Knowledge, Attitude and Practice of Radiologists Regarding Artificial Intelligence in Medical Imaging.

Purpose: This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. Methods: This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. Results: The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (ß=0.481, P<0.001) and practice (ß=0.412, P<0.001), and attitude had a direct effect on practice (ß=0.135, P<0.001). Conclusion: Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.

Tropism-shifted AAV-PHP.eB-mediated bFGF gene therapy promotes varied neurorestoration after ischemic stroke in mice.

ABSTRACT: AAV-PHP.eB is an artificial adeno-associated virus (AAV) that crosses the blood-brain barrier and targets neurons more efficiently than other AAVs when administered systematically. While AAV-PHP.eB has been used in various disease models, its cellular tropism in cerebrovascular diseases remains unclear. In the present study, we aimed to elucidate the tropism of AAV-PHP.eB for different cell types in the brain in a mouse model of ischemic stroke and evaluate its effectiveness in mediating basic fibroblast growth factor (bFGF) gene therapy. Mice were injected intravenously with AAV-PHP.eB either 14 days prior to (pre-stroke) or 1 day following (post-stroke) transient middle cerebral artery occlusion. Notably, we observed a shift in tropism from neurons to endothelial cells with post-stroke administration of AAV-PHP.eB-mNeonGreen (mNG). This endothelial cell tropism correlated strongly with expression of the endothelial membrane receptor lymphocyte antigen 6 family member A (Ly6A). Furthermore, AAV-PHP.eB-mediated overexpression of bFGF markedly improved neurobehavioral outcomes and promoted long-term neurogenesis and angiogenesis post-ischemic stroke. Our findings underscore the significance of considering potential tropism shifts when utilizing AAV-PHP.eB-mediated gene therapy in neurological diseases and suggest a promising new strategy for bFGF gene therapy in stroke treatment.

A dual-mode, image-enhanced, miniaturized microscopy system for incubator-compatible monitoring of live cells.

Imaging live cells under stable culture conditions is essential to investigate cell physiological activities and proliferation. To achieve this goal, typically, a specialized incubation chamber that creates desired culture conditions needs to be incorporated into a microscopy system to perform cell monitoring. However, such imaging systems are generally large and costly, hampering their wide applications. Recent advances in the field of miniaturized microscopy systems have enabled incubator cell monitoring, providing a hospitable environment for live cells. Although these systems are more cost-effective, they are usually limited in imaging modalities and spatial temporal resolution. Here, we present a dual-mode, image-enhanced, miniaturized microscopy system (termed MiniCube) for direct monitoring of live cells inside incubators. MiniCube enables both bright field imaging and fluorescence imaging with single-cell spatial resolution and sub-second temporal resolution. Moreover, this system can also perform cell monitoring inside the incubator with tunable time scales ranging from a few seconds to days. Meanwhile, automatic cell segmentation and image enhancement are realized by the proposed data analysis pipeline of this system, and the signal-to-noise ratio (SNR) of acquired data is significantly improved using a deep learning based image denoising algorithm. Image data can be acquired with 5 times lower light exposure while maintaining comparable SNR. The versatility of this miniaturized microscopy system lends itself to various applications in biology studies, providing a practical platform and method for studying live cell dynamics within the incubator.

Betaine and I-LG may have a predictive value for ATB: A causal study in a large European population.

PURPOSE: To analyze the causal relationship between 486 human serum metabolites and the active tuberculosis (ATB) in European population. METHODS: In this study, the causal relationship between human serum metabolites and the ATB was analyzed by integrating the genome-wide association study (GWAS). The 486 human serum metabolites were used as the exposure variable, three different ATB GWAS databases in the European population were set as outcome variables, and single nucleotide polymorphisms were used as instrumental variables for Mendelian Randomization. The inverse variance weighting was estimated causality, the MR-Egger intercept to estimate horizontal pleiotropy, and the combined effects of metabolites were also considered in the meta-analysis. Furthermore, the web-based MetaboAnalyst 6.0 was engaged for enrichment pathway analysis, while R (version 4.3.2) software and Review Manager 5.3 were employed for statistical analysis. RESULTS: A total of 21, 17, and 19 metabolites strongly associated with ATB were found in the three databases after preliminary screening (P < 0.05). The intersecting metabolites across these databases included tryptophan, betaine, 1-linoleoylglycerol (1-monolinolein) (1-LG), 1-eicosatrienoylglycerophosphocholine, and oleoylcarnitine. Among them, betaine (I2 = 24%, P = 0.27) and 1-LG (I2 = 0%, P = 0.62) showed the lowest heterogeneity among the different ATB databases. In addition, the metabolic pathways of phosphatidylethanolamine biosynthesis (P = 0.0068), methionine metabolism (P = 0.0089), betaine metabolism (P = 0.0205) and oxidation of branched-chain fatty acids (P = 0.0309) were also associated with ATB. CONCLUSION: Betaine and 1-LG may be biomarkers or auxiliary diagnostic tools for ATB. They may provide new guidance for medical practice in the early diagnosis and surveillance of ATB. In addition, by interfering with phosphatidylethanolamine biosynthesis, methionine metabolism, betaine metabolism, oxidation of branched-chain fatty acids, and other pathways, it is helpful to develop new anti-tuberculosis drugs and explore the virulence or pathogenesis of ATB at a deeper level, providing an effective reference for future studies.

Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images.

Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela-like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/MRI imaging hamper fast detection, thus increasing the severity of stroke. Here, we developed a convolutional neural network model by integrating four networks, Xception, ResNet50, VGG19, and EfficientNetb1, to recognize stroke based on 2D facial images with a cross-validation area under curve (AUC) of 0.91 within the training set of 185 acute ischemic stroke patients and 551 age- and sex-matched controls, and AUC of 0.82 in an independent data set regardless of age and sex. The model computed stroke probability was quantitatively associated with facial features, various clinical parameters of blood clotting indicators and leukocyte counts, and, more importantly, stroke incidence in the near future. Our real-time facial image artificial intelligence model can be used to rapidly screen and prediagnose stroke before CT scanning, thus meeting the urgent need in emergency clinics, potentially translatable to routine monitoring.

Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.

PURPOSE: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficult to obtain a training set with multiple stains because the labeling of pathology images is very time-consuming and tedious. Therefore, in this paper, we proposed an unsupervised stain augmentation-based method for segmentation of glomerular instances. METHODS: In this study, we successfully realized the conversion between different staining methods such as PAS, MT and PASM by contrastive unpaired translation (CUT), thus improving the staining diversity of the training set. Moreover, we replaced the backbone of mask R-CNN with swin transformer to further improve the efficiency of feature extraction and thus achieve better performance in instance segmentation task. RESULTS: To validate the method presented in this paper, we constructed a dataset from 216 WSIs of the three stains in this study. After conducting in-depth experiments, we verified that the instance segmentation method based on stain augmentation outperforms existing methods across all metrics for PAS, PASM, and MT stains. Furthermore, ablation experiments are performed in this paper to further demonstrate the effectiveness of the proposed module. CONCLUSION: This study successfully demonstrated the potential of unsupervised stain augmentation to improve glomerular segmentation in pathology analysis. Future research could extend this approach to other complex segmentation tasks in the pathology image domain to further explore the potential of applying stain augmentation techniques in different domains of pathology image analysis.

Optical Genome Mapping: A Machine-Based Platform in Cytogenomics.

Optical genome mapping (OGM) has generated excitement following decades of research and development. Now, commercially available technical platforms have been used to compare various other cytogenetic and cytogenomic technologies, including karyotype, microarrays, and DNA sequencing, with impressive results. In this chapter, using OGM as a case study, we advocate for a new trend in future cytogenomics, emphasizing the power of machine automation to deliver higher-quality cytogenomic data. By briefly discussing OGM, along with its major advantages and limitations, we underscore the importance of karyotype-based genomic research, from both a theoretical framework and a new technology perspective. We also call for the encouragement of further technological platform development for the future of cytogenetics and cytogenomics.

The New Era of Cancer Cytogenetics and Cytogenomics.

The promises of the cancer genome sequencing project, combined with various -omics technologies, have raised questions about the importance of cancer cytogenetic analyses. It is suggested that DNA sequencing provides high resolution, speed, and automation, potentially replacing cytogenetic testing. We disagree with this reductionist prediction. On the contrary, various sequencing projects have unexpectedly challenged gene theory and highlighted the importance of the genome or karyotype in organizing gene network interactions. Consequently, profiling the karyotype can be more meaningful than solely profiling gene mutations, especially in cancer where karyotype alterations mediate cellular macroevolution dominance. In this chapter, recent studies that illustrate the ultimate importance of karyotype in cancer genomics and evolution are briefly reviewed. In particular, the long-ignored non-clonal chromosome aberrations or NCCAs are linked to genome or chromosome instability, genome chaos is linked to genome reorganization under cellular crisis, and the two-phased cancer evolution reconciles the relationship between genome alteration-mediated punctuated macroevolution and gene mutation-mediated stepwise microevolution. By further synthesizing, the concept of karyotype coding is discussed in the context of information management. Altogether, we call for a new era of cancer cytogenetics and cytogenomics, where an array of technical frontiers can be explored further, which is crucial for both basic research and clinical implications in the cancer field.

Tracking Karyotype Changes in Treatment-Induced Drug-Resistant Evolution.

Karyotype coding, which encompasses the complete chromosome sets and their topological genomic relationships within a given species, encodes system-level information that organizes and preserves genes' function, and determines the macroevolution of cancer. This new recognition emphasizes the crucial role of karyotype characterization in cancer research. To advance this cancer cytogenetic/cytogenomic concept and its platforms, this study outlines protocols for monitoring the karyotype landscape during treatment-induced rapid drug resistance in cancer. It emphasizes four key perspectives: combinational analyses of phenotype and karyotype, a focus on the entire evolutionary process through longitudinal analysis, a comparison of whole landscape dynamics by including various types of NCCAs (including genome chaos), and the use of the same process to prioritize different genomic scales. This protocol holds promise for studying numerous evolutionary aspects of cancers, and it further enhances the power of karyotype analysis in cancer research.

Expanding the horizons of targeted protein degradation: A non-small molecule perspective.

Targeted protein degradation (TPD) represented by proteolysis targeting chimeras (PROTACs) marks a significant stride in drug discovery. A plethora of innovative technologies inspired by PROTAC have not only revolutionized the landscape of TPD but have the potential to unlock functionalities beyond degradation. Non-small-molecule-based approaches play an irreplaceable role in this field. A wide variety of agents spanning a broad chemical spectrum, including peptides, nucleic acids, antibodies, and even vaccines, which not only prove instrumental in overcoming the constraints of conventional small molecule entities but also provided rapidly renewing paradigms. Herein we summarize the burgeoning non-small molecule technological platforms inspired by PROTACs, including three major trajectories, to provide insights for the design strategies based on novel paradigms.

Incidence and prognosis of olfactory and gustatory dysfunctions related to SARS-CoV-2 Omicron strain infection in China: A national multicenter survey of 35,566 individuals.

Objective: This cross-sectional study aimed to determine the epidemiology of olfactory and gustatory dysfunctions related to COVID-19 in China. Methods: This study was conducted by 45 tertiary Grade-A hospitals in China. Online and offline questionnaire data were obtained from patients infected with COVID-19 between December 28, 2022, and February 21, 2023. The collected information included basic demographics, medical history, smoking and drinking history, vaccination history, changes in olfactory and gustatory functions before and after infection, and other postinfection symptoms, as well as the duration and improvement status of olfactory and gustatory disorders. Results: Complete questionnaires were obtained from 35,566 subjects. The overall incidence of olfactory and taste dysfunction was 67.75%. Being female or being a cigarette smoker increased the likelihood of developing olfactory and taste dysfunction. Having received four doses of the vaccine or having good oral health or being a alcohol drinker decreased the risk of such dysfunction. Before infection, the average olfactory and taste VAS scores were 8.41 and 8.51, respectively; after infection, they decreased to 3.69 and 4.29 and recovered to 5.83 and 6.55 by the time of the survey. The median duration of dysosmia and dysgeusia was 15 and 12 days, respectively, with 0.5% of patients having symptoms lasting for more than 28 days. The overall self-reported improvement rate was 59.16%. Recovery was higher in males, never smokers, those who received two or three vaccine doses, and those that had never experienced dental health issues, or chronic accompanying symptoms. Conclusions: The incidence of dysosmia and dysgeusia following infection with the SARS-CoV-2 virus is high in China. Incidence and prognosis are influenced by several factors, including sex, SARS-CoV-2 vaccination, history of head-facial trauma, nasal and oral health status, smoking and drinking history, and the persistence of accompanying symptoms.

Global research status and trends of enteric glia: a bibliometric analysis.

Background: Enteric glia are essential components of the enteric nervous system. Previously believed to have a passive structural function, mounting evidence now suggests that these cells are indispensable for maintaining gastrointestinal homeostasis and exert pivotal influences on both wellbeing and pathological conditions. This study aimed to investigate the global status, research hotspots, and future directions of enteric glia. Methods: The literature on enteric glia research was acquired from the Web of Science Core Collection. VOSviewer software (v1.6.19) was employed to visually represent co-operation networks among countries, institutions, and authors. The co-occurrence analysis of keywords and co-citation analysis of references were conducted using CiteSpace (v6.1.R6). Simultaneously, cluster analysis and burst detection of keywords and references were performed. Results: A total of 514 publications from 36 countries were reviewed. The United States was identified as the most influential country. The top-ranked institutions were University of Nantes and Michigan State University. Michel Neunlist was the most cited author. "Purinergic signaling" was the largest co-cited reference cluster, while "enteric glial cells (EGCs)" was the cluster with the highest number of co-occurring keywords. As the keyword with the highest burst strength, Crohns disease was a hot topic in the early research on enteric glia. The burst detection of keywords revealed that inflammation, intestinal motility, and gut microbiota may be the research frontiers. Conclusion: This study provides a comprehensive bibliometric analysis of enteric glia research. EGCs have emerged as a crucial link between neurons and immune cells, attracting significant research attention in neurogastroenterology. Their fundamental and translational studies on inflammation, intestinal motility, and gut microbiota may promote the treatment of some gastrointestinal and parenteral disorders.

[Expert consensus on the diagnosis and treatment of respiratory diseases exacerbated by nonsteroidal anti-inflammatory drugs （2024, Chengdu）].

Non-steroidal anti-inflammatory drugs-exacerbated respiratory disease （N-ERD） is a chronic respiratory disease characterized by eosinophilic inflammation, featuring chronic rhinosinusitis （CRS）, asthma, and intolerance to cyclooxygenase 1 （COX-1） inhibitors. The use of these medications can lead to an acute worsening of rhinitis and asthma symptoms. This condition has not yet received sufficient attention in China, with a high rate of misdiagnosis and a lack of related research. The Chinese Rhinology Research Group convened a group of leading young experts in otolaryngology from across the country, based on the latest domestic and international evidence-based medical practices to formulate this consensus.The consensus covers the epidemiology, pathogenesis, clinical manifestations, diagnostic methods, and treatment strategies for N-ERD, including pharmacotherapy, surgery, biologic treatments, and desensitization therapy. The goal is to improve recognition of N-ERD, reduce misdiagnosis, and enhance treatment outcomes.

Using Apnea-Hypopnea Duration per Hour to Predict Hypoxemia Among Patients with Obstructive Sleep Apnea.

Purpose: To explore the role of the mean apnea-hypopnea duration (MAD) and apnea-hypopnea duration per hour (HAD) in hypoxemia and evaluate whether they can effectively predict the occurrence of hypoxemia among adults with OSA. Patients and Methods: A total of 144 participants underwent basic information gathering and polysomnography (PSG). Logistic regression models were conducted to evaluate the best index in terms of hypoxemia. To construct the prediction model for hypoxemia, we randomly divided the participants into the training set (70%) and the validation set (30%). Results: The participants with hypoxemia tend to have higher levels of obesity, diabetes, AHI, MAD, and HAD compared with non-hypoxemia. The most relevant indicator of blood oxygen concentration is HAD (r = 0.73) among HAD, MAD, and apnea-hypopnea index (AHI). The fitness of HAD on hypoxemia showed the best. In the stage of establishing the prediction model, the area under the curve (AUC) values of both the training set and the validation set are 0.95. The increased HAD would elevate the risk of hypoxemia [odds ratio (OR): 1.30, 95% confidence interval (CI): 1.13-1.49]. Conclusion: The potential role of HAD in predicting hypoxemia underscores the significance of leveraging comprehensive measures of respiratory disturbances during sleep to enhance the clinical management and prognostication of individuals with sleep-related breathing disorders.

In silico bioactivity prediction of proteins interacting with graphene-based nanomaterials guides rational design of biosensor.

Graphene-based nanomaterials have attracted significant attention for their potentials in biomedical and biotechnology applications in recent years, owing to the outstanding physical and chemical properties. However, the interaction mechanism and impact on biological activity of macro/micro biomolecules still require more concerns and further research in order to enhance their applicability in biosensors, etc. Herein, an integrated method has been developed to predict the protein bioactivity performance when interacting with nanomaterials for protein-based biosensor. Molecular dynamics simulation and molecular docking technique were consolidated to investigate several nanomaterials: C60 fullerene, single-walled carbon nanotube, pristine graphene and graphene oxide, and their effect when interacting with protein. The adsorption behavior, secondary structure changes and protein bioactivity changes were simulated, and the results of protein activity simulation were verified in combination with atomic force spectrum, circular dichroism spectrum fluorescence and electrochemical experiments. The best quantification alignment between bioactivity obtained by simulation and experiment measurements was further explored. The two proteins, RNase A and Exonuclease III, were regarded as analysis model for the proof of concept, and the prediction accuracy of protein bioactivity could reach up to 0.98. The study shows an easy-to-operate and systematic approach to predict the effects of graphene-based nanomaterials on protein bioactivity, which holds guiding significance for the design of protein-related biosensors. In addition, the proposed prediction model is not limited to carbon-based nanomaterials and can be extended to other types of nanomaterials. This facilitates the rapid, simple, and low-cost selection of efficient and biosafe nanomaterials candidates for protein-related applications in biosensing and biomedical systems.

Inflammatory microenvironment regulation and osteogenesis promotion by bone-targeting calcium and magnesium repletion nanoplatform for osteoporosis therapy.

Osteoporosis is the most common bone metabolic disease that affects the health of middle-aged and elderly people, which is hallmarked by imbalanced bone remodeling and a deteriorating immune microenvironment. Magnesium and calcium are pivotal matrix components that participate in the bone formation process, especially in the immune microenvironment regulation and bone remodeling stages. Nevertheless, how to potently deliver magnesium and calcium to bone tissue remains a challenge. Here, we have constructed a multifunctional nanoplatform composed of calcium-based upconversion nanoparticles and magnesium organic frameworks (CM-NH2-PAA-Ald, denoted as CMPA), which features bone-targeting and pH-responsive properties, effectively regulating the inflammatory microenvironment and promoting the coordination of osteogenic functions for treating osteoporosis. The nanoplatform can efficaciously target bone tissue and gradually degrade in response to the acidic microenvironment of osteoporosis to release magnesium and calcium ions. This study validates that CMPA possessing favorable biocompatibility can suppress inflammation and facilitate osteogenesis to treat osteoporosis. Importantly, high-throughput sequencing results demonstrate that the nanoplatform exerts a good inflammatory regulation effect through inhibition of the nuclear factor kappa-B signaling pathway, thereby normalizing the osteoporotic microenvironment. This collaborative therapeutic strategy that focuses on improving bone microenvironment and promoting osteogenesis provides new insight for the treatment of metabolic diseases such as osteoporosis.

Hairpin-Empowered Invasive Reaction Combined with Catalytic Hairpin Assembly Cascade Amplification for the Specific Detection of Single-Nucleotide Polymorphisms.

Single-nucleotide polymorphism (SNP) is widely used in the study of disease-related genes and in the genetic study of animal and plant strains. Therefore, SNP detection is crucial for biomedical diagnosis and treatment as well as for molecular design breeding of animals and plants. In this regard, this article describes a novel technique for detecting SNP using flap endonuclease 1 (FEN 1) as a specific recognition element and catalytic hairpin assembly (CHA) cascade reaction as a signal amplification strategy. The mutant target (MT) was hybridized with a biotin-modified upstream probe and hairpin-type downstream probe (DP) to form a specific three-base overlapping structure. Then, FEN 1 was employed for three-base overlapping structure-specific recognition, namely, the precise SNP site identification and the 5' flap of DP dissociation. After dissociation, the hybridized probes were magnetically separated by a streptavidin-biotin complex. Especially, the ability to establish such a hairpin-type DP provided a powerful tool that could be used to hide the cut sequence (CS) and avoid false-positive signals. The cleaved CS initiated the CHA reaction and allowed superior fluorescence signal generation. Owing to the high specificity of FEN 1 for single base recognition, only the MT could be distinguished from the wild-type target and mismatched DNA. Owing to the dual signal amplification, as low as 0.36 fM MT and 1% mutation abundance from the mixtures could be detected, respectively. Furthermore, it could accurately identify SNPs from human cancer cells, as well as soybean leaf genome extracts. This strategy paves the way for the development of more precise and sensitive tools for diagnosing early onset diseases as well as molecular design breeding tools.

Linvoseltamab for Treatment of Relapsed/Refractory Multiple Myeloma.

PURPOSE: We present a phase I/II first-in-human trial evaluating the safety and efficacy of 50 mg and 200 mg doses of linvoseltamab, a B-cell maturation antigen × CD3 bispecific antibody in relapsed/refractory multiple myeloma (RRMM). METHODS: Phase II eligible patients had RRMM that either progressed on/after ≥three lines of therapy including a proteasome inhibitor (PI), an immunomodulatory drug (IMiD), and an anti-CD38 antibody or was triple-class (PI/IMiD/anti-CD38) refractory. Phase II treatment was once a week through week 14 and then once every 2 weeks. Phase II 200 mg patients who achieved a ≥very good partial response by week 24 received linvoseltamab once every 4 weeks. The primary end point in phase II was overall response rate (ORR). RESULTS: Among the 117 patients treated with 200 mg, the median age was 70 years, 39% had high-risk cytogenetics, and 28% had penta-refractory disease. At a median follow-up of 14.3 months, the ORR was 71%, with 50% achieving ≥complete response (CR). In 104 patients treated with 50 mg at a median follow-up of 7.4 months, the ORR was 48%, with 21% achieving ≥CR. The median duration of response (DOR) for 200 mg patients (n = 83) was 29.4 months (95% CI, 19.2 to not evaluable). Among 200 mg patients, the most common adverse events included cytokine release syndrome (35.0% Gr1, 10.3% Gr2, 0.9% Gr3), neutropenia (0.9% Gr2, 18.8% Gr3, 23.1% Gr4), and anemia (3.4% Gr1, 4.3% Gr2, 30.8% Gr3). Immune effector cell-associated neurotoxicity syndrome occurred in 7.7% of patients (2.6% each Gr1, Gr2, Gr3). Infections were reported in 74.4% of patients (33.3% Gr3, 2.6% Gr4); infection frequency and severity declined over time. CONCLUSION: Linvoseltamab 200 mg induced deep and durable responses, with a median DOR of 29.4 months, in patients with RRMM with an acceptable safety profile.

N6-methyladenosine modified TGFB2 triggers lipid metabolism reprogramming to confer pancreatic ductal adenocarcinoma gemcitabine resistance.

Gemcitabine resistance is a major obstacle to the effectiveness of chemotherapy in pancreatic ductal adenocarcinoma (PDAC). Therefore, new strategies are needed to sensitize cancer cells to gemcitabine. Here, we constructed gemcitabine-resistant PDAC cells and analyzed them with RNA-sequence. Employing an integrated approach involving bioinformatic analyses from multiple databases, TGFB2 is identified as a crucial gene in gemcitabine-resistant PDAC and is significantly associated with poor gemcitabine therapeutic response. The patient-derived xenograft (PDX) model further substantiates the gradual upregulation of TGFB2 expression during gemcitabine-induced resistance. Silencing TGFB2 expression can enhance the chemosensitivity of gemcitabine against PDAC. Mechanistically, TGFB2, post-transcriptionally stabilized by METTL14-mediated m6A modification, can promote lipid accumulation and the enhanced triglyceride accumulation drives gemcitabine resistance by lipidomic profiling. TGFB2 upregulates the lipogenesis regulator sterol regulatory element binding factor 1 (SREBF1) and its downstream lipogenic enzymes via PI3K-AKT signaling. Moreover, SREBF1 is responsible for TGFB2-mediated lipogenesis to promote gemcitabine resistance in PDAC. Importantly, TGFB2 inhibitor imperatorin combined with gemcitabine shows synergistic effects in gemcitabine-resistant PDAC PDX model. This study sheds new light on an avenue to mitigate PDAC gemcitabine resistance by targeting TGFB2 and lipid metabolism and develops the potential of imperatorin as a promising chemosensitizer in clinical translation.