Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. OBJECTIVE: The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. METHODS: Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. RESULTS: A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non-logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. CONCLUSIONS: Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

Quercetin inhibits the proliferation of multiple myeloma cells by upregulating PTPRR expression.

Multiple myeloma (MM) is an incurable disease characterized by malignant plasma cell clonal expansion in the bone marrow; therefore, inhibiting the proliferation of plasma cells is an important approach to overcome the progression of MM. Quercetin (Que) is a promising flavonoid with broad-spectrum anti-tumor activity against various cancers, including MM; however, the underlying mechanism is not yet understood. The present study aimed to reveal the gene expression profile of Que-treated MM cells and clarify its potential mechanism. The 30% inhibitory concentration (IC30) of Que against MM cells was calculated, and the proliferation rate was significantly reduced after Que treatment. Next, 495 dysregulated genes were identified via RNA sequencing in Que-treated MM cells. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses indicated that the dysregulated genes were enriched in various apoptosis-related GO terms and amino acid metabolism-related pathways. qPCR validation showed that protein tyrosine phosphatase receptor-type R (PTPRR) had the highest verified log2 FC (abs) among the top 15 dysregulated genes. Overexpression of PTPRR increased the sensitivity of MM cells against Que, significantly inhibiting their proliferation and colony formation ability; silencing of PTPRR showed the opposite results. Furthermore, bioinformatics analyses and PPI network construction of PTPRR indicated that dephosphorylation of ERK might be the potential pathway for the PTPRR-induced inhibition of MM cell proliferation. In summary, our study identified the gene expression profile in Que-treated MM cells and demonstrated that the upregulation of PTPRR was one of the important mechanisms for the Que-induced inhibition of MM cell proliferation.

Anti-DLBCL efficacy of DCZ0825 in vitro and in vivo: involvement of the PI3K‒AKT‒mTOR/JNK pathway.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma, characterized by high heterogeneity. The poor outcome of a portion of patients who suffer relapsing or resistant to conventional treatment impels the development of novel agents for DLBCL. DCZ0825 is a novel compound derived from pterostilbene and osalmide, whose antitumor activities have drawn our attention. In this study, we found that DCZ0825 exhibited high cytotoxicity toward DLBCL cell lines in a dose- and time-dependent manner, as revealed by cell counting kit-8 assay. Flow cytometry and western blot analysis results showed that DCZ0825 also promoted cell apoptosis via both extrinsic and intrinsic apoptosis pathways mediated by caspase. In addition, DCZ0825 induced cell cycle arrest in the G2/M phase by downregulating Cdc25C, CDK1, and Cyclin B1, thus interfering with cell proliferation. Further investigation showed the involvement of the phosphatidylinositol 3-kinase (PI3K)‒AKT‒mTOR/JNK pathway in the efficacy of DCZ0825 against DLBCL. Remarkably, DCZ0825 also exerted notable cytotoxic effects in vivo as well, with low toxicity to important internal organs such as the liver and kidney. Our results suggest that DCZ0825 may have the potential to become a novel anti-DLBCL agent or to replenish the conventional therapeutic scheme of DLBCL.

The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers.

BACKGROUND AND OBJECTIVES: Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. METHODS: The dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such as random forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared. RESULTS: A total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions. CONCLUSION: The factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and  deserves further validation in clinical trials.

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

Deep Learning Methods for Heart Sounds Classification: A Systematic Review.

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

Novel cyclophosphamide of natural products osalmide and pterostilbene induces cytotoxicity and cell cycle arrest in diffuse large B-cell lymphoma cells.

Diffuse large B-cell lymphoma (DLBCL) is the most common category and disease entity of non-Hodgkin lymphoma. Osalmide and pterostilbene are natural products with anticancer activities via different mechanism. In this study, using a new synthetic strategy for the two natural products, we obtained the compound DCZ0801, which was previously found to have anti-multiple myeloma activity. We performed both in vitro and in vivo assays to investigate its bioactivity and explore its underlying mechanism against DLBCL cells. The results showed that DCZ0801 treatment gave rise to a dose- and time-dependent inhibition of cell viability as determined by CCK-8 assay and flow cytometry assay. Western blot analysis results showed that the expression of caspase-3, caspase-8, caspase-9 and Bax was increased, while BCL-2 and BCL-XL levels were decreased, which suggested that DCZ0801 inhibited cell proliferation and promoted intrinsic apoptosis. In addition, DCZ0801 induced G0/G1 phase arrest by downregulating the protein expression levels of CDK4, CDK6 and cyclin D1. Furthermore, DCZ0801 exerted an anti-tumor effect by down-regulating the expressions of p-PI3K and p-AKT. There also existed a trend that the expression of p-JNK and p-P38 was restrained. Intraperitoneal injection of DCZ0801 suppressed tumor development in xenograft mouse models. The preliminary metabolic study showed that DCZ0801 displayed a rapid metabolism within 30 min. These results demonstrated that DCZ0801 may be a new potential anti-DLBCL agent in DLBCL therapy.

DCZ0801, a novel compound, induces cell apoptosis and cell cycle arrest via MAPK pathway in multiple myeloma.

Multiple myeloma (MM) is a refractory malignant hematological malignancy, and many therapeutic strategies have been developed to cure patients with MM. DCZ0801 is a compound that consists of oxophenamide and pterostilbene. The role of these compounds in hematological cancers such as MM has yet to be studied. In this study, we explored the potential mechanism of DCZ0801 action, its anti-tumor activity both in vitro and in vivo on MM. This study was carried out via cell cycle proliferation assay, apoptotic analysis, western blot analysis, and examination of xenotransplantation model of tumors. The in vitro studies revealed that DCZ0801 could inhibit cell proliferation and induce apoptosis by regulating both caspase-dependent and mitogen-activated protein kinase signaling pathways, inducing S-phase arrest of the cell cycle related to downregulation of CDK2, cyclin-A2, and CDC25A protein expression. The in vivo studies showed that DCZ0801 could significantly reduce the size of the tumors in nude mice. Our results demonstrated that DCZ0801 may emerge as the new therapeutic option for the patient with MM.

Correlation between sex and efficacy of immune checkpoint inhibitors (PD-1 and CTLA-4 inhibitors).

Immune checkpoint inhibitors (ICIs) exert the antitumor efficacy depending on immune response, which is affected by sex difference, where both biological and sociological factors are involved. The role of sex in ICI trials has been overlooked. How sex correlates with ICI efficacy is incompletely understood. Clinical trials evaluating ICI versus other therapies in male and female patients were included. The hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS) and progression-free survival (PFS) were used. Six thousand and ninety-six patients from 11 trials were included. More improvement of OS was observed in males (HR, 0.62; 95% CI, 0.53-0.71; p < 0.001) treated with ICI versus controls than females (HR, 0.74; 95% CI, 0.65-0.84; p < 0.001). ICIs improved PFS more in males (HR, 0.57; 95% CI, 0.43-0.71; p < 0.001) than females (HR, 0.71; 95% CI, 0.52-0.91; p < 0.001). The sex difference had more effect on the overall survival in melanoma patients versus NSCLC patients. Overall survival of patients treated with CTLA-4 inhibitor was more influenced by sex variable compared with PD-1 inhibitors. A significant sex-related efficacy difference was observed between female and male melanoma patients. Although male patients had longer OS and PFS than females when treated with ICIs versus controls, the difference was not significant. Sex difference should be more considered in future clinical trials, guidelines and clinical practice.

DCZ3301, a novel aryl-guanidino inhibitor, induces cell apoptosis and cell cycle arrest via suppressing the PI3K/AKT pathway in T-cell leukemia/lymphoma.

DCZ3301, a novel aryl-guanidino compound, was previously found to have potent anti-tumor activity in myeloma and B-cell lymphoma. In the present study, we investigated the effects of DCZ3301 on T-cell leukemia/lymphoma cells both in vitro and in vivo via cell proliferation, cell cycle analysis, apoptosis assay, mitochondrial membrane potential (MMP) assay, western blot analysis and tumor xenograft models. We found that DCZ3301 inhibited the viability of T-cell leukemia/lymphoma cells in a dose- and time-dependent manner. DCZ3301-induced G2/M cell cycle arrest, associated with downregulation of CDK1, cyclin B1, and cdc25C. DCZ3301 also induced cell apoptosis by decreasing MMP in T-cell leukemia/lymphoma cells, but had no significant pro-apoptotic effect on normal peripheral blood mononuclear cells (PBMCs). In addition, DCZ3301-induced apoptosis may be mediated by the caspase-dependent pathway and suppressing the phosphoinositide 3-kinase (PI3K)/AKT pathway. Finally, we showed that DCZ3301 treatment effectively inhibited tumor growth, with no significant side effects, in xenograft mouse models. In conclusion, these results suggest that DCZ3301 may be regarded as a new therapeutic strategy for T-cell leukemia/lymphoma patients.

PKC inhibition of sotrastaurin has antitumor activity in diffuse large B-cell lymphoma via regulating the expression of MCT-1.

MCT-1 (multiple copies in T-cell lymphoma-1), a novel oncogene, was originally identified in T-cell lymphoma. A recent study has demonstrated that MCT-1 is highly expressed in 85% of diffuse large B-cell lymphomas (DLBCL). PKC (protein kinase C) plays an essential role in signal transduction for multiple biologically active substances for activating cellular functions and proliferation. In this study, we found that the mRNA and protein expression levels of MCT-1 were visibly decreased after knocking down PKC by siRNA in SUDHL-4 and OCI-LY8 DLBCL cell lines. A selective PKC inhibitor, sotrastaurin, effectively inhibited cell proliferation and induced cell apoptosis in a dose- and time-dependent manner. Meanwhile, we also observed that the cell cycle was arrested in the G1 phase in sotrastaurin-treated cells. In addition, MCT-1 was down-regulated in the sotrastaurin treatment group in vivo. Furthermore, we demonstrated that the PKC inhibitor sotrastaurin induced cell apoptosis and cell cycle arrest in DLBCL cells potentially through regulating the expression of MCT-1. Our data suggest that targeting PKC may be a potential therapeutic approach for lymphomas and related malignancies that exhibit high levels of MCT-1 protein.

iT2DMS: a Standard-Based Diabetic Disease Data Repository and its Pilot Experiment on Diabetic Retinopathy Phenotyping and Examination Results Integration.

Type 2 diabetes mellitus (T2DM) is a common chronic disease, and the fragment data collected through separated vendors makes continuous management of DM patients difficult. The lack of standard of fragment data from those diabetic patients also makes the further potential phenotyping based on the diabetic data difficult. Traditional T2DM data repository only supports data collection from T2DM patients, lack of phenotyping ability and relied on standalone database design, limiting the secondary usage of these valuable data. To solve these issues, we proposed a novel T2DM data repository framework, which was based on standards. This repository can integrate data from various sources. It would be used as a standardized record for further data transfer as well as integration. Phenotyping was conducted based on clinical guidelines with KNIME workflow. To evaluate the phenotyping performance of the proposed system, data was collected from local community by healthcare providers and was then tested using algorithms. The results indicated that the proposed system could detect DR cases with an average accuracy of about 82.8%. Furthermore, these results had the promising potential of addressing fragmented data. The proposed system has integrating and phenotyping abilities, which could be used for diabetes research in future studies.

The clinical value of combination of immune checkpoint inhibitors in cancer patients: A meta-analysis of efficacy and safety.

The use of immune checkpoint inhibitors (ICIs) in combination therapy is an emerging trend in tumor immunology. However, the value of combination immunotherapy remains controversial, because of the toxic effects induced by combination. The added benefit of each additional drug has not been assessed against the added toxicity. We searched for clinical trials that evaluated ICI monotherapies and combination therapies in lung cancer and melanoma patients. The overall response rate (ORR), grade 3/4 treatment-related adverse event rate, overall survival (OS), and progression-free survival (PFS) were extracted from the most recently published studies to determine the relative risk (RR), hazard ratios (HRs), and 95% confidence intervals (CIs). Seven randomized controlled trials and one open-label study were identified (n = 3,097). Treatments included combinations of several ICIs, a combination of an ICI and dacarbazine, two combinations of an ICI, paclitaxel and carboplatin, and a combination of an ICI and gp100 vaccine. Higher ORR (RR: 1.51, 95% CI: 1.03-2.20, p = 0.034), OS (HR: 0.86, 95% CI: 0.78-0.95, p = 0.000), and PFS (HR: 0.93, 95% CI: 0.72-1.14, p = 0.000) values were observed in combination therapy than in monotherapy. In addition, the toxicity of combination ICI immunotherapy was higher (RR: 1.50, 95% CI: 1.03-2.19, p = 0.036) than that of monotherapy. This meta-analysis showed that the addition of nivolumab to ipilimumab better benefits PFS and ORR. Adding sargramostim was associated with better OS and safety. The efficacy and safety of a nivolumab-ipilimumab-sargramostim combination should be investigated further.

Dihydrocelastrol inhibits multiple myeloma cell proliferation and promotes apoptosis through ERK1/2 and IL-6/STAT3 pathways in vitro and in vivo.

Multiple myeloma (MM) is the second most frequent malignant hematological disease. Dihydrocelastrol (DHCE) is synthesized by hydrogenated celastrol, a treterpene isolated from Chinese medicinal plant Tripterygium regelii. In this study, we first reported the anti-tumor activity of DHCE on MM cells. We found that DHCE could inhibit cell proliferation and promote apoptosis through caspase-dependent way in vitro. In addition, DHCE could inactivate the expression of interleukin (IL)-6 and downregulate the phosphorylation of extracellular regulated protein kinases (ERK1/2) and the signal transducer and activator of transcription 3 (STAT3) in MM. It also retained its activity against MM cell lines in the presence of IL-6. Furthermore, treatment of MM cells with DHCE resulted in an accumulation of cells in G0/G1 phase of the cell cycle. Notably, DHCE reduced the expression of cyclin D1 and cyclin-dependent kinases 4 and 6 in MM cell lines. Additionally, its efficacy toward the MM cell lines could be enhanced in combination with the histone deacetylase inhibitor panobinostat (LBH589), which implied the possibility of the combination treatment of DHCE and LBH589 as a potential therapeutic strategy in MM. In addition, treatment of NCI-H929 tumor-bearing nude mice with DHCE (10 mg/kg/d, i.p., 1-14 days) resulted in 73% inhibition of the tumor growth in vivo. Taken together, the results of our present study indicated that DHCE could inhibit cellular proliferation and induce cell apoptosis in myeloma cells mediated through different mechanisms, possibly through inhibiting the IL-6/STAT3 and ERK1/2 pathways. And it may provide a new therapeutic option for MM patients.

FGF2 regulates proliferation, migration, and invasion of ECA109 cells through PI3K/Akt signalling pathway in vitro.

Esophageal cancer is one of the most common malignant cancers that arise from esophagus tissues. Fibroblast growth factor 2 (FGF2) has been implicated in multiple biological functions and was considered as an oncogenic factor in tumorigenesis. However, the effects of FGF2 in esophageal carcinoma are yet to be fully elucidated. To better understand the function of FGF2 in esophageal cancer, we used the esophageal cancer cell line ECA109 as a cell model and downregulated FGF2 expression using RNAi; the results showed that insufficient expression of FGF2 inhibited cells proliferation, migration, and invasion of ECA109 cells. Meanwhile, the proliferation, migration, and invasion abilities were stimulated after treatment of exogenous FGF2. In addition, a PI3K/Akt signalling pathway inhibitor (LY294002) alleviated the tumorigenic effects of FGF2. These findings implied that the oncogenic effects of FGF2 was mediated, at least in part, through the PI3K/Akt signalling pathway and FGF2 may be a potential therapeutic target to constrain the tumorigenesis of esophageal cancer.

Pterostilbene Inhibits Human Multiple Myeloma Cells via ERK1/2 and JNK Pathway In Vitro and In Vivo.

Multiple myeloma (MM) is the second most common malignancy in the hematologic system, which is characterized by accumulation of plasma cells in bone marrow. Pterostilbene (PTE) is a natural dimethylated analog of resveratrol, which has anti-oxidant, anti-inflammatory and anti-tumor properties. In the present study, we examined the anti-tumor effect of PTE on MM cell lines both in vitro and in vivo using the cell counting kit (CCK)-8, apoptosis assays, cell cycle analysis, reactive oxygen species (ROS) generation, JC-1 mitochondrial membrane potential assay, Western blotting and tumor xenograft models. The results demonstrated that PTE induces apoptosis in the H929 cell line and causes cell cycle arrest at G0/G1 phase by enhancing ROS generation and reducing mitochondrial membrane potential. The anti-tumor effect of PTE may be caused by the activation of the extracellular regulated protein kinases (ERK) 1/2 and c-Jun N-terminal kinase (JNK) signaling pathways. Additionally, mice treated with PTE by intraperitoneal injection demonstrated reduced tumor volume. Taken together, the results of this study indicate that the anti-tumor effect of PTE on MM cells may provide a new therapeutic option for MM patients.

[Study on Information Extraction of Clinic Expert Information from Hospital Portals].

Clinic expert information provides important references for residents in need of hospital care. Usually, such information is hidden in the deep web and cannot be directly indexed by search engines. To extract clinic expert information from the deep web, the first challenge is to make a judgment on forms. This paper proposes a novel method based on a domain model, which is a tree structure constructed by the attributes of search interfaces. With this model, search interfaces can be classified to a domain and filled in with domain keywords. Another challenge is to extract information from the returned web pages indexed by search interfaces. To filter the noise information on a web page, a block importance model is proposed. The experiment results indicated that the domain model yielded a precision 10.83% higher than that of the rule-based method, whereas the block importance model yielded an F1 measure 10.5% higher than that of the XPath method.

Computer aided quantification for retinal lesions in patients with moderate and severe non-proliferative diabetic retinopathy: a retrospective cohort study.

BACKGROUND: Detection of retinal lesions like micro-aneurysms and exudates are important for the clinical diagnosis of diabetes retinopathy. The traditional subjective judgments by clinicians are dependent on their experience and can be subject to lack of consistency and therefore a quantification method is worthwhile. METHODS: In this study, 10 moderate non-proliferative diabetes retinopathy (NPDR) patients and 10 severe NPDR ones were retrospectively selected as a cohort. Mathematical morphological methods were used for automatic segmentation of lesions. For exudates detection, images were pre-processed with adaptive histogram equalization to enhance contrast, then binary images for area calculation were obtained by threshold classification. For micro-aneurysms detection, the images were pre-processed by top-hat and bottom-hat transformation, then Otsu method and Hough transform were used to classify micro-aneurysms. Post-processing morphological methods were used to preclude the false positive noise. RESULTS: After segmentation, the area of exuduates divided by optic disk area (exudates/disk ratio) and counts of microaneurysms were quantified and compared between the moderate and severe non-proliferative diabetic retinopathy groups, which had significant difference(P < 0.05). CONCLUSIONS: In conclusion, morphological features of lesion might be an image marker for NPDR grading and computer aided quantification of retinal lesion could be a practical way for clinicians to better investigates diabetic retinopathy.

Proteasome inhibitor carfilzomib interacts synergistically with histone deacetylase inhibitor vorinostat in Jurkat T-leukemia cells.

In the present study, we investigated the interactions between proteasome inhibitor carfilzomib (CFZ) and histone deacetylase inhibitor vorinostat in Jurkat T-leukemia cells. Coexposure of cells to minimally lethal concentrations of CFZ with very low concentration of vorinostat resulted in synergistic antiproliferative effects and enhanced apoptosis in Jurkat T-leukemia cells, accompanied with the sharply increased reactive oxygen species (ROS), the striking decrease in the mitochondrial membrane potential (MMP), the increased release of cytochrome c, the enhanced activation of caspase-9 and -3, and the cleavage of PARP. The combined treatment of Jurkat cells pre-treated with ROS scavengers N-acetylcysteine (NAC) significantly blocked the loss of mitochondrial membrane potential, suggesting that ROS generation was a former event of the loss of mitochondrial membrane potential. Furthermore, NAC also resulted in a marked reduction in apoptotic cells, indicating a critical role for increased ROS generation by combined treatment. In addition, combined treatment arrested the cell cycle in G2-M phase. These results imply that CFZ interacted synergistically with vorinostat in Jurkat T-leukemia cells, which raised the possibility that the combination of carfilzomib with vorinostat may represent a novel strategy in treating T-cell Leukemia.

Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.

Objectives: The aim of this study was to systematically review the studies on radiomics models in distinguishing between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and evaluate the classification performance of radiomics models using images from various imaging techniques. Materials and methods: PubMed, Embase and Web of Science Core Collection were utilized to search for radiomics studies that differentiate between LUAD and LUSC. The assessment of the quality of studies included utilized the improved Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS). Meta-analysis was conducted to assess the classification performance of radiomics models using various imaging techniques. Results: The qualitative analysis included 40 studies, while the quantitative synthesis included 21 studies. Median RQS for 40 studies was 12 (range -5~19). Sixteen studies were deemed to have a low risk of bias and low concerns regarding applicability. The radiomics model based on CT images had a pooled sensitivity of 0.78 (95%CI: 0.71~0.83), specificity of 0.85 (95%CI:0.73~0.92), and the area under summary receiver operating characteristic curve (SROC-AUC) of 0.86 (95%CI:0.82~0.89). As for PET images, the pooled sensitivity was 0.80 (95%CI: 0.61~0.91), specificity was 0.77 (95%CI: 0.60~0.88), and the SROC-AUC was 0.85 (95%CI: 0.82~0.88). PET/CT images had a pooled sensitivity of 0.87 (95%CI: 0.72~0.94), specificity of 0.88 (95%CI: 0.80~0.93), and an SROC-AUC of 0.93 (95%CI: 0.91~0.95). MRI images had a pooled sensitivity of 0.73 (95%CI: 0.61~0.82), specificity of 0.80 (95%CI: 0.65~0.90), and an SROC-AUC of 0.79 (95%CI: 0.75~0.82). Conclusion: Radiomics models demonstrate potential in distinguishing between LUAD and LUSC. Nevertheless, it is crucial to conduct a well-designed and powered prospective radiomics studies to establish their credibility in clinical application. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=412851, identifier CRD42023412851.