Application of artificial intelligence combined with near infrared spectroscopy in the continuous counter-current extraction process of Angelica dahurica formula granules.

The establishment of near infrared (NIR) spectroscopy model mostly relies on chemometrics, and spectral analysis combined with artificial intelligence (AI) provides a new way of thinking for pharmaceutical quality inspection, new algorithms such as back propagation artificial neural networks (BP-ANN) and swarm intelligence optimization algorithms such as sparrow search algorithm (SSA) provide core technical support. In order to explore the application of AI in the pharmaceutical field, in this study, Angelica dahurica formula granules with a relatively complex system were selected as the research object. Quantitative analysis models were established by using partial least squares regression (PLSR) with a micro-NIR spectrometer, and BP-ANN modeling results were compared. For the best PLSR models of six characteristic components in the continuous counter-current extract of Angelica dahurica, R2v of imperatorin was lower than 0.90, and the RPD values of imperatorin, phellopterin, and isoimperatorin were even lower than 1. When the prediction model established by SSA-BP-ANN was used for quantitative analysis, R2v of six components were all higher than 0.92, and the RPD values all higher than 1.5, which proved that the BP-ANN method was better than PLSR. This study confirmed that in the continuous counter-current extraction progress of Angelica dahurica formula granules, the use of micro-NIR spectrometer combined with AI could realize the rapid prediction of the contents of six characteristic components. The comparison results provided a scientific reference for the process analysis and on-line monitoring in the production process of traditional Chinese medicine by micro-NIR spectrometer combined with AI.

Unfolding the downloads of datasets: A multifaceted exploration of influencing factors.

Scientific data are essential to advancing scientific knowledge and are increasingly valued as scholarly output. Understanding what drives dataset downloads is crucial for their effective dissemination and reuse. Our study, analysing 55,473 datasets from 69 data repositories, identifies key factors driving dataset downloads, focusing on interpretability, reliability, and accessibility. We find that while lengthy descriptive texts can deter users due to complexity and time requirements, readability boosts a dataset's appeal. Reliability, evidenced by factors like institutional reputation and citation counts of related papers, also significantly increases a dataset's attractiveness and usage. Additionally, our research shows that open access to datasets increases their downloads and amplifies the importance of interpretability and reliability. This indicates that easy access enhances the overall attractiveness and usage of datasets in the scholarly community. By emphasizing interpretability, reliability, and accessibility, this study offers a comprehensive framework for future research and guides data management practices toward ensuring clarity, credibility, and open access to maximize the impact of scientific datasets.

Surface Coating of NCM523 Cathode Electrodes by the Difunctional Block Copolymer/Lithium Salt Composites.

Nickel-rich layered oxide cathodes, such as LiNi0.5Co0.2Mn0.3O2 (NCM523), are prevalent in high-power batteries owing to their high energy density. However, these cathodes suffer from undesirable side reactions occurring at the cathode/liquid electrolyte interface, leading to inferior interface stability and poor cycle life. To address these issues, herein, an amphiphilic diblock copolymer poly(dimethylsiloxane)-block-poly(acrylic acid) (PDMS-b-PAA) along with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) is utilized for modifying the electrode surface. This modification causes a thin and stable cathode-electrolyte interface (CEI) on the surface of NCM523 particles, as evidenced by XPS, TEM, and EIS analysis. The introduction of this modified interface successfully suppresses the capacity fading of NCM523. After 200 cycles at a rate of 1.0 C, the capacity of the modified NCM523 cathode is 108.7 mAh g-1, with a capacity retention of 82.8%, while the control samples without the polymer modification display a capacity retention of 72.7%. These results outline the distinct advantage of electrode surface modification with diblock copolymers/LiTFSI for the stabilization of Ni-rich layered oxide cathodes.

DCBLD2 deletion increases hyperglycemia and induces vascular remodeling by inhibiting insulin receptor recycling in endothelial cells.

Discoidin, CUB, LCCL domain-containing 2 (DCBLD2) is a type I transmembrane protein with a similar structure to neuropilin, which acts as a co-receptor for certain receptor tyrosine kinases (RTKs). The insulin receptor is an RTK and plays a critical role in endothelial cell function and glycolysis. However, how and whether DCBLD2 regulates insulin receptor activity in endothelial cells is poorly understood. Diabetes was induced through treatment of Dcbld2 global-genome knockout mice and endothelium-specific knockout mice with streptozotocin. Vascular ultrasound, vascular tension test, and hematoxylin and eosin staining were performed to assess endothelial function and aortic remodeling. Glycolytic rate assays, real-time PCR and western blotting were used to investigate the effects of DCBLD2 on glycolytic activity and insulin receptor (InsR)/phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathway in endothelial cells. Co-immunoprecipitation was used to assess the effects of DCBLD2 on insulin receptor endocytosis and recycling. Membrane and cytoplasmic proteins were isolated to determine whether DCBLD2 could affect the localization of the insulin receptor. We found that Dcbld2 deletion exacerbated endothelial dysfunction and vascular remodeling in diabetic mice. Both Dcbld2 knockdown and Dcbld2 deletion inhibited glycolysis and the InsR/PI3K/Akt signaling pathway in endothelial cells. Furthermore, Dcbld2 deletion inhibited insulin receptor recycling. Taken together, Dcbld2 deficiency exacerbated diabetic endothelial dysfunction and vascular remodeling by inhibiting the InsR/PI3K/Akt pathway in endothelial cells through the inhibition of Rab11-dependent insulin receptor recycling. Our data suggest that DCBLD2 is a potential therapeutic target for diabetes and cardiovascular diseases.

Fabrication and characteristics of multifunctional hydrogel dressings using dopamine modified hyaluronic acid and phenylboronic acid modified chitosan.

The healing of damaged skin is a complex and dynamic process, and the multi-functional hydrogel dressings could promote skin tissue healing. This study, therefore, explored the development of a composite multifunctional hydrogel (HDCP) by incorporating the dopamine modified hyaluronic acid (HA-DA) and phenylboronic acid modified chitosan (CS-PBA) crosslinked using boric acid ester bonds. The integration of HA-DA and CS-PBA could be confirmed using the Fourier transform infrared spectrometer and 1H nuclear magnetic resonance analyses. The fabricated HDCP hydrogels exhibited porous structure, elastic solid behavior, shear-thinning, and adhesion properties. Furthermore, the HDCP hydrogels exhibited antibacterial efficacy against Gram-negative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus). Subsequently, the cytocompatibility of the HDCP hydrogels was verified through CCK-8 assay and fluorescent image analysis following co-cultivation with NIH-3T3 cells. This research presents an innovative multifunctional hydrogel that holds promise as a wound dressing for various applications within the realm of wound healing.

Genipin crosslinked quaternary ammonium chitosan hydrogels for wound dressings.

Bacterial infection can lead to various complications, such as inflammations on surrounding tissues, which can prolong wound healing and thus represent a significant clinical and public healthcare problem. Herein, a report on the fabrication of a novel genipin/quaternized chitosan (CS) hydrogel for wound dressing is presented. The hydrogel was prepared by mixing quaternized CS and genipin under 35 °C bath. The hydrogels showed porous structure (250-500 µm) and mechanical properties (3000-6000 Pa). In addition, the hydrogels displayed self-healing ability and adhesion performance on different substrates. Genipin crosslinked quaternized CS hydrogels showed antibacterial activities againstE. coliandS. aureus. The CCK-8 and fluorescent images confirmed the cytocompatibility of hydrogels by seeding with NIH-3T3 cells. The present study showed that the prepared hydrogel has the potential to be used as wound dressing.

Effect of novel anti-tumor and anti-angiogenesis drug taurolactone on angiogenic factor AGGF1 and angiogenesis mimicry in patients with hepatocellular carcinoma.

OBJECTIVE: Our study was to investigate the impact of taurolactone, a novel anti-tumor and anti-angiogenic drug, on AGGF1, an angiogenic factor, and angiogenesis mimicry in patients diagnosed with hepatocellular carcinoma (HCC). METHODS: A total of 120 HCC patients were enrolled from the Department of Oncology and Hepatobiliary Surgery at our hospital between May 2021 and December 2022. HCC diagnoses were confirmed through imaging or tissue biopsy for all patients. The age of patients ranged from 37 to 72 years, with an average age of 64.29 ± 4.58 years. These participants were divided equally into two groups: the control group and the observation group, each consisting of 60 individuals. While the control group received standard drug treatment, the observation group was administered taurolactone treatment. Before being included in the study, all participants or their legal representatives provided signed informed consent. Patient demographic information was collected through a questionnaire survey. ELISA was used to measure the levels of VEGF and AGGF1 in patients following treatment. Western blot was applied to assess the protein expression of PDGF, Angiopoietin, and AGGF1. MRI imaging technology was utilized to assess the perfusion characteristics of tumor blood vessels in patients. Tumor vessel density was compared between patients using ultrasonography. We also conducted a comparison between the two groups in terms of progression-free survival and overall survival. RESULTS: General patient information between the two groups showed no significant differences (P > 0.05). Of note, the observation group exhibited greatly lower levels of VEGF and AGGF1 compared to the control group (P < 0.05). Moreover, the levels of PDGF, Angiopoietin, and AGGF1 protein expression were significantly reduced in the observation group compared to the control group (P < 0.05). In terms of tumor perfusion, the observation group displayed lower average and maximum perfusion volumes in tumor blood vessels compared to the control group (P < 0.05). Additionally, the observation group demonstrated delayed peak times and arrival times of tumor blood vessels in comparison to the control group (P < 0.05). Furthermore, the density of tumor blood vessels was notably lower in the observation group compared to the control group (P < 0.05). Patients in the observation group had longer progression-free survival and overall survival than the control group (P < 0.05). CONCLUSION: In HCC patients, our study highlighted the potential efficacy of taurolactone treatment as it effectively inhibited angiogenic factors and angiogenesis mimicry, ultimately leading to an improved prognosis for these patients.

Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning.

Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA's appropriate applications. Near-infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi-strategy improved Walrus Optimization Algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.

Appling fluorescence spectroscopy with absolute principal component coefficient to explore dynamic migration of DOM fractions from an urbanized river during torrential rainfall.

This research delves into the dynamic interplay between urbanization and the characteristics of Dissolved Organic Matter (DOM) in the Anyang River, particularly under the stress of torrential rain. The motivation stems from a critical need to decipher how urban landscapes influence water quality, focusing on the intricate transformations and movements of DOM. Employing advanced fluorescence spectroscopy techniques like Excitation-Emission Matrices (EEM) and Parallel Factor Analysis (PARAFAC), the study meticulously differentiates DOM compositions in urban and agricultural settings. It unveils a pronounced distinction, with urban streams showing elevated proteinaceous DOM from wastewater, contrasting with the humic substances prevalent in agricultural runoff. The analysis also captures how intense rainfall events catalyze significant shifts in DOM profiles, thereby emphasizing the need for tailored water quality management strategies in urbanized catchments. This comprehensive approach not only bridges gaps in understanding the urban impact on riverine ecosystems but also sets a foundation for future research and policy development in the face of escalating environmental changes.

CircTLK1: A novel regulator involved in VSMC phenotypic switching and the developmental process of atherosclerosis.

Phenotypic switching of vascular smooth muscle cells (VSMCs) is essential for atherosclerosis development. Circular RNA (circRNA) is a specific non-coding RNA that is produced as a closed-loop structure in mammals, and its specific expression pattern is closely related to its cell type and tissue. To clarify the roles of circTLK1 in VSMC phenotypic switching, we performed qRT-PCR, immunoblotting, and immunostaining. qRT-PCR revealed that circTLK1 was upregulated in both mouse models of atherosclerosis in vivo and PDGF (platelet-derived growth factor)-BB-induced VSMCs in vitro. Furthermore, the overexpression of circTLK1 promoted PDGF-BB-induced VSMC phenotypic switching. Conversely, experiments performed in vivo demonstrate that the knockdown of SMC-specific circTLK1 led to a reduction in the development of atherosclerosis. The relationship between circTLK1 and miR-513a-3p and Krüppel-like factor 4 (KLF4) was detected by RNA immunoprecipitation (RIP), luciferase reporter assay, RNA pull-down, and RNA fluorescence in situ hybridization (RNA FISH). Mechanistically, circTLK1 acted as a sponge for miR-513a-3p, leading to the upregulation of KLF4, a key transcription factor for phenotypic switching. Targeting the circTLK1/miR-513a-3p/KLF4 axis may provide a potential therapeutic strategy for atherosclerosis.

Advanced PEG-tyramine biomaterial ink for precision engineering of perfusable and flexible small-diameter vascular constructs via coaxial printing.

Vascularization is crucial for providing nutrients and oxygen to cells while removing waste. Despite advances in 3D-bioprinting, the fabrication of structures with void spaces and channels remains challenging. This study presents a novel approach to create robust yet flexible and permeable small (600-1300 µm) artificial vessels in a single processing step using 3D coaxial extrusion printing of a biomaterial ink, based on tyramine-modified polyethylene glycol (PEG-Tyr). We combined the gelatin biocompatibility/activity, robustness of PEG-Tyr and alginate with the shear-thinning properties of methylcellulose (MC) in a new biomaterial ink for the fabrication of bioinspired vessels. Chemical characterization using NMR and FTIR spectroscopy confirmed the successful modification of PEG with Tyr and rheological characterization indicated that the addition of PEG-Tyr decreased the viscosity of the ink. Enzyme-mediated crosslinking of PEG-Tyr allowed the formation of covalent crosslinks within the hydrogel chains, ensuring its stability. PEG-Tyr units improved the mechanical properties of the material, resulting in stretchable and elastic constructs without compromising cell viability and adhesion. The printed vessel structures displayed uniform wall thickness, shape retention, improved elasticity, permeability, and colonization by endothelial-derived - EA.hy926 cells. The chorioallantoic membrane (CAM) and in vivo assays demonstrated the hydrogel's ability to support neoangiogenesis. The hydrogel material with PEG-Tyr modification holds promise for vascular tissue engineering applications, providing a flexible, biocompatible, and functional platform for the fabrication of vascular structures.

Rizatriptan benzoate-loaded dissolving microneedle patch for management of acute migraine therapy.

In this study, dissolving microneedles (MNs) using polyvinyl alcohol (PVA) and poly (1-vinylpyrrolidone-co-vinyl acetate) (P(VP-co-VA)) as matrix materials were developed for transdermal delivery of rizatriptan benzoate (RB) for acute migraine treatment. In-vitro permeation studies were conducted to assess the feasibility of the as-fabricated dissolving MNs to release RB. Drug skin penetration were tested by Franz diffusion cells, showing an increase of the transdermal flux compared to passive diffusion due to the as-fabricated dissolving MNs having a sufficient mechanical strength to penetrate the skin and form microchannels. The pharmacological study in vivo showed that RB-loaded dissolving MNs significantly alleviated migraine-related response by up-regulating the level of 5-hydroxytryptamine (5-HT) and down-regulating the levels of calcitonin gene-related peptide (CGRP) and substance P (SP). In conclusion, the RB-loaded dissolving MNs have advantages of safety, convenience, and high efficacy over conventional administrations, laying a foundation for the transdermal drug delivery system treatment for acute migraine.

Health information needs and feedback of users in the online TCM community.

To improve the information service quality of the online Traditional Chinese Medicine (TCM) community, this study investigated users' information needs, feedback and the relationship between them. Using qualitative content analysis, the basic characteristics of users' needs were obtained. Logistic regression was used to explore the impact of different need characteristics of feedback. The main findings are as follows: 1) Disease consultation, health preservation, professional discussion, knowledge sharing and experience description are the major 5 types of information needs in the online TCM community; 2) Some users provided TCM-related information, such as the tongue image and the TCM four diagnosis; 3) A total of 78.8% of the posts received effective feedback, and the main types of feedback were answering, discussing, inquiring and emotional supporting; 4) Providing enough information can significantly and positively affect whether needs receive effective feedback, suggesting that users can present information about their condition in as many different formats as possible when articulating their needs.

In situ rapid evaluation method of quality of peach kernels based on near infrared spectroscopy.

This study aimed to perform a rapid in situ assessment of the quality of peach kernels using near infrared (NIR) spectroscopy, which included identifications of authenticity, species, and origins, and amygdalin quantitation. The in situ samples without any pretreatment were scanned by a portable MicroNIR spectrometer, while their powder samples were scanned by a benchtop Fourier transform NIR (FT-NIR) spectrometer. To improve the performance of the in situ determination model of the portable NIR spectrometer, the two spectrometers were first compared in identification and content models of peach kernels for both in situ and powder samples. Then, the in situ sample spectra were transferred by using the improved principal component analysis (IPCA) method to enhance the performance of the in situ model. After model transfer, the prediction performance of the in situ sample model was significantly improved, as shown by the correlation coefficient in the prediction set (Rp), root means square error of prediction (RMSEP), and residual prediction deviation (RPD) of the in situ model reached 0.9533, 0.0911, and 3.23, respectively, and correlation coefficient in the test set (Rt) and root means square error of test (RMSET) reached 0.9701 and 0.1619, respectively, suggesting that model transfer could be a viable solution to improve the model performance of portable spectrometers.

Implementation of near-infrared spectroscopy and convolutional neural networks for predicting particle size distribution in fluidized bed granulation.

Monitoring the particle size distribution (PSD) is crucial for controlling product quality during fluidized bed granulation. This paper proposed a rapid analytical method that quantifies the D10, D50, and D90 values using a Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) framework tailored for deep learning with near-infrared (NIR) spectroscopy. This innovative framework, which fuses CBAM with CNN, excels at extracting intricate features while prioritizing crucial ones, thereby facilitating the creation of a robust multi-output regression model. To expand the training dataset, we incorporated the C-Mixup algorithm, ensuring that the deep learning model was trained comprehensively. Additionally, the Bayesian optimization algorithm was introduced to optimize the hyperparameters, improving the prediction performance of the deep learning model. Compared with the commonly used Partial Least Squares (PLS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models, the CBAM-CNN model yielded higher prediction accuracy. Furthermore, the CBAM-CNN model avoided spectral preprocessing, preserved the spectral information to the maximum extent, and returned multiple predicted values at one time without degrading the prediction accuracy. Therefore, the CBAM-CNN model showed better prediction performance and modeling convenience for analyzing PSD values in fluidized bed granulation.

Evaluation of the Pollination Efficiency of Apple Trees by Osmia excavata Alfken (Hymenoptera: Megachilidae).

The mason bee Osmia excavata Alfken is an apple pollinating insect widely distributed in northern China, in order to effectively utilize the mason bee and improve the pollination rate of apples, there is a need to evaluate the pollination efficiency of the bees. This study evaluated the pollination efficiency of the mason bee on apple orchards in Jinan and Yantai, Shandong Province, China. The study compared natural pollination areas and pollination areas with different release densities of O. excavata in terms of the effects of bee density, timing of pollination, and distance effects on fruit set rate, fruit shape index, fruit shape skewness, fruit soluble solids content, and fruit firmness. The optimal release density of bees was 6000 cocoons per hectare, resulting in the highest fruit setting rate of apple lateral flowers. From 07:00 to 14:00 was the best time for bee pollination. The optimal distance of hives from apple trees for pollination by O. excavata was 60 m. Single fruit weight was significantly higher and fruit unsymmetrical rate, partial slope and hardness were all significantly lower at the release densities of 6000 or 12000 cocoons per hectare compared with 3000 cocoons per hectare or under natural pollination conditions. There was no significant difference in the content of soluble solids under different release densities. Thus, the radius of 60 m from the hive was the effective pollination range and 6000 cocoons per hectare of mason bees could ensure the fruit quality of apple.

Recent Advances of Chitosan-Based Hydrogels for Skin-Wound Dressings.

The management of wound healing represents a significant clinical challenge due to the complicated processes involved. Chitosan has remarkable properties that effectively prevent certain microorganisms from entering the body and positively influence both red blood cell aggregation and platelet adhesion and aggregation in the bloodstream, resulting in a favorable hemostatic outcome. In recent years, chitosan-based hydrogels have been widely used as wound dressings due to their biodegradability, biocompatibility, safety, non-toxicity, bioadhesiveness, and soft texture resembling the extracellular matrix. This article first summarizes an overview of the main chemical modifications of chitosan for wound dressings and then reviews the desired properties of chitosan-based hydrogel dressings. The applications of chitosan-based hydrogels in wound healing, including burn wounds, surgical wounds, infected wounds, and diabetic wounds are then discussed. Finally, future prospects for chitosan-based hydrogels as wound dressings are discussed. It is anticipated that this review will form a basis for the development of a range of chitosan-based hydrogel dressings for clinical treatment.

Multidimensional IRT for forced choice tests: A literature review.

The Multidimensional Forced Choice (MFC) test is frequently utilized in non-cognitive evaluations because of its effectiveness in reducing response bias commonly associated with the conventional Likert scale. Nonetheless, it is critical to recognize that the MFC test generates ipsative data, a type of measurement that has been criticized due to its limited applicability for comparing individuals. Multidimensional item response theory (MIRT) models have recently sparked renewed interest among academics and professionals. This is largely due to the development of several models that make it easier to collect normative data from forced-choice tests. The paper introduces a modeling framework made up of three key components: response format, measurement model, and decision theory. Under this paradigm, four IRT models were chosen as examples. Following that, a comprehensive study is carried out to compare and characterize the parameter estimation techniques used in MFC-IRT models. This work then examines empirical research on the concept by analyzing three distinct domains: parameter invariance testing, computerized adaptive testing (CAT), and validity investigation. Finally, it is recommended that future research initiatives follow four distinct paths: modeling, parameter invariance testing, forced-choice CAT, and validity studies.

High-solution emission characters and health risks of volatile organic compounds for sprayers in automobile repair industries.

The increasing automobile repair industries (ARIs) with spray facilities have become an important volatile organic compound (VOC) pollution source in China. However, the VOC health risk assessment for long-term exposure in ARIs has not been well characterized. In this study, though sampled VOCs from 51 typical ARIs in Beijing, the relationship between emission patterns, average daily exposure concentrations (EC), and health risks was comprehensively analyzed with the health assessment method. Results showed that concentrations of 117 VOCs from the samples ranged from 68.53 to 19863.32 µg·m-3, while the ARI operator's daily VOC inhalation EC was 11.24-1460.70 µg·m-3. The organic VOC (OVOC) concentration accounted for 73.16 ~ 94.52% in the solvent-based paint workshops, while aromatics were the main VOC component in water-based paint spraying (WPS) workshops, accounting for 70.08%, respectively. And the method of inhalation exposure health risk assessment was firstly used to evaluate carcinogenicity and non-carcinogenicity risk for sprayers in ARIs. The cumulative lifetime carcinogenic risk (LCR) for 24 sampled VOCs were within acceptable ranges, while the mean hazard index (HI) for 1 year with 44 sampled VOCs was over 1. Among them, ethyl alcohol had a high carcinogenic risk in both mixed water-based paint (MP) and solvent-based paint workshops. The mean HI associated with aromatics were 2.88E - 3 and 4.30E - 3 for 1 h in MP and WPS workshops. O-ethyl toluene and acetone are VOC components that need to be paid attention to in future paint raw materials and spraying operations. Our study will provide the important references for the standard of VOC occupational exposure health limits in ARIs.

The HSP90-MYC-CDK9 network drives therapeutic resistance in mantle cell lymphoma.

Brexucabtagene autoleucel CAR-T therapy is highly efficacious in overcoming resistance to Bruton's tyrosine kinase inhibitors (BTKi) in mantle cell lymphoma. However, many patients relapse post CAR-T therapy with dismal outcomes. To dissect the underlying mechanisms of sequential resistance to BTKi and CAR-T therapy, we performed single-cell RNA sequencing analysis for 66 samples from 25 patients treated with BTKi and/or CAR-T therapy and conducted in-depth bioinformatics™ analysis. Our analysis revealed that MYC activity progressively increased with sequential resistance. HSP90AB1 (Heat shock protein 90 alpha family class B member 1), a MYC target, was identified as early driver of CAR-T resistance. CDK9 (Cyclin-dependent kinase 9), another MYC target, was significantly upregulated in Dual-R samples. Both HSP90AB1 and CDK9 expression were correlated with MYC activity levels. Pharmaceutical co-targeting of HSP90 and CDK9 synergistically diminished MYC activity, leading to potent anti-MCL activity. Collectively, our study revealed that HSP90-MYC-CDK9 network is the primary driving force of therapeutic resistance.