Activation of polyamine catabolism promotes glutamine metabolism and creates a targetable vulnerability in lung cancer.

Polyamines are a class of small polycationic alkylamines that play essential roles in both normal and cancer cell growth. Polyamine metabolism is frequently dysregulated and considered a therapeutic target in cancer. However, targeting polyamine metabolism as monotherapy often exhibits limited efficacy, and the underlying mechanisms are incompletely understood. Here we report that activation of polyamine catabolism promotes glutamine metabolism, leading to a targetable vulnerability in lung cancer. Genetic and pharmacological activation of spermidine/spermine N1-acetyltransferase 1 (SAT1), the rate-limiting enzyme of polyamine catabolism, enhances the conversion of glutamine to glutamate and subsequent glutathione (GSH) synthesis. This metabolic rewiring ameliorates oxidative stress to support lung cancer cell proliferation and survival. Simultaneous glutamine limitation and SAT1 activation result in ROS accumulation, growth inhibition, and cell death. Importantly, pharmacological inhibition of either one of glutamine transport, glutaminase, or GSH biosynthesis in combination with activation of polyamine catabolism synergistically suppresses lung cancer cell growth and xenograft tumor formation. Together, this study unveils a previously unappreciated functional interconnection between polyamine catabolism and glutamine metabolism and establishes cotargeting strategies as potential therapeutics in lung cancer.

The Role of the Cerebellum in Drug Reward: A Review.

Drug abuse remains a global problem; nonetheless, its mechanism has not yet been fully understood. Recent studies have reported on the non-motor functions of the cerebellum, and evidence from neuroimaging and behavioral studies has suggested the role of cerebellum in drug reward, which has received increasing attention. Furthermore, emerging technological developments have aided in clarifying the various circuits and functions of the cerebellum. Exploring the role of the cerebellum in drug reward can improve our understanding of the mechanism underlying addiction and facilitate the development of new treatment schemes. This review summarizes the anatomy of the cerebellum and its connections to brain regions considered important in addiction. Subsequently, we investigate the neurological reasons elucidating why the cerebellum is a potential target for drug reward. Additionally, we expound the molecular targets of addictive drugs in the cerebellum, mainly glutamate and endocannabinoids. Unlike previous studies, this article focuses on the influence of alcohol, nicotine, morphine, cannabis, and cocaine on the cerebellum from multiple viewpoints, including imaging and behavioral changes, molecular signals, neurotransmitters, and synaptic transmission. We aim to clarify some drug-induced cerebellar changes to supplement the previous research regarding the relationship between addiction and the cerebellum. Finally, we discuss the limitations and prospects of drug reward research on the cerebellum to provide novel insights into studying the cerebellum and its role in addiction. We recommend that future addiction network models should include the cerebellum to provide new therapeutic targets for treating addiction.

The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.

Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.

Multicolor emissive carbon dot-based fluorometric analysis platform for rapid quantification and discrimination of nitroimidazole antibiotic residues.

The development of efficient, rapid, portable, and accurate analysis of veterinary drug residues in food matrices is in great demand for food safety assessment. Here, we have developed a smartphone-integrated platform for fluorometric quantification of metronidazole (MNZ) residues and constructed a sensor array for discrimination of different nitroimidazole antibiotics (NIIMs). Multicolor CDs (B-CDs, C-CDs, Y-CDs, and R-CD) were prepared and showed different fluorescence response to MNZ. The fluorescence of C-CDs was quenched Because of the inner filter effect (IFE) between the C-CDs and MNZ, while that of R-CDs was enhanced due to the passivation of surface defects by MNZ. Based on the response pattern, the fluorometric quantification of MNZ based on the fluorescence images of C-CD + R-CD system (R/G values) was achieved with a low detection limit of 0.45 µM. By designing a smartphone-integrated platform, the analysis can be completed within 20 min. In addition, a fluorescence sensor array based C-CDs and R-CDs was also developed. The unique fingerprint of each NIIMs was obtained by linear discriminant analysis (LDA) of the response patterns, indicating an effective discrimination of five NIIMs. Moreover, the platform was used for quantification of MNZ in food samples and the recoveries were within 84.0-106.3 % with relative standard deviations 1.2-10.2 %. Therefore, the proposed method shows great potential as a universal platform for rapid detection of veterinary drug residues.

A comparative analysis of energy expenditure and substrate metabolism in male university students with overweight/obesity: Tabata vs HIIT and MICT.

Introduction: Exploring the energy expenditure and substrate metabolism data during exercise, 10-minute recovery, and 20-minute recovery phases in Tabata, HIIT(High-Intensity Interval Training), and MICT(Moderate-Intensity Continuous Training). This study explores the scientific aspects of weight reduction strategies, examining energy expenditure and substrate metabolism from various training perspectives. The aim is to establish a theoretical foundation for tailoring targeted exercise plans for individuals within the population with overweight/obesity. Methods: This study used an experimental design with fifteen male university students with overweight/obesity. Participants underwent random testing with Tabata, HIIT, and MICT. Tabata involved eight sets of 20 seconds exercise and 10 seconds rest, totaling 4 minutes. HIIT included four sets of power cycling: 3 minutes at 80% VO2max intensity followed by 2 minutes at 20% VO2max. MICT comprised 30 minutes of exercise at 50% VO2max intensity. Gas metabolism indices were continuously measured. Subsequently, fat and glucose oxidation rates, along with energy expenditure, were calculated for each exercise type. Results: During both the exercise and recovery phases, the Tabata group exhibited a significantly higher fat oxidation rate of (0.27 ± 0.03 g/min) compared to the HIIT group (0.20 ± 0.04 g/min, p<0.05) and the MICT group (0.20 ± 0.03g/min, p<0.001). No significant difference was observed between the HIIT and MICT groups (p=0.854). In terms of energy expenditure rate, the Tabata group maintained a substantially elevated level at 5.76 ± 0.74kcal/min compared to the HIIT group (4.81 ± 0.25kcal/min, p<0.01) and the MICT group (3.45 ± 0.25kcal/min, p<0.001). Additionally, the energy expenditure rate of the HIIT group surpassed that of the MICT group significantly (p<0.001). Conclusion: The study finds that male college students with overweight/obesity in both exercise and recovery, Tabata group has lower fat and glucose oxidation rates, and energy expenditure compared to HIIT and MICT groups. However, over the entire process, Tabata still exhibits significantly higher rates in these aspects than HIIT and MICT. Despite a shorter exercise duration, Tabata shows a noticeable "time-efficiency" advantage. Tabata can be used as an efficient short-term weight loss exercise program for male college students with overweight/obesity.

Effect of needle-free injection on psychological insulin resistance and insulin dosage in patients with type 2 diabetes.

Background and objective: Psychological insulin resistance (PIR), which refers to the reluctance of diabetic patients to use insulin, is a frequently encountered clinical issue. Needle-free injection (NFI) offers advantages in terms of expediting insulin absorption and mitigating adverse reactions related to injection. To evaluate the effects of subcutaneous injection of insulin aspart 30 with NFI on PIR and insulin dosage in patients with type 2 diabetes mellitus (T2DM). Methods: Sixty-four patients with T2DM participated in this randomized, prospective, open, crossover study. Insulin aspart 30 was administered subcutaneously to each subject via QS-P NFI and Novo Pen 5 (NP) successively. The effects of NFI on PIR were analyzed. Differences in insulin dosage, glycemic variability, and injection safety were compared at similar levels of glycemic control. Results: After the administration of NFI, the insulin treatment attitude scale score decreased (53.7 ± 7.3 vs. 58.9 ± 10.7, p<0.001), the insulin treatment adherence questionnaire score increased (46.3 ± 4.9 vs. 43.8 ± 7.1, p<0.001), and the insulin treatment satisfaction questionnaire score increased (66.6 ± 10.5 vs. 62.4 ± 16.5, p<0.001). At the same blood glucose level, NFI required a smaller dosage of insulin aspart 30 compared with that of NP (30.42 ± 8.70 vs. 33.66 ± 9.13 U/d, p<0.001). There were no differences in glycemic variability indices (standard deviation, mean amplitude of glycemic excursion or coefficient of variation) between the two injection methods. Compared with NP, NFI did not increase the incidence of hypoglycemia (17.2% vs. 14.1%, p=0.774), and it decreased the incidence of induration (4.7% vs. 23.4%, p=0.002) and leakage (6.3% vs. 20.3%, p=0.022) while decreasing the pain visual analog scale score (2.30 ± 1.58 vs. 3.11 ± 1.40, p<0.001). Conclusion: NFI can improve PIR in patients with T2DM and be used with a smaller dose of insulin aspart 30 while maintaining the same hypoglycemic effect. Clinical trial registration: https://www.chictr.org.cn/, identifier ChiCTR2400083658.

Causal association between major depressive disorder and venous thromboembolism: a bidirectional mendelian randomization study.

Purpose: Major depressive disorder (MDD) and venous thromboembolism (VTE) may be linked in observational studies. However, the causal association remains ambiguous. Therefore, this study investigates the causal associations between them. Methods: We performed a two-sample univariable and multivariable bidirectional Mendelian randomization (MR) analysis to evaluate the associations between MDD and VTE. The summary genetic associations of MDD statistics were obtained from the Psychiatric Genomics Consortium and UK Biobank. Information on VTE, deep vein thrombosis (DVT), and pulmonary embolism (PE) were obtained from the FinnGen Biobank. Inverse-variance weighting was used as the main analysis method. Other methods include weighted median, MR-Egger, Simple mode, and Weighted mode. Results: Univariable MR analysis revealed no significant associations between MDD and VTE risk (odds ratio (OR): 0.936, 95% confidence interval (CI): 0.736-1.190, p = 0.590); however, after adjusting the potential relevant polymorphisms of body mass index and education, the multivariable MR analysis showed suggestive evidence of association between them (OR: 1.163, 95% CI: 1.004-1.346, p = 0.044). Univariable MR analysis also revealed significant associations between MDD and PE risk (OR: 1.310, 95% CI: 1.073-1.598, p = 0.008), but the association between them was no longer significant in MVMR analysis (p = 0.072). We found no significant causal effects between MDD and DVT risk in univariable or multivariable MR analyses. There was also no clear evidence showing the causal effects between VTE, PE, or DVT and MDD risk. Conclusion: We provide suggestive genetic evidence to support the causal association between MDD and VTE risk. No causal associations were observed between VTE, PE, or DVT and MDD risk. Further validation of these associations and investigations of potential mechanisms are required.

A novel plant type, leaf disease and severity identification framework using CNN and transformer with multi-label method.

The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstrution.

Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning while maintaining image quality, which involves a consistent pursuit of lower incident rays and higher reconstruction performance. Although deep learning approaches have achieved encouraging success in LDCT reconstruction, most of them treat the task as a general inverse problem in either the image domain or the dual (sinogram and image) domains. Such frameworks have not considered the original noise generation of the projection data and suffer from limited performance improvement for the LDCT task. In this paper, we propose a novel reconstruction model based on noise-generating and imaging mechanism in full-domain, which fully considers the statistical properties of intrinsic noises in LDCT and prior information in sinogram and image domains. To solve the model, we propose an optimization algorithm based on the proximal gradient technique. Specifically, we derive the approximate solutions of the integer programming problem on the projection data theoretically. Instead of hand-crafting the sinogram and image regularizers, we propose to unroll the optimization algorithm to be a deep network. The network implicitly learns the proximal operators of sinogram and image regularizers with two deep neural networks, providing a more interpretable and effective reconstruction procedure. Numerical results demonstrate our proposed method improvements of > 2.9 dB in peak signal to noise ratio, > 1.4% promotion in structural similarity metric, and > 9 HU decrements in root mean square error over current state-of-the-art LDCT methods.

Facile Synthesis of Ag-Doped Urchin-like MnO2 on Carbon Cloth for Supercapacitors.

Based on MnO2/carbon cloth (CC) composite materials, an Ag-doped MnO2 nanowire, self-assembled, urchin-like structure was synthesized in situ on the surface of CC using a simple method, and a novel and efficient flexible electrode material for supercapacitors was developed. The morphology, structure, elemental distribution, and pore distribution of the material were analyzed using SEM, TEM, XRD, XPS, and BET. The electrochemical performance was tested using cyclic voltammetry (CV) and galvanostatic charge/discharge (GCD). In the three-electrode system, GCD testing showed that the specific capacitance of the material reached 520.8 F/g at 0.5 A/g. After 2000 cycles at a current density of 1 A/g, the capacitance retention rate was 90.6%, demonstrating its enormous potential in the application of supercapacitor electrode materials.

N-palmitoylethanolamide attenuates negative emotions induced by morphine withdrawal in mice.

Depression and anxiety are prominent symptoms of withdrawal syndrome, often caused by the abuse of addictive drugs like morphine. N-palmitoylethanolamide (PEA), a biologically active lipid, is utilized as an anti-inflammatory and analgesic medication. Recent studies have highlighted PEA's role in mitigating cognitive decline and easing depression resulting from chronic pain. However, it remains unknown whether PEA can influence negative emotions triggered by morphine withdrawal. This study seeks to explore the impact of PEA on such emotions and investigate the underlying mechanisms. Mice subjected to morphine treatment underwent a 10-day withdrawal period, followed by assessments of the effect of PEA on anxiety- and depression-like behaviors using various tests. Enzyme-linked immunosorbent assay was conducted to measure levels of monoamine neurotransmitters in specific brain regions. The findings indicate that PEA mitigated anxiety and depression symptoms and reduced 5-hydroxytryptamine, noradrenaline, and dopamine levels in the hippocampus and prefrontal cortex. In summary, PEA demonstrates a significant positive effect on negative emotions associated with morphine withdrawal, accompanied with the reduction in levels of monoamine neurotransmitters in key brain regions. These insights could be valuable for managing negative emotions arising from morphine withdrawal.

b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT.

Objective.Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance.Approach.Specifically, we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact-affected images. These extracted metal artifact representations are then bidirectionally embedded into both the metal artifact generator and the metal artifact eliminator, which can simultaneously improve the performance of artifact removal and artifact generation. The artifact eliminator learns artifact removal in a supervised manner, while the artifact generator learns artifact generation in an adversarial manner. To further improve the performance of the bidirectional task networks, we propose artifact consistency loss to align the consistency of images generated by the eliminator and the generator with or without embedding artifact representations.Main results.To validate the effectiveness of our algorithm, experiments are conducted on simulated and clinical datasets containing various dental metal morphologies. Quantitative metrics are calculated to evaluate the results of the simulation tests, which demonstrate b-MAR improvements of >1.4131 dB in PSNR, >0.3473 HU decrements in RMSE, and >0.0025 promotion in structural similarity index measurement over the current state-of-the-art MAR methods. All results indicate that the proposed b-MAR method can remove artifacts caused by various metal morphologies and restore the structural integrity of dental tissues effectively.Significance.The proposed b-MAR method strengthens the joint learning of the artifact removal process and the artifact generation process by bidirectionally embedding artifact representations, thereby improving the model's artifact removal performance. Compared with other comparison methods, b-MAR can robustly and effectively correct metal artifacts in dental CBCT images caused by different dental metals.

DDT-Net: Dose-Agnostic Dual-Task Transfer Network for Simultaneous Low-Dose CT Denoising and Simulation.

Deep learning (DL) algorithms have achieved unprecedented success in low-dose CT (LDCT) imaging and are expected to be a new generation of CT reconstruction technology. However, most DL-based denoising models often lack the ability to generalize to unseen dose data. Moreover, most simulation tools for LDCT typically operate on proprietary projection data, which is generally not accessible without an established collaboration with CT manufacturers. To alleviate these issues, in this work, we propose a dose-agnostic dual-task transfer network, termed DDT-Net, for simultaneous LDCT denoising and simulation. Concretely, the dual-task learning module is constructed to integrate the LDCT denoising and simulation tasks into a unified optimization framework by learning the joint distribution of LDCT and NDCT data. We approximate the joint distribution of continuous dose level data by training DDT-Net with discrete dose data, which can be generalized to denoising and simulation of unseen dose data. In particular, the mixed-dose training strategy adopted by DDT-Net can promote the denoising performance of lower-dose data. The paired dataset simulated by DDT-Net can be used for data augmentation to further restore the tissue texture of LDCT images. Experimental results on synthetic data and clinical data show that the proposed DDT-Net outperforms competing methods in terms of denoising and generalization performance at unseen dose data, and it also provides a simulation tool that can quickly simulate realistic LDCT images at arbitrary dose levels.

A fusion protein of vimentin with Fc fragment inhibits Japanese encephalitis virus replication.

Japanese encephalitis virus (JEV), a member of the Flaviviridae family and a flavivirus, is known to induce acute encephalitis. Vimentin protein has been identified as a potential receptor for JEV, engaging in interactions with the viral membrane protein. The Fc fragment, an integral constituent of immunoglobulins, plays a crucial role in antigen recognition by dendritic cells (DCs) or phagocytes, leading to subsequent antigen presentation, cytotoxicity, or phagocytosis. In this study, we fused the receptor of JEV vimentin with the Fc fragment of IgG and expressed the resulting vimentin-Fc fusion protein in Escherichia coli. Pull-down experiments demonstrated the binding ability of the vimentin-Fc fusion protein to JEV virion in vitro. Additionally, we conducted inhibition assays at the cellular level, revealing the ability of vimentin-Fc protein suppressing JEV replication, it may be a promising passive immunotherapy agent for JEV. These findings pave the way for potential therapeutic strategies against JEV.

Clinical biomarker-based biological aging and risk of benign prostatic hyperplasia: A large prospective cohort study.

Objective: Chronological age (CAge), biological age (BAge), and accelerated age (AAge) are all important for aging-related diseases. CAge is a known risk factor for benign prostatic hyperplasia (BPH); However, the evidence of association of BAge and AAge with BPH is limited. This study aimed to evaluate the association of CAge, Bage, and AAge with BPH in a large prospective cohort. Method: A total of 135,933 males without BPH at enrolment were extracted from the UK biobank. We calculated three BAge measures (Klemera-Doubal method, KDM; PhenoAge; homeostatic dysregulation, HD) based on 16 biomarkers. Additionally, we calculated KDM-BAge and PhenoAge-BAge measures based on the Levine method. The KDM-AAge and PhenoAge-AAge were assessed by the difference between CAge and BAge and were standardized (mean = 0 and standard deviation [SD] = 1). Cox proportional hazard models were applied to assess the associations of CAge, Bage, and AAge with incident BPH risk. Results: During a median follow-up of 13.150 years, 11,811 (8.690%) incident BPH were identified. Advanced CAge and BAge measures were associated with an increased risk of BPH, showing threshold effects at a later age (all P for nonlinearity <0.001). Nonlinear relationships between AAge measures and risk of BPH were also found for KDM-AAge (P = 0.041) and PhenoAge-AAge (P = 0.020). Compared to the balance comparison group (-1 SD < AAge < 1 SD), the accelerated aging group (AAge > 2 SD) had a significantly elevated BPH risk with hazard ratio (HR) of 1.115 (95% CI, 1.000-1.223) for KDM-AAge and 1.180 (95% CI, 1.068-1.303) for PhenoAge-AAge, respectively. For PhenoAge-AAge, subgroup analysis of the accelerated aging group showed an increased HR of 1.904 (95% CI, 1.374-2.639) in males with CAge <50 years and 1.233 (95% CI, 1.088-1.397) in those having testosterone levels <12 nmol/L. Moreover, AAge-associated risk of BPH was independent of and additive to genetic risk. Conclusions: Biological aging is an independent and modifiable risk factor for BPH. We suggest performing active health interventions to slow biological aging, which will help mitigate the progression of prostate aging and further reduce the burden of BPH.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

RNA splicing alterations in lung cancer pathogenesis and therapy.

RNA splicing alterations are widespread and play critical roles in cancer pathogenesis and therapy. Lung cancer is highly heterogeneous and causes the most cancer-related deaths worldwide. Large-scale multi-omics studies have not only characterized the mutational landscapes but also discovered a plethora of transcriptional and post-transcriptional changes in lung cancer. Such resources have greatly facilitated the development of new diagnostic markers and therapeutic options over the past two decades. Intriguingly, altered RNA splicing has emerged as an important molecular feature and therapeutic target of lung cancer. In this review, we provide a brief overview of splicing dysregulation in lung cancer and summarize the recent progress on key splicing events and splicing factors that contribute to lung cancer pathogenesis. Moreover, we describe the general strategies targeting splicing alterations in lung cancer and highlight the potential of combining splicing modulation with currently approved therapies to combat this deadly disease. This review provides new mechanistic and therapeutic insights into splicing dysregulation in cancer.

Efficacy and safety of sitagliptin added to metformin and insulin compared with voglibose in patients with newly diagnosed type 2 diabetes

OBJECTIVE: To assess the efficacy and safety of sitagliptin compared with voglibose added to combined metformin and insulin in patients with newly diagnosed type 2 diabetes (T2DM). METHODS: In this 12-week prospective, randomized, parallel trial, 70 newly diagnosed T2DM patients with glycosylated hemoglobin (HbA1c) ≥9% and/or fasting plasma glucose (FPG) ≥11.1 mmol/L were randomized (1:1) to receive sitagliptin 100 mg per day + metformin + insulin glargine or voglibose 0.2 mg three times daily + metformin + insulin glargine. Change in HbA1c at week 12 was the primary endpoint. RESULTS: The mean baseline HbA1c was 11.0% in the patients. The changes in HbA1c from baseline were -6.00% in the sitagliptin group and -3.58% in the voglibose group, and the between-group difference was -2.42% (95% CI -1.91 to -2.93, p=0.02). The differences in FPG and homeostatic model assessment of β-cell function (HOMA-β) and the change in body weight between groups from baseline were -2.95 mmol/L (p=0.04), 43.91 (p=0.01) and -2.23 kg (p=0.01), respectively. One patient (2.9%) in the sitagliptin group and three patients (8.6%) in the voglibose group exhibited hypoglycemia. CONCLUSIONS: Sitagliptin added to combined metformin and insulin therapy showed greater efficacy and good safety regarding hypoglycemia in patients with newly diagnosed T2DM compared with voglibose.