs: +5.47), and small intestine cancers (QALYs: +1.73, LYs: +2.48), but not for patients with metastatic gastric, cholangiocarcinoma, pancreatic, ovarian, and brain cancers. Incremental cost-effectiveness ratios varied substantially across tumor types. Pembrolizumab was found to be cost-effective in treating colorectal and endometrial cancers (incremental cost-effectiveness ratios: $121 967 and $139 257, respectively), and not cost-effective for other assessed cancers at a $150 000 willingness-to-pay/QALY threshold, compared with SoC chemotherapies. CONCLUSIONS: The cost-effectiveness of TADs can vary by cancers. Using analytic tools such as external controls and Bayesian hierarchical models can tackle several challenges in assessing the value of TADs and uncertainties from basket trials.

Trajectories of social isolation and loneliness and the risk of incident type 2 diabetes mellitus across genetic risk score.

AIM: This study aimed to investigate the association of social isolation, loneliness, and their trajectory with the risk of developing type 2 diabetes mellitus (T2DM) across genetic risk. METHODS: We included 439,337 participants (mean age 56.3 ± 8.1 years) enrolled in the UK Biobank study who were followed up until May 31, 2021. Social isolation and loneliness were self-reported and were further categorized into never, transient, incident, and persistent patterns. RESULTS: During a median follow-up of 12.7 years, 15,258 incident T2DM cases were documented. Social isolation (versus no social isolation: hazard ratio (HR) 95 % confidence interval (CI) 1.04 [1.00;1.09]) and loneliness (versus no loneliness: 1.26 [1.19;1.34]) were associated with an increased T2DM risk, independent of the genetic risk for T2DM. The interactions existed between social isolation and loneliness (Pinteraction < 0.05); the increased T2DM risk associated with social isolation was only significant among participants without loneliness. In the longitudinal analysis, only persistent social isolation (versus never social isolation: 1.22 [1.02;1.45]) was associated with an increased T2DM risk, whereas incident loneliness (versus never loneliness: 1.95 [1.40;2.71]) and persistent loneliness (2.00 [1.31;3.04]) were associated with higher T2DM risks. CONCLUSION: Social isolation and loneliness, especially their persistent pattern, were independently associated with an increased incident T2DM risk, irrespective of an individual's genetic risk. Loneliness modified the association between social isolation and incident T2DM.

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 µg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.

Comprehensive profiling of amino acids and derivatives in biological samples: A robust UHPLC-MS/MS method for investigating acute lung injury.

The severe respiratory dysfunctions associated with acute lung injury (ALI) and its sequelae have a high morbidity and mortality rate, are multifactorial, and lack a viable treatment. Considering the critical function that amino acids and derivatives play in the genesis of illnesses and the regulation of metabolic processes, monitoring the levels of metabolites associated with amino acids in biological matrices is necessary and interesting to study their pathological mechanisms. Exploring the dynamics of amino acids and derivatives level and searching for biomarkers provides improved clinical ideas for the diagnosis and treatment of ALI. Therefore, we developed an ultra-high-performance liquid chromatography-electrospray tandem mass spectrometry (UHPLC-MS/MS) method that can simultaneously determine the amino acid and derivatives metabolic levels to study amino acid profiles in different biological samples to facilitate clinical research of ALI. In this study, 48 amino acids and derivatives, including neurotransmitters, polyamines, purines, and other types, were quantified simultaneously in a fast, high-throughput, sensitive, and reliable manner within a 15-minute run time without derivatization. No relevant studies have been reported to quantify these 48 amino acid metabolites in three biological samples simultaneously. Satisfactory linearity (R > 0.995), inter-day and intra-day accuracy (85.17-112.67 % and 85.29-111.60 %, respectively), inter-day and intra-day precision (RSD < 13.80 % and RSD < 12.01 %, respectively), matrix effects (81.00 %-118.00 %), recovery (85.09 %-114.65 %) and stability (RSD < 14.72 %) were all demonstrated by the optimized method's successful validation for all analytes. In addition, the suggested method was effectively implemented in plasma, urine, and lung tissue from normal mice and mice with ALI, with the aim of finding potential biomarkers associated with ALI. Potential biomarkers were screened through multivariate statistical analysis and volcanic map analysis, and the changes of markers in ALI were again identified through heat map analysis and correlation analysis with biochemical indicators, which provided ideas and references for subsequent mechanism studies. Here, the technique created in this work offers a quick and dependable way to perform an integrated analysis of amino acids in a variety of biological materials, which can provide research ideas for understanding the physiopathological state of various diseases.

Exploring treatment burden in people with type 2 diabetes mellitus: a thematic analysis in china's primary care settings.

BACKGROUND: Understanding treatment burden is a critical element to the effective management of Type 2 Diabetes Mellitus (T2DM). The current study aims to address the knowledge gap surrounding treatment burden of T2DM from the patient's perspective in China's primary care settings. METHODS: A narrative review informed the creation of an a priori coding structure to identify aspects of T2DM treatment burden. Focus groups were conducted, employing a maximum variation sampling strategy to select participants from diverse sociodemographic backgrounds across urban, suburban, rural, and remote areas in China. Participants included adults with T2DM care in primary care settings for over a year and a Treatment Burden Questionnaire score of 25 or higher. Deductive thematic analysis, guided by the coding structure, facilitated a comprehensive exploration and further development of the conceptual framework of T2DM treatment burden. RESULTS: Four focus groups, each comprising five participants from diverse areas, were conducted. Utilising the Cumulative Complexity Model and Normalisation Process Theory as theoretical underpinnings, the thematic analysis refined the conceptual framework based on the coding structure from the narrative review. Five key themes were refined, encompassing medical information, medication, administration, healthcare system, and lifestyle. Additionally, the financial and time/travel themes merged into a new theme termed "personal resources", illustrating their overlapping within the framework. Participants in these focus groups highlighted challenges in managing medical information, an aspect often underrepresented in prior treatment burden research. The thematic analysis culminated in a finalised conceptual framework, offering a comprehensive understanding of the treatment burden experiences of people with T2DM in China's primary care settings. This framework includes six key constructs, delineating T2DM treatment burden and associated factors, such as antecedents and consequences. CONCLUSIONS: This study provides insights into the treatment burden of T2DM. A conceptual framework was finalised to deepen the understanding of the multifaceted constructs and the nature of treatment burden in people with T2DM. Furthermore, it emphasises the need to tailor T2DM treatment to individual capacities, considering their personal resource allocation and treatment utilisation.

Health burden from food systems is highly unequal across income groups.

Food consumption contributes to the degradation of air quality in regions where food is produced, creating a contrast between the health burden caused by a specific population through its food consumption and that faced by this same population as a consequence of food production activities. Here we explore this inequality within China's food system by linking air-pollution-related health burden from production to consumption, at high levels of spatial and sectorial granularity. We find that low-income groups bear a 70% higher air-pollution-related health burden from food production than from food consumption, while high-income groups benefit from a 29% lower health burden relative to their food consumption. This discrepancy largely stems from a concentration of low-income residents in food production areas, exposed to higher emissions from agriculture. Comprehensive interventions targeting both production and consumption sides can effectively reduce health damages and concurrently mitigate associated inequalities, while singular interventions exhibit limited efficacy.

Uncovering the secrets of agarwood aroma according to regions and grades using a comprehensive analytical strategy.

Agarwood holds significant importance as a valuable resource for aromatic purposes, however, key components responsible for aroma and the differences between regions and grades remain to be deeply elucidated. Thus, the odors of agarwood sourced from typical zones, as well as the renowned Kynam agarwood, were analyzed by HS-SPME Arrow GC-MS in SCAN and MRM modes. The integrated strategy proposed herein exploits the respective advantages of non-targeted and targeted analysis. In addition to a total of 55 volatile components identified from the NIST database, 114 odor components were matched according to the Smart Aroma Database, and a series of differential compounds was also unearthed and quantified.

Identification and validation of an immunotherapeutic signature for colon cancer based on the regulatory patterns of ferroptosis and their association with the tumor microenvironment.

The integrated landscape of ferroptosis regulatory patterns and their association with colon microenvironment have been demonstrated in recent studies. However, the ferroptosis-related immunotherapeutic signature for colon cancer (CC) remains unclear. We comprehensively evaluated 1623 CC samples, identified patterns of ferroptosis modification based on ferroptosis-associated genes, and systematically correlated these patterns with tumor microenvironment (TME) cell infiltration characteristics. In addition, the ferroptosis-regulated gene score (FRG-score) was constructed to quantify the pattern of ferroptosis alterations in individual tumors. Three distinct patterns of ferroptosis modification were identified, including antioxidant defense, iron toxicity, and lipid peroxidation. The characteristics of TME cell infiltration under these three patterns were highly consistent with the three immune phenotypes of tumors, including immune-inflamed, immune-excluded and immune-desert phenotypes. We also demonstrated that evaluation of ferroptosis regulatory patterns within individual tumors can predict tumor inflammatory status, tumor subtype, TME stromal activity, genetic variation, and clinical outcome. Immunotherapy cohorts confirmed that patients with low FRG-scores showed remarkable therapeutic and clinical benefits. Furthermore, the hub gene apolipoprotein L6 (APOL6), a drug-sensitive target associated with cancer cell ferroptosis, was identified through our proposed novel key gene screening process and validated in CC cell lines and scRNA-seq.

The golden key to open mystery boxes of SMARCA4-deficient undifferentiated thoracic tumor: focusing immunotherapy, tumor microenvironment and epigenetic regulation.

SMARCA4-deficient undifferentiated thoracic tumor is extremely invasive. This tumor with poor prognosis is easily confused with SMARCA4-deficent non-small cell lung cancer or sarcoma. Standard and efficient treatment has not been established. In this review, we summarized the etiology, pathogenesis and diagnosis, reviewed current and proposed innovative strategies for treatment and improving prognosis. Immunotherapy, targeting tumor microenvironment and epigenetic regulator have improved the prognosis of cancer patients. We summarized clinicopathological features and immunotherapy strategies and analyzed the progression-free survival (PFS) and overall survival (OS) of patients with SMARCA4-UT who received immune checkpoint inhibitors (ICIs). In addition, we proposed the feasibility of epigenetic regulation in the treatment of SMARCA4-UT. To our knowledge, this is the first review that aims to explore innovative strategies for targeting tumor microenvironment and epigenetic regulation and identify potential benefit population for immunotherapy to improve the prognosis.

Mass spectrometric analysis strategies for pyrrolizidine alkaloids.

Pyrrolizidine alkaloids (PAs) in food and natural preparations have received widespread attention due to their hepatotoxicity, genotoxicity, and embryotoxicity. Mass spectrometry (MS), as a high resolution, high sensitive, and high throughput detection tool, has been the most commonly used technique for the determination of PAs. The continuous advancement of new technologies, methods, and strategies in the field of MS has contributed to the improvement of the analytical efficiency and methodological enhancement of PAs. This paper provides an overview of the structure, toxicity properties and commonly employed analytical methods, focusing on the concepts, advances, and novel techniques and applications of MS-based methods for the analysis of PAs. Additionally, the remaining challenges, future perspectives, and trends for PA detection are discussed. This review provides a reference for toxicological studies of PAs, content monitoring, and the establishment of quality control and safety standards for herbal and food products.