bluuebunny/crossref_metadata_2025 huggingface dataset:
Mesial-Temporal Epileptic Ripples Correlate With Verbal Memory Impairment
Authors: Bruder Jonas Christian , Wagner Kathrin , Lachner-Piza Daniel , Klotz Kerstin Alexandra , Schulze-Bonhage Andreas , Jacobs Julia
Published: June 2022

Rationale

High frequency oscillations (HFO; ripples = 80–200, fast ripples 200–500 Hz) are promising epileptic biomarkers in patients with epilepsy. However, especially in temporal epilepsies differentiation of epileptic and physiological HFO activity still remains a challenge. Physiological sleep-spindle-ripple formations are known to play a role in slow-wave-sleep memory consolidation. This study aimed to find out if higher rates of mesial-temporal spindle-ripples correlate with good memory performance in epilepsy patients and if surgical removal of spindle-ripple-generating brain tissue correlates with a decline in memory performance. In contrast, we hypothesized that higher rates of overall ripples or ripples associated with interictal epileptic spikes correlate with poor memory performance.

Methods

Patients with epilepsy implanted with electrodes in mesial-temporal structures, neuropsychological memory testing and subsequent epilepsy surgery were included. Ripples and epileptic spikes were automatically detected in intracranial EEG and sleep-spindles in scalp EEG. The coupling of ripples to spindles was automatically analyzed. Mesial-temporal spindle-ripple rates in the speech-dominant-hemisphere (left in all patients) were correlated with verbal memory test results, whereas ripple rates in the non-speech-dominant hemisphere were correlated with non-verbal memory test performance, using Spearman correlation).

Results

Intracranial EEG and memory test results from 25 patients could be included. All ripple rates were significantly higher in seizure onset zone channels (p < 0.001). Patients with pre-surgical verbal memory impairment had significantly higher overall ripple rates in left mesial-temporal channels than patients with intact verbal memory (Mann–Whitney-U-Test: p = 0.039). Spearman correlations showed highly significant negative correlations of the pre-surgical verbal memory performance with left mesial-temporal spike associated ripples (rs = −0.458; p = 0.007) and overall ripples (rs = −0.475; p = 0.006). All three ripple types in right-sided mesial-temporal channels did not correlate with pre-surgical nonverbal memory. No correlation for the difference between post- and pre-surgical memory and pre-surgical spindle-ripple rates was seen in patients with left-sided temporal or mesial-temporal surgery.

Discussion

This study fails to establish a clear link between memory performance and spindle ripples. This highly suggests that spindle-ripples are only a small portion of physiological ripples contributing to memory performance. More importantly, this study indicates that spindle-ripples do not necessarily compromise the predictive value of ripples in patients with temporal epilepsy. The majority of ripples were clearly linked to areas with poor memory function.
Comatose Patients After Cardiopulmonary Resuscitation: An Analysis Based on Quantitative Methods of EEG Reactivity
Authors: Huang Huijin , Su Yingying , Niu Zikang , Liu Gang , Li Xiaoli , Jiang Mengdi
Published: June 2022

Objective

Every year, approximately 50–110/1,00,000 people worldwide suffer from cardiac arrest, followed by hypoxic-ischemic encephalopathy after cardiopulmonary resuscitation (CPR), and approximately 40–66% of patients do not recover. The purpose of this study was to identify the brain network parameters and key brain regions associated with awakening by comparing the reactivity characteristics of the brain networks between the awakening and unawakening groups of CPR patients after coma, thereby providing a basis for further awakening interventions.

Method

This study involved a prospective cohort study. Using a 64-electrode electroencephalography (EEG) wireless 64A system, EEG signals were recorded from 16 comatose patients after CPR in the acute phase (<1 month) from 2019 to 2020. MATLAB (2017b) was used to quantitatively analyze the reactivity (power spectrum and entropy) and brain network characteristics (coherence and phase lag index) after pain stimulation. The patients were divided into an awakening group and an unawakening group based on their ability to execute commands or engage in repeated and continuous purposeful behavior after 3 months. The above parameters were compared to determine whether there were differences between the two groups.

Results

(1) Power spectrum: the awakening group had higher gamma, beta and alpha spectral power after pain stimulation in the frontal and parietal lobes, and lower delta and theta spectral power in the bilateral temporal and occipital lobes than the unawakening group. (2) Entropy: after pain stimulation, the awakening group had higher entropy in the frontal and parietal lobes and lower entropy in the temporal occipital lobes than the unawakening group. (3) Connectivity: after pain stimulation, the awakening group had stronger gamma and beta connectivity in nearly the whole brain, but weaker theta and delta connectivity in some brain regions (e.g., the frontal-occipital lobe and parietal-occipital lobe) than the unawakening group.

Conclusion

After CPR, comatose patients were more likely to awaken if there was a higher stimulation of fast-frequency band spectral power, higher entropy, stronger whole-brain connectivity and better retention of frontal-parietal lobe function after pain stimulation.
Impact of Body Temperature in Patients With Acute Basilar Artery Occlusion: Analysis of the BASILAR Database
Authors: Zhang Wenbin , Li Fengli , Zhang Cai , Lei Bo , Deng Wei , Zeng Hongliang , Yu Yang , Wu Junxiong , Peng Daizhou , Tian Zhenxuan , Zhu Xiurong , Hu Zhizhou , Hong Yifan , Li Wenbo , Ge Hanming , Xu Xinwei , Ju Dongsheng , Yang Shunyu , Pan Chengde , Zi Wenjie , Wang Shouchun
Published: June 2022

Background

A link between body temperature and stroke outcomes has been established but not for acute basilar artery occlusion. We aimed to determine the association between body temperature and clinical outcomes in patients with acute basilar artery occlusion and temperature management range.

Methods

We included patients from the Endovascular Treatment for Acute Basilar Artery Occlusion Study (BASILAR) database with records of both admission body temperature (ABT) and peak body temperature (PBT). ABT was defined as the body temperature first measured at the hospital visit, PBT was defined as the highest temperature within 24 h of treatment, and minus body temperature (MBT) was defined as PBT-ABT. The primary clinical outcome was favorable functional outcome, defined as the proportion of patients with a modified Rankin Scale score of 0–3 at 3 months. Secondary outcomes included 3-month mortality, in-hospital mortality, and symptomatic cerebral hemorrhage.

Results

A total of 664 patients were enrolled in the study; 74.7% were men, with a median age of 65 (interquartile range, 57.25–74) years. In all patients, multivariate analysis indicated that PBT and MBT were independent predictors of favorable functional outcome [odds ratio (OR), 0.57 (95% CI, 0.43–0.77); OR, 0.68 (95% CI, 0.52–0.88), respectively], and higher ABT, PBT, and MBT were associated with an increased 3-month mortality [OR, 1.47 (95% CI, 1.03–2.10), OR, 1.58 (95% CI, 1.28–1.96), OR, 1.35 (95% CI, 1.11–1.65), respectively]. Proportional odds models demonstrated that when ABT, PBT, MBT were in the range of <37.5, <38.9, and −0.6–2.7°C, respectively, the benefit of the endovascular treatment is clearly greater than that of standard medical treatment in terms of favorable functional outcome.

Conclusions

Body temperature is an independent predictor of clinical outcome in patients with acute basilar artery occlusion. It is necessary to control the patient body temperature within the appropriate range in clinical settings.

Trial Registration

Chinese Clinical Trial Registry ChiCTR1800014759. Registered 03 February 2018. Retrospectively registered.
Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach
Authors: Steele Katherine M. , Schwartz Michael H.
Published: June 2022

Background

Altered motor control is common in cerebral palsy (CP). Understanding how altered motor control affects movement and treatment outcomes is important but challenging due to complex interactions with other neuromuscular impairments. While regression can be used to examine associations between impairments and movement, causal modeling provides a mathematical framework to specify assumed causal relationships, identify covariates that may introduce bias, and test model plausibility. The goal of this research was to quantify the causal effects of altered motor control and other impairments on gait, before and after single-event multi-level orthopedic surgery (SEMLS).

Methods

We evaluated the impact of SEMLS on change in Gait Deviation Index (ΔGDI) between gait analyses. We constructed our causal model with a Directed Acyclic Graph that included the assumed causal relationships between SEMLS, ΔGDI, baseline GDI (GDIpre), baseline neurologic and orthopedic impairments (Imppre), age, and surgical history. We identified the adjustment set to evaluate the causal effect of SEMLS on ΔGDI and the impact of Imppre on ΔGDI and GDIpre. We used Bayesian Additive Regression Trees (BART) and accumulated local effects to assess relative effects.

Results

We prospectively recruited a cohort of children with bilateral CP undergoing SEMLS (N = 55, 35 males, age: 10.5 ± 3.1 years) and identified a control cohort with bilateral CP who did not undergo SEMLS (N = 55, 30 males, age: 10.0 ± 3.4 years). There was a small positive causal effect of SEMLS on ΔGDI (1.70 GDI points). Altered motor control (i.e., dynamic and static motor control) and strength had strong effects on GDIpre, but minimal effects on ΔGDI. Spasticity and orthopedic impairments had minimal effects on GDIpre or ΔGDI.

Conclusion

Altered motor control did have a strong effect on GDIpre, indicating that these impairments do have a causal effect on a child’s gait pattern, but minimal effect on expected changes in GDI after SEMLS. Heterogeneity in outcomes suggests there are other factors contributing to changes in gait. Identifying these factors and employing causal methods to examine the complex relationships between impairments and movement will be required to advance our understanding and care of children with CP.
A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification
Authors: Prabhakar Sunil Kumar , Rajaguru Harikumar , Kim Chulho , Won Dong-Ok
Published: June 2022

The vital data about the electrical activities of the brain are carried by the electroencephalography (EEG) signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG studies incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model (FHM) with Singular Value Decomposition (SVD) Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee (HDPAB) algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model (ZIPMRM) and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset.
Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement
Authors: Han Mo , Zandigohar Mehrshad , Günay Sezen Yağmur , Schirner Gunar , Erdoğmuş Deniz
Published: June 2022

Electromyography (EMG) data has been extensively adopted as an intuitive interface for instructing human-robot collaboration. A major challenge to the real-time detection of human grasp intent is the identification of dynamic EMG from hand movements. Previous studies predominantly implemented the steady-state EMG classification with a small number of grasp patterns in dynamic situations, which are insufficient to generate differentiated control regarding the variation of muscular activity in practice. In order to better detect dynamic movements, more EMG variability could be integrated into the model. However, only limited research was conducted on such detection of dynamic grasp motions, and most existing assessments on non-static EMG classification either require supervised ground-truth timestamps of the movement status or only contain limited kinematic variations. In this study, we propose a framework for classifying dynamic EMG signals into gestures and examine the impact of different movement phases, using an unsupervised method to segment and label the action transitions. We collected and utilized data from large gesture vocabularies with multiple dynamic actions to encode the transitions from one grasp intent to another based on natural sequences of human grasp movements. The classifier for identifying the gesture label was constructed afterward based on the dynamic EMG signal, with no supervised annotation of kinematic movements required. Finally, we evaluated the performances of several training strategies using EMG data from different movement phases and explored the information revealed from each phase. All experiments were evaluated in a real-time style with the performance transitions presented over time.
Identification of Auxiliary Biomarkers and Description of the Immune Microenvironmental Characteristics in Duchenne Muscular Dystrophy by Bioinformatical Analysis and Experiment
Authors: Han Xu , Han Jingzhe , Wang Ning , Ji Guang , Guo Ruoyi , Li Jing , Wu Hongran , Ma Shaojuan , Fang Pingping , Song Xueqin
Published: June 2022

Background

Duchenne muscular dystrophy (DMD) is a genetic muscle disorder characterized by progressive muscle wasting associated with persistent inflammation. In this study, we aimed to identify auxiliary biomarkers and further characterize the immune microenvironment in DMD.

Methods

Differentially expressed genes (DEGs) were identified between DMD and normal muscle tissues based on Gene Expression Omnibus (GEO) datasets. Bioinformatical analysis was used to screen and identify potential diagnostic signatures of DMD which were further validated by real-time quantitative reverse transcription PCR (RT-qPCR). We also performed single-sample gene-set enrichment analysis (ssGSEA) to characterize the proportion of tissue-infiltrating immune cells to determine the inflammatory state of DMD.

Results

In total, 182 downregulated genes and 263 upregulated genes were identified in DMD. C3, SPP1, TMSB10, TYROBP were regarded as adjunct biomarkers and successfully validated by RT-qPCR. The infiltration of macrophages, CD4+, and CD8+ T cells was significantly higher (p < 0.05) in DMD compared with normal muscle tissues, while the infiltration of activated B cells, CD56dim natural killer cells, and type 17 T helper (Th17) cells was lower. In addition, the four biomarkers (C3, SPP1, TMSB10, TYROBP) were strongly associated with immune cells and immune-related pathways in DMD muscle tissues.

Conclusion

Analyses demonstrated C3, SPP1, TMSB10, and TYROBP may serve as biomarkers and enhance our understanding of immune responses in DMD. The infiltration of immune cells into the muscle microenvironment might exert a critical impact on the development and occurrence of DMD.
Association Between Nap Duration and Cognitive Functions Among Saudi Older Adults
Authors: Alqurashi Yousef D. , AlHarkan Khalid , Aldhawyan Adam , Bahamdan Ahmed , Alabdulkader Assim , Alotaibi Raed , Alduailej Saleh , Alqahtani Mohammed , Ghamdi Kholoud Al
Published: June 2022

Purpose

Current evidence of whether napping promotes or declines cognitive functions among older adults is contradictory. The aim of this study was to determine the association between nap duration and cognitive functions among Saudi older adults.

Methods

Old adults (> 60 years) were identified from the Covid-19 vaccine center at Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia between May and August 2021. Face-to-face interviews were conducted by a geriatrician or family physicians. Data collected for each participant included sociodemographic, sleep patterns, health status and cognitive functions. St. Louis University mental status (SLUMS) was used to assess the cognitive functions. A multi-Linear regression model was used to determine the association between cognitive functions and nap duration.

Results

Two-hundred participants (58 females) aged 66 ± 5 years were recruited. Participants were categorized according to their nap duration into non-nappers (0 min), short nappers (> 0- ≤ 30 min), moderate nappers (> 30–≤ 90 min), and extended nappers (> 90 min). The mean duration of the nap was 49.1 ± 58.4 min. The mean SLUMS score was 24.1 ± 4.7 units. Using the multi-linear regression model, the mean total SLUMS score for extended nappers was, on average, significantly lower than non-nappers [−2.16 units; 95% CI (−3.66, −0.66), p = < 0.01] after controlling for the covariates (age, sex, education level, sleep hours, diabetes mellitus, hypertension, pain).

Conclusions

Extended napping was associated with deterioration in cognitive function among Saudi older adults.
A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma
Authors: Liu Yuchen , Han Yanxun , Chen Bangjie , Zhang Jian , Yin Siyue , Li Dapeng , Wu Yu , Jiang Yuan , Wang Xinyi , Wang Jianpeng , Fu Ziyue , Shen Hailong , Ding Zhao , Yao Kun , Tao Ye , Wu Jing , Liu Yehai
Published: June 2022

Background

Laryngeal squamous cell carcinoma (LSCC) is the most common type of head and neck squamous cell carcinoma. However, there are currently no reliable biomarkers for the diagnosis and prognosis of LSCC. Thus, this study aimed to identify the independent risk factors and develop and validate a new dynamic web-based nomogram that can predict auxiliary laryngeal carcinogenesis.

Methods

Data on the medical history of 221 patients who were recently diagnosed with LSCC and 359 who were recently diagnosed with benign laryngeal lesions (BLLs) at the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed. Using the bootstrap method, 580 patients were divided in a 7:3 ratio into a training cohort (LSCC, 158 patients; BLL, 250 patients) and an internal validation cohort (LSCC, 63 patients; BLL, 109 patients). In addition, a retrospective analysis of 31 patients with LSCC and 54 patients with BLL from Fuyang Hospital affiliated with Anhui Medical University was performed as an external validation cohort. In the training cohort, the relevant indices were initially screened using univariate analysis. Then, least absolute shrinkage and selection operator logistic analysis was used to evaluate the significant potential independent risk factors (P<0.05); a dynamic online diagnostic nomogram, whose discrimination was evaluated using the area under the ROC curve (AUC), was constructed, while the consistency was evaluated using calibration plots. Its clinical application was evaluated by performing a decision curve analysis (DCA) and validated by internal validation of the training set and external validation of the validation set.

Results

Five independent risk factors, sex (odds ratio [OR]: 6.779, P<0.001), age (OR: 9.257, P<0.001), smoking (OR: 2.321, P=0.005), red blood cell width distribution (OR: 2.698, P=0.001), albumin (OR: 0.487, P=0.012), were screened from the results of the multivariate logistic analysis of the training cohort and included in the LSCC diagnostic nomogram. The nomogram predicted LSCC with AUC values of 0.894 in the training cohort, 0.907 in the internal testing cohort, and 0.966 in the external validation cohort. The calibration curve also proved that the nomogram predicted outcomes were close to the ideal curve, the predicted outcomes were consistent with the real outcomes, and the DCA curve showed that all patients could benefit. This finding was also confirmed in the validation cohort.

Conclusion

An online nomogram for LSCC was constructed with good predictive performance, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors and assist physicians in making a personalized diagnosis and treatment for patients.
Potential of Mitochondrial Ribosomal Genes as Cancer Biomarkers Demonstrated by Bioinformatics Results
Authors: Bao Shunchao , Wang Xinyu , Li Mo , Gao Zhao , Zheng Dongdong , Shen Dihan , Liu Linlin
Published: June 2022

Next-generation sequencing and bioinformatics analyses have clearly revealed the roles of mitochondrial ribosomal genes in cancer development. Mitochondrial ribosomes are composed of three RNA components encoded by mitochondrial DNA and 82 specific protein components encoded by nuclear DNA. They synthesize mitochondrial inner membrane oxidative phosphorylation (OXPHOS)-related proteins and participate in various biological activities via the regulation of energy metabolism and apoptosis. Mitochondrial ribosomal genes are strongly associated with clinical features such as prognosis and foci metastasis in patients with cancer. Accordingly, mitochondrial ribosomes have become an important focus of cancer research. We review recent advances in bioinformatics research that have explored the link between mitochondrial ribosomes and cancer, with a focus on the potential of mitochondrial ribosomal genes as biomarkers in cancer.
Application of Multi-Scale Fusion Attention U-Net to Segment the Thyroid Gland on Localized Computed Tomography Images for Radiotherapy
Authors: Wen Xiaobo , Zhao Biao , Yuan Meifang , Li Jinzhi , Sun Mengzhen , Ma Lishuang , Sun Chaoxi , Yang Yi
Published: June 2022

Objective

To explore the performance of Multi-scale Fusion Attention U-Net (MSFA-U-Net) in thyroid gland segmentation on localized computed tomography (CT) images for radiotherapy.

Methods

We selected localized radiotherapeutic CT images from 80 patients with breast cancer or head and neck tumors; label images were manually delineated by experienced radiologists. The data set was randomly divided into the training set (n = 60), the validation set (n = 10), and the test set (n = 10). We expanded the data in the training set and evaluated the performance of the MSFA-U-Net model using the evaluation indices Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), positive predictive value (PPV), sensitivity (SE), and Hausdorff distance (HD).

Results

For the MSFA-U-Net model, the DSC, JSC, PPV, SE, and HD values of the segmented thyroid gland in the test set were 0.90 ± 0.09, 0.82± 0.11, 0.91 ± 0.09, 0.90 ± 0.11, and 2.39 ± 0.54, respectively. Compared with U-Net, HRNet, and Attention U-Net, MSFA-U-Net increased DSC by 0.04, 0.06, and 0.04, respectively; increased JSC by 0.05, 0.08, and 0.04, respectively; increased SE by 0.04, 0.11, and 0.09, respectively; and reduced HD by 0.21, 0.20, and 0.06, respectively. The test set image results showed that the thyroid edges segmented by the MSFA-U-Net model were closer to the standard thyroid edges delineated by the experts than were those segmented by the other three models. Moreover, the edges were smoother, over–anti-noise interference was stronger, and oversegmentation and undersegmentation were reduced.

Conclusion

The MSFA-U-Net model could meet basic clinical requirements and improve the efficiency of physicians’ clinical work.
Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image
Authors: Shen Wen-Qian , Guo Yanhui , Ru Wan-Er , Li Cheukfai , Zhang Guo-Chun , Liao Ning , Du Guo-Qing
Published: June 2022

Background

The detection of phosphatidylinositol-3 kinase catalytic alpha (PIK3CA) gene mutations in breast cancer is a key step to design personalizing an optimal treatment strategy. Traditional genetic testing methods are invasive and time-consuming. It is urgent to find a non-invasive method to estimate the PIK3CA mutation status. Ultrasound (US), one of the most common methods for breast cancer screening, has the advantages of being non-invasive, fast imaging, and inexpensive. In this study, we propose to develop a deep convolutional neural network (DCNN) to identify PIK3CA mutations in breast cancer based on US images.

Materials and Methods

We retrospectively collected 312 patients with pathologically confirmed breast cancer who underwent genetic testing. All US images (n=800) of breast cancer patients were collected and divided into the training set (n=600) and test set (n=200). A DCNN-Improved Residual Network (ImResNet) was designed to identify the PIK3CA mutations. We also compared the ImResNet model with the original ResNet50 model, classical machine learning models, and other deep learning models.

Results

The proposed ImResNet model has the ability to identify PIK3CA mutations in breast cancer based on US images. Notably, our ImResNet model outperforms the original ResNet50, DenseNet201, Xception, MobileNetv2, and two machine learning models (SVM and KNN), with an average area under the curve (AUC) of 0.775. Moreover, the overall accuracy, average precision, recall rate, and F1-score of the ImResNet model achieved 74.50%, 74.17%, 73.35%, and 73.76%, respectively. All of these measures were significantly higher than other models.

Conclusion

The ImResNet model gives an encouraging performance in predicting PIK3CA mutations based on breast US images, providing a new method for noninvasive gene prediction. In addition, this model could provide the basis for clinical adjustments and precision treatment.
Investigating the Mechanism of Inhibition of Cyclin-Dependent Kinase 6 Inhibitory Potential by Selonsertib: Newer Insights Into Drug Repurposing
Authors: Baig Mohammad Hassan , Yousuf Mohd. , Khan Mohd. Imran , Khan Imran , Ahmad Irfan , Alshahrani Mohammad Y. , Hassan Md. Imtaiyaz , Dong Jae-June
Published: June 2022

Cyclin-dependent kinases (CDKs) play significant roles in numerous physiological, and are considered an attractive drug target for cancer, neurodegenerative, and inflammatory diseases. In the present study, we have aimed to investigate the binding affinity and inhibitory potential of selonsertib toward CDK6. Using the drug repurposing approach, we performed molecular docking of selonsertib with CDK6 and observed a significant binding affinity. To ascertain, we further performed essential dynamics analysis and free energy calculation, which suggested the formation of a stable selonsertib-CDK6 complex. The in-silico findings were further experimentally validated. The recombinant CDK6 was expressed, purified, and treated with selonsertib. The binding affinity of selonsertib to CDK6 was estimated by fluorescence binding studies and enzyme inhibition assay. The results indicated an appreciable binding of selonsertib against CDK6, which subsequently inhibits its activity with a commendable IC50 value (9.8 μM). We concluded that targeting CDK6 by selonsertib can be an efficient therapeutic approach to cancer and other CDK6-related diseases. These observations provide a promising opportunity to utilize selonsertib to address CDK6-related human pathologies.
Lung Cancer Stem Cell Markers as Therapeutic Targets: An Update on Signaling Pathways and Therapies
Authors: Zheng Yue , Wang Laduona , Yin Limei , Yao Zhuoran , Tong Ruizhan , Xue Jianxin , Lu You
Published: June 2022

Cancer stem cells, a relatively small group of self-renewing cancer cells, were first isolated from acute myeloid leukemia. These cells can play a crucial role in tumor metastasis, relapse, and therapy resistance. The cancer stem cell theory may be applied to lung cancer and explain the inefficiency of traditional treatments and eventual recurrence. However, because of the unclear accuracy and illusive biological function of cancer stem cells, some researchers remain cautious about this theory. Despite the ongoing controversy, cancer stem cells are still being investigated, and their biomarkers are being discovered for application in cancer diagnosis, targeted therapy, and prognosis prediction. Potential lung cancer stem cell markers mainly include surface biomarkers such as CD44, CD133, epithelial cell adhesion molecule, and ATP-binding cassette subfamily G member 2, along with intracellular biomarkers such as aldehyde dehydrogenase, sex-determining region Y-box 2, NANOG, and octamer-binding transcription factor 4. These markers have different structures and functions but are closely associated with the stem potential and uncontrollable proliferation of tumor cells. The aberrant activation of major signaling pathways, such as Notch, Hedgehog, and Wnt, may be associated with the expression and regulation of certain lung cancer stem cell markers, thus leading to lung cancer stem cell maintenance, chemotherapy resistance, and cancer promotion. Treatments targeting lung cancer stem cell markers, including antibody drugs, nanoparticle drugs, chimeric antigen receptor T-cell therapy, and other natural or synthetic specific inhibitors, may provide new hope for patients who are resistant to conventional lung cancer therapies. This review provides comprehensive and updated data on lung cancer stem cell markers with regard to their structures, functions, signaling pathways, and promising therapeutic target approaches, aiming to elucidate potential new therapies for lung cancer.
Biological Characterization and Clinical Value of OAS Gene Family in Pancreatic Cancer
Authors: Gao Li-Juan , Li Jia-Lei , Yang Rui-Rui , He Zhong-Mei , Yan Min , Cao Xia , Cao Ji-Min
Published: June 2022

Background

OAS gene family plays an important role in antiviral process, but its role in pancreatic cancer has not yet been studied.

Methods

We analyzed the expression, prognostic value and biological function of the OAS gene family in human pancreatic cancer through comprehensive bioinformatic analysis and cellular level validation.

Results

OAS family was highly expressed in pancreatic cancer, and this high expression significantly affected the clinical stage and prognosis of the tumor. OAS gene family was closely related to the immune infiltration of pancreatic cancer, especially neutrophils and dendritic cells, and many immune-related factors and pathways are enriched in the tumor, such as type I interferon signaling pathway and NOD-like receptor signaling pathway.

Conclusion

Taken together, high expression of OAS family is closely related to poor prognosis of pancreatic cancer. OAS gene family may serve as the biomarker and even therapeutic target of pancreatic cancer.
Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study
Authors: Yu Ze , Ye Xuan , Liu Hongyue , Li Huan , Hao Xin , Zhang Jinyuan , Kou Fang , Wang Zeyuan , Wei Hai , Gao Fei , Zhai Qing
Published: June 2022

Lapatinib is used for the treatment of metastatic HER2(+) breast cancer. We aim to establish a prediction model for lapatinib dose using machine learning and deep learning techniques based on a real-world study. There were 149 breast cancer patients enrolled from July 2016 to June 2017 at Fudan University Shanghai Cancer Center. The sequential forward selection algorithm based on random forest was applied for variable selection. Twelve machine learning and deep learning algorithms were compared in terms of their predictive abilities (logistic regression, SVM, random forest, Adaboost, XGBoost, GBDT, LightGBM, CatBoost, TabNet, ANN, Super TML, and Wide&Deep). As a result, TabNet was chosen to construct the prediction model with the best performance (accuracy = 0.82 and AUC = 0.83). Afterward, four variables that strongly correlated with lapatinib dose were ranked via importance score as follows: treatment protocols, weight, number of chemotherapy treatments, and number of metastases. Finally, the confusion matrix was used to validate the model for a dose regimen of 1,250 mg lapatinib (precision = 81% and recall = 95%), and for a dose regimen of 1,000 mg lapatinib (precision = 87% and recall = 64%). To conclude, we established a deep learning model to predict lapatinib dose based on important influencing variables selected from real-world evidence, to achieve an optimal individualized dose regimen with good predictive performance.
MRI-Based Pancreatic Atrophy Is Associated With Malignancy or Invasive Carcinoma in Intraductal Papillary Mucinous Neoplasm
Authors: Lin Tingting , Chen Xin , Liu Jingjing , Cao Yingying , Cui Wenjing , Wang Zhongqiu , Wang Cheng , Chen Xiao
Published: June 2022

Background

Abrupt change in the caliber of the main pancreatic duct (MPD) with distal pancreatic atrophy (PA) was considered as one of worrisome features in the International Association of Pancreatology guideline and American College of Gastroenterology guideline for the management of intraductal papillary mucinous neoplasms (IPMNs). However, this feature was not included in other guidelines. Moreover, the association between PA alone and malignancy in IPMNs has not been fully evaluated. In the present study, we investigated the role of image-based PA in identifying malignant IPMNs or invasive carcinoma.

Methods

A total of 186 patients with IPMNs were included for analysis. The tumor size, location, MPD diameter, presence of a mural nodule (MN), and PA were evaluated using magnetic resonance imaging. Demographic information and serum carbohydrate antigen 19-9 and carcinoembryonic antigen (CEA) levels were also collected. IPMNs with high-grade dysplasia and associated invasive carcinoma were regarded as malignant IPMNs.

Results

PA was observed in 34 cases (18.3%). The occurrence of malignant IPMNs or invasive carcinoma in patients with PA were significantly higher than in those without PA (52.9% vs. 22.3%; 44.1% vs. 8.9%, all P < 0.01). Multivariate logistic regression analysis showed that PA was an independently associated factor for malignant IPMNs [odds ratio (OR) = 2.69, 95% confidence interval (CI): 1.07-6.78] or invasive carcinoma (OR = 7.78, 95%CI: 2.62-23.10) after modified with confounders. Subgroup analysis in MPD-involved IPMNs also indicated that PA was an independently associated factor for invasive carcinoma (OR = 9.72, 95%CI: 2.43-38.88). PA had a similar performance with MPD plus MN [the area under the curve (AUC) was both 0.71] in identifying malignancy. PA had a higher performance in identifying invasive carcinoma in MPD-involved IPMNs than MN (AUC = 0.71 vs. 0.65, P = 0.02).

Conclusion

Our data showed that imaging-based PA was associated with malignancy or invasive carcinoma regardless of abrupt change in the caliber of MPD in IPMNs. PA had an acceptable performance in identifying malignant IPMNs.
Systemic Ketone Replacement Does Not Improve Survival or Cancer Cachexia in Mice With Lung Cancer
Authors: Langer Henning Tim , Ramsamooj Shakti , Liang Roger J. , Grover Rahul , Hwang Seo-Kyoung , Goncalves Marcus DaSilva
Published: June 2022

Cachexia is a debilitating comorbidity affecting many lung cancer patients. We have previously found that cachectic mice with lung cancer have reduced serum ketone body levels due to low PPARα activity in the liver. Restoring hepatic PPARα activity with fenofibrate increased circulating ketones and delayed muscle and white adipose tissue wasting. We hypothesized that the loss of circulating ketones plays a pathophysiologic role in cachexia and performed two dietary intervention studies to test this hypothesis. In the first study, male and female mice were randomized to consume either a very low carbohydrate, ketogenic diet (KD) or normal chow (NC) after undergoing tumor induction. The KD successfully restored serum ketone levels and decreased blood glucose in cachectic mice but did not improve body weight maintenance or survival. In fact, there was a trend for the KD to worsen survival in male but not in female mice. In the second study, we compounded a ketone ester supplement into the NC diet (KE) and randomized tumor-bearing mice to KE or NC after tumor induction. We confirmed that KE was able to acutely and chronically increase ketone body abundance in the serum compared to NC. However, the restoration of ketones in the circulation was not able to improve body weight maintenance or survival in male or female mice with lung cancer. Finally, we investigated PPARα activity in the liver of mice fed KE and NC and found that animals fed a ketone ester supplement showed a significant increase in mRNA expression of several PPARα targets. These data negate our initial hypothesis and suggest that restoring ketone body availability in the circulation of mice with lung cancer does not alter cachexia development or improve survival, despite increasing hepatic PPARα activity.
Prognostic Significance of NLR, PLR, LMR and Tumor Infiltrating T Lymphocytes in Patients Undergoing Surgical Resection for Hilar Cholangiocarcinoma
Authors: Lin Zhi-qiang , Ma Chi , Cao Wen-zhuo , Ning Zhen , Tan Guang
Published: June 2022

Objective

This study evaluated the prognostic significance of preoperative neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR) and tumor-infiltrating lymphocytes (TILs), and whether these preoperative blood inflammatory indicators were associated with TILs in hilar cholangiocarcinoma (HCCA).

Methods

A total of 76 patients with HCCA who underwent radical resection were included. Data on their clinicopathologic characteristics, perioperative features, and survival outcomes were analyzed. The optimal cutoff levels for the NLR, PLR and LMR were defined by using the web application Cut-off Finder. The densities of specific immune cells (CD3+, CD4+, CD8+) within the tumor microenvironment were examined by immunohistochemical. The association of the number of CD3+, CD4+ and CD8+ T cells infiltration in the local tumor microenvironment with preoperative NLR, PLR and LMR level was analyzed. Survival curves were calculated using the Kaplan–Meier estimate. Univariate and multivariate logistic regression models were used to identify factors associated with overall survival.

Results

The optimal cutoff value of preoperative NLR, PLR and LMR was 2.00, 117.60, and 4.02, respectively. NLR was significantly negatively correlated with CD3+ and CD8+ T cell infiltration, but not with CD4+ T cells. PLR had no correlation with CD3+, CD4+, or CD8+ T cell infiltration, while LMR had a significantly positive correlation with CD3+ T cells infiltration but not with CD4+ or CD8+ T cells. In the multivariate logistic regression model, T stage, lymph node metastasis, CA19-9 and LMR were independent risk factors associated with overall survival (OS). Survival curves indicated that HCCA patients with low CD3+ T cells infiltration and low preoperative LMR live shorter than others.

Conclusions

LMR played as an independent factor for predicting the survival in patients with HCCA after R0 radical resection. A high LMR was associated with an accumulation of CD3+ T cells in HCCA.
Intensity-Modulated Radiotherapy Associated With Improved Survival Outcome in Anal Cancer
Authors: Mohamed Ahmed Allam , Schlenter Marsha , Heinzel Alexander , Kintsler Svetlana , Eble Michael J.
Published: June 2022

Purpose

To describe the survival and toxicity outcome from a single-centre experience in patients with squamous cell cancer of the anal canal (SCC-AC), related to the impact of technological advances in diagnostics and radiation techniques.

Material and Methods

A retrospective cohort study was performed after the approval of the institutional ethical committee (EK 478-21). We identified 142 patients in our registry, who received radical treatment for SCC-AC between 2000 and 2020. Fifty-five patients had FDG PET/CT for initial staging and target volume delineation, 87.33% received concomitant chemoradiotherapy (CRT), 64 patients were treated with 3-dimensional conformal radiotherapy (3DRT) between 2000-2009, and 78 patients with intensity-modulated radiotherapy (IMRT) between 2009-2020. Endpoints for the analysis included locoregional relapse-free survival (LRFS), disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS). Acute and late toxicities were also reported.

Results

At a median follow-up of 31.2 months, the median overall survival was 135 months, 5-year LRFS was 73.1%, 5-year DFS was 65.3%, and 5-year CSS was 75.3%. The use of IMRT was associated with shorter treatment duration. In the univariate analysis, IMRT was associated with significantly improved DFS and CSS for the whole cohort and significantly improved DFS, OS, and CSS for patients who received CRT. In the multivariate analysis, IMRT was associated with the improvement of all survival paraments. The use of FDG PET/CT did not translate into an improvement in the survival outcomes in both univariate and multivariate analyses. Grade-3 and more dermatological toxicities occurred less frequently, but hematological toxicities were more frequent in the IMRT-group. Late side effects and colostomies were less frequently reported in the IMRT group.

Conclusion

The use of IMRT in the management of SCC-AC was associated with improvement of the oncological outcomes with improved toxicity profiles in this long-term single-centre experience.
Glutathione Peroxidase 4 as a Therapeutic Target for Anti-Colorectal Cancer Drug-Tolerant Persister Cells
Authors: Zhang Xiaoli , Ma Yiming , Ma Jianhui , Yang Lan , Song Qingzhi , Wang Hongying , Lv Guoqing
Published: June 2022

Background

Despite the effectiveness of chemotherapy and targeted therapy for colorectal cancer, drug resistance drives therapy failure and tumor relapse. Increasing evidence has suggested that cancer cells can enter a reversible drug-tolerant persister state to survive chemotherapy or targeted agents. However, the traits and treatable vulnerabilities of anti-colorectal cancer drug-tolerant persister cells is not yet known.

Methods

In this study, we established 5-fluorouracil and AZ628-tolerant persister cell models in two colorectal cancer cell lines, namely HCT116 and SW620, and revealed the characteristics of colorectal cancer persister cells by cell viability assay and flow cytometry. We investigated the efficacy and mechanism of ferroptosis inducers RSL3 and FIN56 on persister cells, which are glutathione peroxidase 4 inhibitors. In the xenograft mouse model, we further evaluated the inhibitory effect of RSL3 on tumor regrowth.

Results

Colorectal cancer persister cells, which were enriched in the residual cancer cell population, exhibited reduced drug sensitivity, were largely quiescent and expressed high levels of stem cell-related genes and mesenchymal markers but not epithelial markers. The persister cells were more sensitive and underwent ferroptosis induced by glutathione peroxidase 4 inhibitors. Mechanistically, glutathione peroxidase 4 and ferrous iron, which are pivotal ferroptosis regulators, were upregulated in residual cells or tumors, and were hence potential therapeutic targets of persister cells. In the xenograft model, we confirmed that inhibition of glutathione peroxidase 4 restrained tumor regrowth after discontinuation of anti-cancer drug treatment. Moreover, biopsies obtained from patients with colorectal cancer undergoing neoadjuvant chemoradiotherapy revealed upregulated glutathione peroxidase 4 and ferritin heavy chain 1. High glutathione peroxidase 4 expression correlates with a worse prognosis in colorectal cancer patients.

Conclusions

Our work reveals that the upregulated glutathione peroxidase 4 and ferrous iron in anti-colorectal cancer drug-tolerant persister cells were potential therapeutic targets. Glutathione peroxidase 4 inhibition combined with chemotherapy or targeted therapy may be a promising therapy for colorectal cancer.
ESR2 Drives Mesenchymal-to-Epithelial Transition in Triple-Negative Breast Cancer and Tumorigenesis In Vivo
Authors: Piperigkou Zoi , Koutsandreas Anastasios , Franchi Marco , Zolota Vasiliki , Kletsas Dimitrios , Passi Alberto , Karamanos Nikos K.
Published: June 2022

Estrogen receptors (ERs) have pivotal roles in the development and progression of triple-negative breast cancer (TNBC). Interactions among cancer cells and tumor microenvironment are orchestrated by the extracellular matrix that is rapidly emerging as prominent contributor of fundamental processes of breast cancer progression. Early studies have correlated ERβ expression in tumor sites with a more aggressive clinical outcome, however ERβ exact role in the progression of TNBC remains to be elucidated. Herein, we introduce the functional role of ERβ suppression following isolation of monoclonal cell populations of MDA-MB-231 breast cancer cells transfected with shRNA against human ESR2 that permanently resulted in 90% reduction of ERβ mRNA and protein levels. Further, we demonstrate that clone selection results in strongly reduced levels of the aggressive functional properties of MDA-MB-231 cells, by transforming their morphological characteristics, eliminating the mesenchymal-like traits of triple-negative breast cancer cells. Monoclonal populations of shERβ MDA-MB-231 cells undergo universal matrix reorganization and pass on a mesenchymal-to-epithelial transition state. These striking changes are encompassed by the total prevention of tumorigenesis in vivo following ERβ maximum suppression and isolation of monoclonal cell populations in TNBC cells. We propose that these novel findings highlight the promising role of ERβ targeting in future pharmaceutical approaches for managing the metastatic dynamics of TNBC breast cancer.