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Abstract Heat shock proteins are essential molecular chaperones that play crucial roles in stabilizing protein structures, facilitating the repair or degradation of damaged proteins, and maintaining proteostasis and cellular functions. Extensive research has demonstrated that heat shock proteins are highly expressed in cancers and closely associated with tumorigenesis and progression. The "Hallmarks of Cancer" are the core features of cancer biology that collectively define a series of functional characteristics acquired by cells as they transition from a normal state to a state of tumor growth, including sustained proliferative signaling, evasion of growth suppressors, resistance to cell death, enabled replicative immortality, the induction of angiogenesis, and the activation of invasion and metastasis. The pivotal roles of heat shock proteins in modulating the hallmarks of cancer through the activation or inhibition of various signaling pathways has been well documented. Therefore, this review provides an overview of the roles of heat shock proteins in vital biological processes from the perspective of the hallmarks of cancer and summarizes the small-molecule inhibitors that target heat shock proteins to regulate various cancer hallmarks. Moreover, we further discuss combination therapy strategies involving heat shock proteins and promising dual-target inhibitors to highlight the potential of targeting heat shock proteins for cancer treatment. In summary, this review highlights how targeting heat shock proteins could regulate the hallmarks of cancer, which will provide valuable information to better elucidate and understand the roles of heat shock proteins in oncology and the mechanisms of cancer occurrence and development and aid in the development of more efficacious and less toxic novel anticancer agents. Graphic abstract

Heat shock proteins are essential molecular chaperones that play crucial roles in stabilizing protein structures, facilitating the repair or degradation of damaged proteins, and maintaining proteostasis and cellular functions. Extensive research has demonstrated that heat shock proteins are highly expressed in cancers and closely associated with tumorigenesis and progression. The "Hallmarks of Cancer" are the core features of cancer biology that collectively define a series of functional characteristics acquired by cells as they transition from a normal state to a state of tumor growth, including sustained proliferative signaling, evasion of growth suppressors, resistance to cell death, enabled replicative immortality, the induction of angiogenesis, and the activation of invasion and metastasis. The pivotal roles of heat shock proteins in modulating the hallmarks of cancer through the activation or inhibition of various signaling pathways has been well documented. Therefore, this review provides an overview of the roles of heat shock proteins in vital biological processes from the perspective of the hallmarks of cancer and summarizes the small-molecule inhibitors that target heat shock proteins to regulate various cancer hallmarks. Moreover, we further discuss combination therapy strategies involving heat shock proteins and promising dual-target inhibitors to highlight the potential of targeting heat shock proteins for cancer treatment. In summary, this review highlights how targeting heat shock proteins could regulate the hallmarks of cancer, which will provide valuable information to better elucidate and understand the roles of heat shock proteins in oncology and the mechanisms of cancer occurrence and development and aid in the development of more efficacious and less toxic novel anticancer agents. [ABSTRACT FROM AUTHOR]

Perinatal depression (PD), which affects about 10–20 percent of women, often goes unnoticed because related symptoms frequently overlap with those commonly experienced during pregnancy. Moreover, identifying PD currently depends heavily on the use of questionnaires, and objective biological indicators for diagnosis has yet to be identified. This research proposes a safe and non-invasive method for diagnosing PD and aims to delve deeper into its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram (EEG) for mothers-to-be and fetuses, we collected the resting-state scalp EEG of pregnant women (with PD/healthy) at the 38th week of gestation. To compensate for the low spatial resolution of scalp EEG, source analysis was first applied to project the scalp EEG to the cortical-space. Afterwards, cortical-space networks and large-scale networks were constructed to investigate the mechanism of PD from two different level. Herein, differences in the two distinct types of networks between PD patients and healthy mothers-to-be were explored, respectively. We found that the PD patients illustrated decreased network connectivity in the cortical-space, while the large-scale networks revealed weaker connections at cerebellar area. Further, related spatial topological features derived from the two different networks were combined to promote the recognition of pregnant women with PD from those healthy ones. Meanwhile, the depression severity at patient level was effectively predicted based on the combined spatial topological features as well. These findings consistently validated that the two kinds of networks indeed played off each other, which thus helped explore the underlying mechanism of PD; and further verified the superiority of the combination strategy, revealing its reliability and potential in diagnosis and depression severity evaluation.

Gas therapy is currently popular in cancer treatment because of its controllability, effectiveness, and biosafety, as well as the fast development of carriers for gas delivery. Here, we introduce the various gas molecules with exact anticancer effects, including nitric oxide (NO), carbon monoxide (CO), oxygen (O2), hydrogen (H2), etc., and their anticancer mechanisms. Besides, recent advances in stimuli-responsive delivery systems for gas therapy are summarized. In terms of the various stimulation methods, the design of delivery systems for gases is introduced. In addition, the strategies of gas therapy combined with other therapeutic modalities, including chemotherapy, radiotherapy (RT), photodynamic therapy (PDT), photothermal therapy (PTT), etc., are described. Finally, the current challenges and prospects of delivery platforms for gases, and the future development and clinical translational value of gas therapy are also discussed.

Su Hyun Lim,1,2 Gi Taek Yee,3 Dongwoo Khang1,2,4 1Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, South Korea; 2Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon, 21999, South Korea; 3Department of Neurosurgery, Gil Medical Center, Gachon University, School of Medicine, Incheon, 21565, South Korea; 4Department of Physiology, School of Medicine, Gachon University, Incheon, 21999, South KoreaCorrespondence: Dongwoo Khang, Department of Physiology, School of Medicine, Gachon University, Incheon, 21999, South Korea, Tel +82 32 899 1525, Email dkhang@gachon.ac.kr Gi Taek Yee, Department of Neurosurgery, College of Medicine, Gil Medical Center, Gachon University, Incheon, 21565, South Korea, Tel +82-32-460-3304, Email gtyee@gilhospital.comAbstract: The blood-brain barrier (BBB) and blood-tumor barrier (BTB) pose substantial challenges to efficacious drug delivery for glioblastoma multiforme (GBM), a primary brain tumor with poor prognosis. Nanoparticle-based combinational strategies have emerged as promising modalities to overcome these barriers and enhance drug penetration into the brain parenchyma. This review discusses various nanoparticle-based combinatorial approaches that combine nanoparticles with cell-based drug delivery, viral drug delivery, focused ultrasound, magnetic field, and intranasal drug delivery to enhance drug permeability across the BBB and BTB. Cell-based drug delivery involves using engineered cells as carriers for nanoparticles, taking advantage of their intrinsic migratory and homing capabilities to facilitate the transport of therapeutic payloads across BBB and BTB. Viral drug delivery uses engineered viral vectors to deliver therapeutic genes or payloads to specific cells within the GBM microenvironment. Focused ultrasound, coupled with microbubbles or nanoparticles, can temporarily disrupt the BBB to increase drug permeability. Magnetic field-guided drug delivery exploits magnetic nanoparticles to facilitate targeted drug delivery under an external magnetic field. Intranasal drug delivery offers a minimally invasive avenue to bypass the BBB and deliver therapeutic agents directly to the brain via olfactory and trigeminal pathways. By combining these strategies, synergistic effects can enhance drug delivery efficiency, improve therapeutic efficacy, and reduce off-target effects. Future research should focus on optimizing nanoparticle design, exploring new combination strategies, and advancing preclinical and clinical investigations to promote the translation of nanoparticle-based combination therapies for GBM. Keywords: glioblastoma, blood-brain barrier, blood-tumor barrier, nanoparticle, combination strategy, ultrasound-wave, magnetic field, intranasal drug delivery

White blood cell detection plays an integral role in diagnosing pathologies such as leukemia and gestational diabetes. Despite this, conventional image-based white blood cell classification methodologies encounter obstacles including inaccurate cell segmentation and labor-intensive artificial feature extraction. Contrarily, Convolutional Neural Networks (CNNs) have the capacity to learn features autonomously from raw images, thereby offering a novel and effective solution for blood cell detection. Notwithstanding, the features ascertained by a solitary CNN tend to be unidirectional. Conversely, ensemble learning combines results from numerous networks, thus ensuring an adequate acquisition of feature information and subsequently enhancing the model's overall efficacy. Consequently, this study introduces a method for white blood cell classification underpinned by ensemble CNNs. Initially, three high-performing CNNs possessing disparate structures, namely VGG16, ResNet50, and Inception V3, are enlisted as base learners to augment the diversity of base learners. Subsequently, the Gompertz function is employed to strategize the ensemble learning combination strategy, taking into consideration the prediction confidence and fuzzy level of each base learner. Ultimately, the ensemble CNN model is developed, incorporating learning outcomes from several singular models and utilizing diversified information to achieve white blood cell classification. Empirical results indicate that the ensemble learning technique advanced in this study enables accurate and reliable white blood cell classification, demonstrating potential clinical value. [ABSTRACT FROM AUTHOR]

In this study, lambda-cyhalothrin (LC) loaded polyurea microcapsules (MCs) with different particle sizes were fabricated. All of the MCs showed varying degrees of physical collapse, which was more obvious among those with smaller particle sizes. MCs with particle sizes of 1.38 μm (MC-S), 5.13 μm (MC-M) and 10.05 μm (MC-L) had shell thicknesses of 39.6, 50.3 and 150.1 nm, respectively. MCs with smaller particles tended to have significantly faster release profiles, and the MC-S group had much higher bioactivity against Agrotis ipsilon and better foliar affinity on the peanut leaves (indicated by rainfastness) than MC-M and MC-L. All of the MCs exhibited light-enhanced release profiles and had much slower degradation compared with the emulsifiable concentrate (EC) group, among which MC-L had the slowest degradation. To generate MCs with both favorable quick efficacy and long-lasting efficacy, binary mixtures of MC-S, MC-M and MC-L were produced by mixing them in pairs at ratios of 2:1, 1:1 and 1:2. The mixture of MC-S:MC-L at 1:2 showed the best comprehensive efficacy in the peanut foliar spray scenario among the nine tested combinations, and its effective duration was three times longer than that of EC. Overall, the precise combination of MCs with different particle sizes can regulate the efficacy of pesticide control and serve as a strategy for the better utilization of pesticides.

Three rat sarcoma (RAS) gene isoforms, KRAS, NRAS, and HRAS, constitute the most mutated family of small GTPases in cancer. While the development of targeted immunotherapies has led to a substantial improvement in the overall survival of patients with non-KRAS-mutant cancer, patients with RAS-mutant cancers have an overall poorer prognosis owing to the high aggressiveness of RAS-mutant tumors. KRAS mutations are strongly implicated in lung, pancreatic, and colorectal cancers. However, RAS mutations exhibit diverse patterns of isoforms, substitutions, and positions in different types of cancers. Despite being considered "undruggable", recent advances in the use of allele-specific covalent inhibitors against the most common mutant form of RAS in non-smallcell lung cancer have led to the development of effective pharmacological interventions against RAS-mutant cancer. Sotorasib (AMG510) has been approved by the FDA as a second-line treatment for patients with KRAS-G12C mutant NSCLC who have received at least one prior systemic therapy. Other KRAS inhibitors are on the way to block KRAS-mutant cancers. In this review, we summarize the progress and promise of small-molecule inhibitors in clinical trials, including direct inhibitors of KRAS, pan-RAS inhibitors, inhibitors of RAS effector signaling, and immune checkpoint inhibitors or combinations with RAS inhibitors, to improve the prognosis of tumors with RAS mutations. [ABSTRACT FROM AUTHOR]

Current ensemble learning combination strategies have difficulty in accommodating information and explainability of models among base learners. Using evidential reasoning (ER) rules as a combination strategy, the above problems can be solved by involving the results of each base learner as evidence in the integration. However, the evidence parameters of conventional ER rule are single, and it is obviously unreasonable to use the same evidence parameters for different base learner models. Therefore, an ensemble learning method based on adaptive evidential reasoning (A-ER) rule is proposed. This method judges the type of evidence before each evidence fusion. Different evidence parameters are adaptively assigned according to different types of evidence. Results from different classification cases show that the proposed method has a higher classification accuracy compared to other methods in the cases. This proves that the method has more reasonable evidence parameter settings and superior interpretability and generalization ability. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide, despite several advances has been achieved in last decades. Few prognostic and predictive biomarkers guide therapeutic choice in metastatic CRC (mCRC), among which DNA mismatch repair deficiency and/or microsatellite instability (dMMR/MSI) holds a crucial role. Tumors characterized by dMMR/MSI benefit from immune checkpoint inhibitors. However, most of the mCRC patients (around 95%) are microsatellite stable (MSS), thereby intrinsically resistant to immunotherapy. This represents a clear unmet need for more effective treatments in this population of patients. In this review, we aim to analyze immune-resistance mechanisms and therapeutic strategies to overcome them, such as combinations of immunotherapy and chemotherapy, radiotherapy or target therapies specifically in MSS mCRC. We also explored both available and potential biomarkers that may better select MSS mCRC patients for immunotherapy. Lastly, we provide a brief overview on future perspectives in this field, such as the gut microbiome and its potential role as immunomodulator. [ABSTRACT FROM AUTHOR]

The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide, despite several advances has been achieved in last decades. Few prognostic and predictive biomarkers guide therapeutic choice in metastatic CRC (mCRC), among which DNA mismatch repair deficiency and/or microsatellite instability (dMMR/MSI) holds a crucial role. Tumors characterized by dMMR/MSI benefit from immune checkpoint inhibitors. However, most of the mCRC patients (around 95%) are microsatellite stable (MSS), thereby intrinsically resistant to immunotherapy. This represents a clear unmet need for more effective treatments in this population of patients. In this review, we aim to analyze immune-resistance mechanisms and therapeutic strategies to overcome them, such as combinations of immunotherapy and chemotherapy, radiotherapy or target therapies specifically in MSS mCRC. We also explored both available and potential biomarkers that may better select MSS mCRC patients for immunotherapy. Lastly, we provide a brief overview on future perspectives in this field, such as the gut microbiome and its potential role as immunomodulator.

The third-generation epidermal growth factor receptor (EGFR) inhibitor osimertinib (OSI) has been approved as the first-line treatment for EGFR-mutant non-small cell lung cancer (NSCLC). This study aims to explore a rational combination strategy for enhancing the OSI efficacy. In this study, OSI induced higher CD47 expression, an important anti-phagocytic immune checkpoint, via the NF-κB pathway in EGFR-mutant NSCLC HCC827 and NCI-H1975 cells. The combination treatment of OSI and the anti-CD47 antibody exhibited dramatically increasing phagocytosis in HCC827 and NCI-H1975 cells, which highly relied on the antibody-dependent cellular phagocytosis effect. Consistently, the enhanced phagocytosis index from combination treatment was reversed in CD47 knockout HCC827 cells. Meanwhile, combining the anti-CD47 antibody significantly augmented the anticancer effect of OSI in HCC827 xenograft mice model. Notably, OSI induced the surface exposure of "eat me" signal calreticulin and reduced the expression of immune-inhibitory receptor PD-L1 in cancer cells, which might contribute to the increased phagocytosis on cancer cells pretreated with OSI. In summary, these findings suggest the multidimensional regulation by OSI and encourage the further exploration of combining anti-CD47 antibody with OSI as a new strategy to enhance the anticancer efficacy in EGFR-mutant NSCLC with CD47 activation induced by OSI. [ABSTRACT FROM AUTHOR]

When the state of the sensors of the wind turbine, especially the anemometer, is abnormal, it will affect the correctness of other parameters of the whole power system. To recognize and reconstruct the abnormal data accurately and efficiently, this study proposes an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), extreme learning machine (ELM), crazy improve butterfly optimization algorithm (CIBOA), and bi-directional long short-term memory (BILSTM). In this system, the ICEEMDAN is utilized to decompose the original wind speed sequence, and then the ELM is employed as the initial prediction engine to extract the features of each sub-sequence to achieve the preliminary prediction outcomes. The CIBOA is employed to optimize the BILSTM model parameters. To further explore the unsteady features in the wind speed series, the residual results of the preliminary prediction are modeled using BILSTM, and the predicted residuals and preliminary results are integrated to obtain the final reconstructed values. In addition, the combined model is discussed in detail employing six assessment indicators, improvements of the reconstruction model, Diebold-Mariano test, operation time, and sensitivity analysis. The results indicate that the Pearson correlation coefficient (PCC) values of the proposed model are 0.9986, 0.9978, and 0.9979, respectively. It is concluded that the proposed hybrid reconstruction model accurately identifies and reconstructs abnormal wind speed, which provides a new technique for the reasonable utilization of wind energy. • Combines signal decomposition, multiple reconstruction modeling, intelligent optimization, and deep learning. • Initial and residual forecasts are applied simultaneously to identify and reconstruct abnormal wind speeds. • Improved intelligent optimization algorithm for parameter optimization. • Developed an abnormal wind speed identification and reconstruction system. [ABSTRACT FROM AUTHOR]

Perinatal depression (PD), which affects about 10–20 percent of women, often goes unnoticed because related symptoms frequently overlap with those commonly experienced during pregnancy. Moreover, identifying PD currently depends heavily on the use of questionnaires, and objective biological indicators for diagnosis has yet to be identified. This research proposes a safe and non-invasive method for diagnosing PD and aims to delve deeper into its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram (EEG) for mothers-to-be and fetuses, we collected the resting-state scalp EEG of pregnant women (with PD/healthy) at the 38th week of gestation. To compensate for the low spatial resolution of scalp EEG, source analysis was first applied to project the scalp EEG to the cortical-space. Afterwards, cortical-space networks and large-scale networks were constructed to investigate the mechanism of PD from two different level. Herein, differences in the two distinct types of networks between PD patients and healthy mothers-to-be were explored, respectively. We found that the PD patients illustrated decreased network connectivity in the cortical-space, while the large-scale networks revealed weaker connections at cerebellar area. Further, related spatial topological features derived from the two different networks were combined to promote the recognition of pregnant women with PD from those healthy ones. Meanwhile, the depression severity at patient level was effectively predicted based on the combined spatial topological features as well. These findings consistently validated that the two kinds of networks indeed played off each other, which thus helped explore the underlying mechanism of PD; and further verified the superiority of the combination strategy, revealing its reliability and potential in diagnosis and depression severity evaluation. • Non-invasive scalp EEG emerges as an optimal diagnostic tool for identifying depression in expectant mothers. • Dual-network EEG analysis provides a comprehensive understanding of perinatal depression. • Combining spatial topological features from different networks promotes diagnosis and evaluation of perinatal depression. [ABSTRACT FROM AUTHOR]

Deep learning has made fascinating achievements in fault diagnosis of rotating machinery. However, the individual deep learning models generally have poor performances under variable operating conditions or noise environment. Additionally, they are prone to overfitting when dealing with unbalanced fault data. Therefore, an ensemble method based on wavelet packet transform (WPT) and convolutional neural networks (CNNs) is presented for rotating machinery fault diagnosis. First, the raw signals are transformed into multiple wavelet packet coefficients with local information and a reconstructed signal with global information through WPT. Then, these signals are separately fed into the corresponding CNN models for diagnosis. Finally, the diagnosis results of multiple CNNs are combined into a more stable and accurate diagnosis result through the improved weighted voting strategy. The proposed method is applied to the fault diagnosis of the motor bearing and the CNC machine tool spindle bearing. Compared with the other traditional diagnosis methods, the results show that the proposed method achieves a better diagnostic performance. [ABSTRACT FROM AUTHOR]

Lung cancer continues to be the deadliest cancer worldwide. A new strategy of combining chemotherapeutics with naturally occurring anticancer compounds, such as lactoferrin, might improve the efficacy and toxicity of current chemotherapy. The aim of this study was to evaluate the effect of recombinant human lactoferrin (rhLf) in combination with etoposide on anticancer activity in human lung adenocarcinoma cells. In addition, we examined the impact of rhLf on etoposide-induced cytotoxicity of human endothelial cells. We found that treatment of A549 cells with a combination of etoposide and rhLf resulted in significantly greater inhibition of cancer cell growth as compared to etoposide alone. The combination repressed cancer cell growth by cell cycle arrest in the G2/M phase and induction of apoptosis. In contrast to cancer cells, rhLf did not affect endothelial cell viability. Importantly, rhLf significantly diminished the etoposide-induced cytotoxicity of endothelial cells. Analysis of the type of drug interaction based on combination index value showed that rhLf synergized with etoposide to induce anticancer activity. The calculated dose reduction index indicated that the combination treatment reduced a 10-fold of etoposide dose to achieve the same anticancer effect. Our data demonstrate that rhLf enhanced the anticancer activity of etoposide and diminished etoposide-induced cytotoxic effect in endothelial cells. [ABSTRACT FROM AUTHOR]

IntroductionBecause of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier.MethodsFirst, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier.ResultsOur proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively.DiscussionCompared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management.

Cancer, which is a leading cause of deaths around the world, is characterized by genetic mutations and epigenetic changes. Baicalin and its aglycone baicalein, the major bioactive flavones derived from the dried root of Scutellaria baicalensis Georigi, belong to flavonoid compounds. Many studies demonstrated that both of them exhibited remarkable promising anticancer activities. This study summarized potential anticancer mechanisms of baicalin and baicalein including induction of apoptosis, initiation of cell cycle arrest, suppression of metastasis, induction of autophagy, and regulation of immunity. Combination strategies involving baicalin or baicalein as chemotherapeutic adjuvants, clinical trial and safety were also discussed. In addition, we compared the difference in their anticancer effects. Interestingly, baicalein showed quicker and stronger inhibitory effects on multiple cancers than those of baicalin, probably due to its smaller size and high lipophilicity which contribute to fast absorption and improve ability to penetrate cells. Taken together, both baicalin and baicalein are effective in treating cancer with good tolerance. However, deglycosylation of baicalin to baicalein was found to have stronger anticancer potential. [ABSTRACT FROM AUTHOR]

The non‐destructive and rapid estimation of soil total carbon (SOC) content with mid‐infrared spectroscopy (MIR, 4000–400 cm−1) will play a vital role in precision agriculture. However, the benefit derived from the full MIR range is compromised by multicollinearity and noise. Hence, variable selection methods have been developed to reduce the full spectrum to a few variables that contribute the most information to a property of interest. However, only a few studies have applied variable selection methods in the MIR region to estimate organic carbon content in the soil. Therefore, four variable selection methods, namely stability competitive adaptive reweighted sampling (sCARS), bootstrapping soft shrinkage (BOSS), interval combination optimization (ICO) and the interval combination optimization‐successive projections algorithm (ICO‐SPA) method were investigated to ascertain the method that identified the wavebands most sensitive to SOC in order to improve prediction accuracy. The selected variables (i.e., reduced spectrum), as well as the full MIR spectrum, were coupled with partial least squares regression (PLSR) for the model calibration of SOC. The results showed that the models based on variable selection achieved higher prediction accuracy than the full spectrum model. sCARS selected 19 variables, BOSS selected 21 variables, ICO selected 311 variables, whereas ICO‐SPA selected only 9 variables (accounting for 0.38% of all variables), while the prediction accuracy of the ICO‐SPA‐PLSR model was similar to those of the other three variable selection methods. The ICO‐SPA‐PLSR model had an Rp2 value of 0.93, RPD of 3.90 and RMSEP of 0.13%. This method identified 3450, 2920, 2767, 2000, 1800, 1765, 1600, 1560 and 927 cm−1 as the feature wavenumbers with the most useful information in accurately estimating SOC content. Therefore, the combination strategy of interval (ICO) and individual (SPA) variable selection may be a good alternative variables selection method for MIR spectroscopic data. Highlights: Different spectral variable selection methods were compared for SOC estimation by MIR.ICO‐SPA variable selection was employed to improve the prediction accuracy of SOC by MIR spectra.ICO‐SPA method selected informative key spectral variables of SOC in MIR range.Interval coupled with individual variable selection can be an alternative for spectroscopic data. [ABSTRACT FROM AUTHOR]

Deoxynojirimycin (DNJ) is an α-glucosidase inhibitor with high food values. However, the complex and costly enrichment processes have greatly prevented its application. Herein, this study aimed to propose a simple and efficient enrichment process for DNJ from Morus alba L. extracts using cation exchange resins. The LSI and D113 resins were chosen due to their excellent adsorption and desorption properties. The adsorption characteristics agreed with the pseudo-first-order kinetic model and the Langmuir isotherm model. This adsorption was chemisorption, spontaneous, endothermic and entropy-driven. Furthermore, the concentration and pH of the extracts, desorption solvent, breakthrough and elution curves, sample loading and elution rate were investigated to optimize the enrichment process by resin column chromatography. The results also showed that the purity of DNJ was improved to 44.00 % with a total recovery of 78.21 % using the LSI-D113 combination strategy. This research demonstrated the industrial feasibility of DNJ enrichment using cation exchange resins. [Display omitted] • The adsorption/desorption features of cation exchange resins on DNJ were studied. • The adsorption process was fitted to the Langmuir and pseudo-first-order model. • The adsorption was spontaneous, endothermic, chemisorption and entropy-driven. • The cation exchange resin columns combination strategy improved DNJ enrichment. • The enrichment process was simple, low-cost and suited for large-scale production. [ABSTRACT FROM AUTHOR]

The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%.

Background: Patients with refractory central nervous system leukemia (CNSL) have a dismal prognosis and lack effective therapy. Case reports have shown that sorafenib is effective against brain metastases, including leukemia. Methods: To explore the efficacy of sorafenib combined with conventional therapies for refractory CNSL, a phase 2 study was conducted. The primary end point was the complete remission rate (CRR) within 8 weeks of treatment. Secondary end points included the overall response rate (ORR), event‐free survival (EFS), overall survival (OS), and adverse events (AEs). Results: Twenty‐six patients with refractory CNSL were enrolled; they included 17 with isolated CNSL, 7 with hematological relapse, and 2 with another extramedullary relapse. After 8 weeks of treatment, 21 patients achieved complete remission, 2 achieved partial remission, and 3 achieved no remission for a CRR of 80.8% (95% CI, 62.1%‐91.5%) and an ORR of 88.5% (95% CI, 71.0%‐96.0%). Twenty patients survived, and 6 died. The 2‐year EFS and OS rates were 75.0% (95% CI, 54.5%‐88.3%) and 76.9% (95% CI, 54.2%‐90.4%), respectively. Six patients experienced grade 3 or 4 treatment‐related AEs, including moderate chronic graft‐vs‐host disease (n = 3), grade 3 or 4 acute graft‐vs‐host disease (n = 2), and grade 3 skin rash (n = 1). No treatment‐related deaths occurred during the therapy of refractory CNSL. Conclusions: Sorafenib combined with conventional therapies is effective and safe for refractory CNSL. Lay Summary: Sorafenib combined with conventional therapies is effective and safe for refractory central nervous system leukemia. Sorafenib combined with conventional therapies is effective and safe for refractory central nervous system leukemia. [ABSTRACT FROM AUTHOR]

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Triple-negative breast cancer (TNBC) is a subtype of human breast cancer with one of the worst prognoses, with no targeted therapeutic strategies currently available. Regulated cell death (RCD), also known as programmed cell death (PCD), has been widely reported to have numerous links to the progression and therapy of many types of human cancer. Of note, RCD can be divided into numerous different subroutines, including autophagy-dependent cell death, apoptosis, mitotic catastrophe, necroptosis, ferroptosis, pyroptosis and anoikis. More recently, targeting the subroutines of RCD with small-molecule compounds has been emerging as a promising therapeutic strategy, which has rapidly progressed in the treatment of TNBC. Therefore, in this review, we focus on summarizing the molecular mechanisms of the above-mentioned seven major RCD subroutines related to TNBC and the latest progress of small-molecule compounds targeting different RCD subroutines. Moreover, we further discuss the combined strategies of one drug (e.g., narciclasine) or more drugs (e.g., torin-1 combined with chloroquine) to achieve the therapeutic potential on TNBC by regulating RCD subroutines. More importantly, we demonstrate several small-molecule compounds (e.g., ONC201 and NCT03733119) by targeting the subroutines of RCD in TNBC clinical trials. Taken together, these findings will provide a clue on illuminating more actionable low-hanging-fruit druggable targets and candidate small-molecule drugs for potential RCD-related TNBC therapies. [ABSTRACT FROM AUTHOR]

Summary: The emergence of acquired resistance limits the long-term efficacy of EGFR tyrosine kinase inhibitors (EGFR TKIs). Thus, development of effective strategies to overcome resistance to EGFR TKI is urgently needed. Multiple mechanisms to reactivate ERK signaling have been successfully demonstrated in acquired resistance models. We found that in EGFR mutant non-small cell lung cancer (NSCLC) patients, acquired resistance to EGFR TKIs was accompanied by increased activation of ERK. Increased ERK activation was also found in in vitro models of acquired EGFR TKI resistance. ASN007 is a potent selective ERK1/2 inhibitor with promising antitumor activity in cancers with BRAF and RAS mutations. ASN007 treatment impeded tumor cell growth and the cell cycle in EGFR TKI-resistant cells. In addition, combination treatment with ASN007 and EGFR TKIs significantly decreased the survival of resistant cells, enhanced induction of apoptosis, and effectively inhibited the growth of erlotinib-resistant xenografts, providing the preclinical rationale for testing combinations of ASN007 and EGFR TKIs in EGFR-mutated NSCLC patients. This study emphasizes the importance of targeting ERK signaling in maintaining the long-term benefits of EGFR TKIs by overcoming acquired resistance. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the efficacy of negative pressure wound therapy (NPWT) combined with platelet-rich fibrin (PRF) in treating bone-exposed wounds and explore its possible mechanism. Materials & methods: A bone-exposed wound was created in a total of 32 healthy Sprague-Dawley rats, which were divided into either control group, NPWT group, PRF group or both (N + P group). The bone-exposed area, skin contraction rate and granulation coverage and the level of growth factors in granulation tissue were determined on days 4, 7 and 10. Results: The N + P group showed significantly higher wound closure rate than that achieved with others respectively. Four factors were significantly higher in N + P group than in the other three groups. Conclusion: Combination of NPWT and PRF can repair bone-exposed wounds effectively and accelerate wound healing. Wounds with exposed bone are refractory, and are usually caused by mechanical trauma, burns, infections, pressure ulcers, diabetic illness and tumor resection. Surgery is the most effective way to cover such wounds. However, repairing defects with local or distal skin flaps can cause potential donor site damage. Skin grafting might be a simpler method, but the avascular interface of bone makes granulation tissue difficult to grow, thus transplanting skin on exposed bones is contraindicated. There is growing interest in identifying alternative treatment strategies. In this study, our goal is to use platelet-derived fibrin in combination with negative pressure wound therapy (NPWT) to determine whether this combined strategy can achieve mesenchymal healing with granulation tissue to treat bone-exposed wounds. The results of wound healing in the NPWT and platelet-rich fibrin combined treatment group were better than those of other treatment groups. The combined treatment strategy of platelet-derived fibrin and NPWT can repair bone-exposed wounds effectively, accelerate wound healing and is a promising therapeutic option for bone-exposed wounds. [ABSTRACT FROM AUTHOR]

Abstract Venetoclax has been approved by the United States Food and Drug Administration since 2016 as a monotherapy for treating patients with relapsed/refractory chronic lymphocytic leukemia having 17p deletion. It has led to a breakthrough in the treatment of hematologic malignancies in recent years. However, unfortunately, resistance to venetoclax is inevitable. Multiple studies confirmed that the upregulation of the anti-apoptotic proteins of the B-cell lymphoma 2 (BCL2) family mediated by various mechanisms, such as tumor microenvironment, and the activation of intracellular signaling pathways were the major factors leading to resistance to venetoclax. Therefore, only targeting BCL2 often fails to achieve the expected therapeutic effect. Based on the mechanism of resistance in specific hematologic malignancies, the combination of specific drugs with venetoclax was a clinically optional treatment strategy for overcoming resistance to venetoclax. This study aimed to summarize the possible resistance mechanisms of various hematologic tumors to venetoclax and the corresponding clinical strategies to overcome resistance to venetoclax in hematologic malignancies.

Abstract Epigenetics is dynamic and heritable modifications to the genome that occur independently of DNA sequence. It requires interactions cohesively with various enzymes and other molecular components. Aberrant epigenetic alterations can lead to inappropriate onset of genetic expressions and promote tumorigenesis. As the epigenetic modifiers are susceptible to extrinsic factors and reversible, they are becoming promising targets in multiple cancer therapies. Recently, various epi-drugs have been developed and implicated in clinical use. The use of epi-drugs alone, or in combination with chemotherapy or immunotherapy, has shown compelling outcomes, including augmentation of anti-tumoral effects, overcoming drug resistance, and activation of host immune response.

The deep belief network is widely used in fault diagnosis and health management of rotating machinery. However, on the one hand, deep belief networks only tend to focus on the global information of bearing vibration, ignoring local information. On the other hand, the single deep belief network has limited learning ability and cannot diagnose the health of rotating machinery more accurately and stably. As a non-recursive variational signal decomposition method, variational mode decomposition can easily obtain local information of signals. And the ensemble deep belief network composed of multiple deep belief networks also improves the accuracy and stability of the health status diagnosis of rotating machinery. This paper combines the advantages of ensemble deep belief network and variational mode decomposition to propose a novel diagnostic method for rolling bearings. Firstly, the variational mode decomposition is used to decompose the vibration data of the rolling bearing into intrinsic mode functions with local information. Then, using the deep belief network based on cross-entropy to learn the intrinsic mode functions of the rolling bearing data and reconstruct the vibration data. Finally, In the decision-making layer, the improved combination strategy is used to process the health status information of the bearings obtained by multiple deep belief networks to obtain a more accurate and stable diagnosis result. This method is used to diagnose experimental bearing vibration data. The results show that the method can simultaneously focus on and learn the global and local information of bearing vibration data and overcome the limitations of individual deep learning models. Experiments show that it is more effective than the existing intelligent diagnosis methods.

Effective emission control of cooking oil fumes (COFs), particularly for grease particles, has always been a cause of great concern for catering industry. The review and evaluation of combinations of purification technology are urgently required. This work presents a literature review and combination strategy evaluation of purification technology of grease particles of commercial kitchens. A variety of mainstream purification technologies, such as mechanical separation (M), filtration (F), washing absorption (W) and electrostatic deposition (E) are discussed. In order to establish a complete and efficient fume purification system for commercial kitchen, this study proposes the four-point principles of combined purification technologies as: (1) from easy to difficult (for grease particle diameter); (2) fire prevention and noise reduction; (3) electrostatic deposition postposition; (4) Absorption and dissolution (by-product from electrostatic). Based on the above principles and separation characteristics, the recommended combinations of purification strategies are M-E, F-E, M-F-E and M-E-F. The combination strategy of M-F-E is adopted as an example to evaluate and optimize COFs purification system use life cycle assessment approach. The results indicate that the optimization of the M-F-E purification system using rotating mesh plate instead of baffle filter can reduce the environmental impact of global warming and eutrophication by about 35% which reduces the emissions of CO2 and SO2 from 92.533 kg and 0.110 kg to 60.214 kg and 0.072 kg, respectively. Besides the review of relevant purification technologies, the study also proposes the combination of principles of purification technologies and the evaluation and optimization of life cycle assessment for the optimal design of combined purification system. [ABSTRACT FROM AUTHOR]

Lung cancer continues to be the deadliest cancer worldwide. A new strategy of combining chemotherapeutics with naturally occurring anticancer compounds, such as lactoferrin, might improve the efficacy and toxicity of current chemotherapy. The aim of this study was to evaluate the effect of recombinant human lactoferrin (rhLf) in combination with etoposide on anticancer activity in human lung adenocarcinoma cells. In addition, we examined the impact of rhLf on etoposide-induced cytotoxicity of human endothelial cells. We found that treatment of A549 cells with a combination of etoposide and rhLf resulted in significantly greater inhibition of cancer cell growth as compared to etoposide alone. The combination repressed cancer cell growth by cell cycle arrest in the G2/M phase and induction of apoptosis. In contrast to cancer cells, rhLf did not affect endothelial cell viability. Importantly, rhLf significantly diminished the etoposide-induced cytotoxicity of endothelial cells. Analysis of the type of drug interaction based on combination index value showed that rhLf synergized with etoposide to induce anticancer activity. The calculated dose reduction index indicated that the combination treatment reduced a 10-fold of etoposide dose to achieve the same anticancer effect. Our data demonstrate that rhLf enhanced the anticancer activity of etoposide and diminished etoposide-induced cytotoxic effect in endothelial cells.

Purpose: This single-center, open-label, single-arm, phase II clinical trial aimed to examine the efficacy and safety of the first-generation EGFR-TKIs combined with chemotherapy among treatment-naïve advanced non-small-cell lung cancer (NSCLC) patients harboring sensitive EGFR mutations. Materials and Methods: Patients with advanced EGFR-mutant NSCLC were given concurrent gefitinib (250 mg orally daily) and 3-week cycle of carboplatin plus pemetrexed for 4 to 6 cycles, followed by gefitinib maintenance until disease progression or unacceptable toxicity. The primary endpoint was progression-free survival (PFS), and the secondary endpoints were overall survival (OS), objective response rate (ORR), disease control rate (DCR) and safety. This trial was registered at ClinicalTrials.gov (NCT02886195). Results: Of the 21 patients enrolled in this study, a 76.2% ORR and 100% DCR were observed and a higher ORR was seen in patients with EGFR 21L858R mutations than in those with 19del mutations (P = 0.012). The subjects had a median PFS of 15.0 months and a median OS of 26.0 months, and numerically longer PFS was seen in patients with EGFR 21L858R mutations than in those with 19del mutations (P = 0.281). There were 15 NSCLC patients without cerebral metastases at baseline, with 4 cases developing cerebral metastases during the treatment, and the 6-, 12- and 24-month cumulative incidence rates of the central nervous system metastasis were 6.67%, 13.3% and 26.7%, respectively. There were 17 subjects with progressive diseases tested for EGFR T790M mutations, and 11 cases were positive for T790M mutations. Grade 3 toxicity included neutropenia (9.5%), leukopenia (4.8%), liver dysfunction (9.5%) and diarrhea (4.8%), and no grade 4 adverse events or treatment-related death occurred. Conclusion: The combination of first-generation EGFR-TKIs and chemotherapy achieves a satisfactory PFS, ORR and DCR and well-tolerated toxicity in advanced NSCLC patients with EGFR mutations, notably in patients with EGFR L858R mutations. [ABSTRACT FROM AUTHOR]

Society 5.0 is a human-centric initiative to exploit cutting edge communication and automation technologies for the sake of the whole society with the collaboration of stakeholders from public, civil and private sectors. Social innovativeness plays an important role in this social initiative whose common strategic goal is to generate benefits both in social and economic terms in order to build a super smart society that continuously provides people with new value offerings. In this concern, the organized members of the society, i.e. public, private and civil sector organizations as the real providers of these social values need to craft innovative strategic approaches to adapt the newer contingencies of Society 5.0. The aim of this study is to develop propositions on such critical strategic choices pertaining to strategy selections, combinations, revisions, and reconfigurations. [ABSTRACT FROM AUTHOR]

The peptide‐based therapeutic cancer vaccines have attracted enormous attention in recent years as one of the effective treatments of tumour immunotherapy. Most of peptide‐based vaccines are based on epitope peptides stimulating CD8+ T cells or CD4+ T helper cells to target tumour‐associated antigens (TAAs) or tumour‐specific antigens (TSAs). Some adjuvants and nanomaterials have been exploited to optimize the efficiency of immune response of the epitope peptide to improve its clinical application. At present, numerous peptide‐based therapeutic cancer vaccines have been developed and achieved significant clinical benefits. Similarly, the combination of peptide‐based vaccines and other therapies has demonstrated a superior efficacy in improving anti‐cancer activity. We delve deeper into the choices of targets, design and screening of epitope peptides, clinical efficacy and adverse events of peptide‐based vaccines, and strategies combination of peptide‐based therapeutic cancer vaccines and other therapies. The review will provide a detailed overview and basis for future clinical application of peptide‐based therapeutic cancer vaccines. [ABSTRACT FROM AUTHOR]

CAR-T cell therapies hold great potential in achieving long-term remission in patients suffering from malignancies. However, their efficacy in treating solid tumors is impeded by challenges such as limited infiltration, compromised cancer recognition, decreased cytotoxicity, heightened exhaustion, absence of memory phenotypes, and inevitable toxicity. To surmount these obstacles, researchers are exploring innovative strategies, including the integration of CAR-T cells with targeted inhibitors. The combination of CAR-T therapies with specific targeted drugs has shown promise in enhancing CAR-T cell infiltration into tumor sites, boosting their tumor recognition capabilities, strengthening their cytotoxicity, alleviating exhaustion, promoting the development of a memory phenotype, and reducing toxicity. By harnessing the synergistic potential, a wider range of patients with solid tumors may potentially experience favorable outcomes. To summarize the current combined strategies of CAR-T therapies and targeted therapies, outline the potential mechanisms, and provide insights for future studies, we conducted this review by collecting existing experimental and clinical evidence., Competing Interests: Declaration of interests The authors declare competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Neoangiogenesis is a recognized hallmark of cancer, granting tumor cells to dispose of metabolic substrates through a newly created vascular supply. Neoangiogenesis was also confirmed in melanoma, where vascular proliferation is associated with increased aggressiveness and poorer prognosis. Furthermore, melanoma cells show the so-called vascular mimicry, consisting in the assumption of endothelial-like features inducing the expression of pro-angiogenic receptors and ligands, which take part in the interplay with extracellular matrix (ECM) components and are potentiated by the ECM remodeling and the barrier molecule junction alterations that characterize the metastatic phase. Although neoangiogenesis was biologically proven and clinically associated with worse outcomes in melanoma patients, in the past anti-angiogenic therapies were employed with poor improvement of the already unsatisfactory results associated with chemotherapic agents. Among the novel therapies of melanoma, immunotherapy has led to previously unexpected outcomes of treatment, yet there is a still strong need for potentiating the results, possibly by new regimens of combination therapies. Molecular models in many cancer types showed mutual influences between immune responses and vascular normalization. Recently, clinical trials are investigating the efficacy of the association between anti-angiogenetic agents and immune-checkpoint inhibitors to treat advanced stage melanoma. This paper reviews the biological bases of angiogenesis in melanoma and summarizes the currently available clinical data on the use of anti-angiogenetic compounds in melanoma.

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Epigenetics is dynamic and heritable modifications to the genome that occur independently of DNA sequence. It requires interactions cohesively with various enzymes and other molecular components. Aberrant epigenetic alterations can lead to inappropriate onset of genetic expressions and promote tumorigenesis. As the epigenetic modifiers are susceptible to extrinsic factors and reversible, they are becoming promising targets in multiple cancer therapies. Recently, various epi-drugs have been developed and implicated in clinical use. The use of epi-drugs alone, or in combination with chemotherapy or immunotherapy, has shown compelling outcomes, including augmentation of anti-tumoral effects, overcoming drug resistance, and activation of host immune response. [ABSTRACT FROM AUTHOR]

WEE1 kinase is renowned as an S-G2 checkpoint inhibitor activated by ATR-CHK1 in response to replication stress. WEE1 inhibition enhances replication stress and effectively circumvents checkpoints into mitosis, which triggers significant genetic impairs and culminates in cell death. This approach has been validated clinically for its promising anti-tumor efficacy across various cancer types, notably in cases of ovarian cancers. Nonetheless, the initial stage of clinical trials has shown that the first-in-human WEE1 inhibitor adavosertib is limited by dose-limiting adverse events. As a result, recent efforts have been made to explore predictive biomarkers and smart combination schedules to alleviate adverse effects. In this review, we focused on the exploration of therapeutic biomarkers, as well as schedules of combination utilizing WEE1 inhibitors and canonical anticancer drugs, according to the latest preclinical and clinical studies, indicating that the optimal application of WEE1 inhibitors will likely be as part of dose-reducing combination and be tailored to specific patient populations. [Display omitted] • Adavosertib has the best efficacy in ovarian and endometrial cancers. • CCNE1 amplification is the most promising biomarker of WEE1 inhibitors. • Chemotherapy or PARPi plus WEE1i enhances clinical efficacy but also toxicity. • Sequential administration might alleviate WEE1i-mediated adverse effects. • WEE1 inhibition enhances immunotherapy through recruitment, activation and cytotoxicity of immune cells. [ABSTRACT FROM AUTHOR]