bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neoadjuvant chemo-immunotherapy with camrelizumab plus nab-paclitaxel and cisplatin in resectable locally advanced squamous cell carcinoma of the head and neck: a pilot phase II trial
Authors: Wu Di , Li Yong , Xu Pengfei , Fang Qi , Cao Fei , Lin Hongsheng , Li Yin , Su Yong , Lu Lixia , Chen Lei , Li Yizhuo , zhao Zheng , Hong Xiaoyu , Li Guohong , Tian Yaru , Sun Jinyun , Yan Honghong , Fan Yunyun , Zhang Xinrui , Li Zhiming , Liu Xuekui 
Published: March 2024

AbstractNeoadjuvant chemoimmunotherapy has emerged as a potential treatment option for resectable head and neck squamous cell carcinoma (HNSCC). In this single-arm phase II trial (NCT04826679), patients with resectable locally advanced HNSCC (T2‒T4, N0‒N3b, M0) received neoadjuvant chemoimmunotherapy with camrelizumab (200 mg), nab-paclitaxel (260 mg/m2), and cisplatin (60 mg/m2) intravenously on day one of each three-week cycle for three cycles. The primary endpoint was the objective response rate (ORR). Secondary endpoints included pathologic complete response (pCR), major pathologic response (MPR), two-year progression-free survival rate, two-year overall survival rate, and toxicities. Here, we report the perioperative outcomes; survival outcomes were not mature at the time of data analysis. Between April 19, 2021 and March 17, 2022, 48 patients were enrolled and received neoadjuvant therapy, 27 of whom proceeded to surgical resection and remaining 21 received non-surgical therapy. The ORR was 89.6% (95% CI: 80.9, 98.2) among 48 patients who completed neoadjuvant therapy. Of the 27 patients who underwent surgery, 17 (63.0%, 95% CI: 44.7, 81.2) achieved a MPR or pCR, with a pCR rate of 55.6% (95% CI: 36.8, 74.3). Treatment-related adverse events of grade 3 or 4 occurred in two patients. This study meets the primary endpoint showing potential efficacy of neoadjuvant camrelizumab plus nab-paclitaxel and cisplatin, with an acceptable safety profile, in patients with resectable locally advanced HNSCC.
BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis
Authors: Qiu Zhiguang , Yuan Li , Lian Chun-Ang , Lin Bin , Chen Jie , Mu Rong , Qiao Xuejiao , Zhang Liyu , Xu Zheng , Fan Lu , Zhang Yunzeng , Wang Shanquan , Li Junyi , Cao Huiluo , Li Bing , Chen Baowei , Song Chi , Liu Yongxin , Shi Lili , Tian Yonghong , Ni Jinren , Zhang Tong , Zhou Jizhong , Zhuang Wei-Qin , Yu Ke 
Published: March 2024

AbstractMetagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.
Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions
Authors: Currie Jordan , Manda Vyshnavi , Robinson Sean K. , Lai Celine , Agnihotri Vertica , Hidalgo Veronica , Ludwig R. W. , Zhang Kai , Pavelka Jay , Wang Zhao V. , Rhee June-Wha , Lam Maggie P. Y. , Lau Edward 
Published: March 2024

AbstractThe spatial and temporal distributions of proteins are critical to protein function, but cannot be directly assessed by measuring protein bundance. Here we describe a mass spectrometry-based proteomics strategy, Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently protein turnover rates and subcellular localization in the same experiment. Applying the method, we find that unfolded protein response (UPR) has different effects on protein turnover dependent on their subcellular location in human AC16 cells, with proteome-wide slowdown but acceleration among stress response proteins in the ER and Golgi. In parallel, UPR triggers broad differential localization of proteins including RNA-binding proteins and amino acid transporters. Moreover, we observe newly synthesized proteins including EGFR that show a differential localization under stress than the existing protein pools, reminiscent of protein trafficking disruptions. We next applied SPLAT to an induced pluripotent stem cell derived cardiomyocyte (iPSC-CM) model of cancer drug cardiotoxicity upon treatment with the proteasome inhibitor carfilzomib. Paradoxically, carfilzomib has little effect on global average protein half-life, but may instead selectively disrupt sarcomere protein homeostasis. This study provides a view into the interactions of protein spatial and temporal dynamics and demonstrates a method to examine protein homeostasis regulations in stress and drug response.
Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology
Authors: Filandr Frantisek , Sarpe Vladimir , Raval Shaunak , Crowder D. Alex , Khan Morgan F. , Douglas Pauline , Coales Stephen , Viner Rosa , Syed Aleem , Tainer John A. , Lees-Miller Susan P. , Schriemer David C. 
Published: March 2024

AbstractWe present a hydrogen/deuterium exchange workflow coupled to tandem mass spectrometry (HX-MS2) that supports the acquisition of peptide fragment ions alongside their peptide precursors. The approach enables true auto-curation of HX data by mining a rich set of deuterated fragments, generated by collisional-induced dissociation (CID), to simultaneously confirm the peptide ID and authenticate MS1-based deuteration calculations. The high redundancy provided by the fragments supports a confidence assessment of deuterium calculations using a combinatorial strategy. The approach requires data-independent acquisition (DIA) methods that are available on most MS platforms, making the switch to HX-MS2 straightforward. Importantly, we find that HX-DIA enables a proteomics-grade approach and wide-spread applications. Considerable time is saved through auto-curation and complex samples can now be characterized and at higher throughput. We illustrate these advantages in a drug binding analysis of the ultra-large protein kinase DNA-PKcs, isolated directly from mammalian cells.
An analysis of adjuvant chemoradiotherapy versus chemotherapy on the survival rates for patients with stage IB-III uterine serous carcinoma
Authors: Li Shuqing , Yi Zhihui , Li Mingqing , Zhu Zhiling 
Published: March 2024

AbstractThe aim of the present study was to investigate whether a combination of chemotherapy plus radiotherapy was able to increase the overall survival rates compared with chemotherapy alone in stage IB-III uterine serous carcinoma. A total of 1096 patients (593 who had not received radiotherapy, and 503 who had) with primary stage IB-III uterine serous carcinoma who underwent surgery and received chemotherapy were included in the present study. The Kaplan–Meier method and Log-Rank tests showed that radiotherapy did not increase 5-year overall survival rates compared with the no-radiotherapy groups (52.3 cf. 50.8%, respectively; P = 0.641). Cox regression analysis subsequently corroborated that radiotherapy did not affect the 5-year overall survival rate (P = 0.635). Patients who were aged ≥ 60 years had a higher mortality rate [hazard ratio (HR), 1.712; 95% confidence interval (95% CI), 1.385–2.117; P < 0.05]. The 5-year overall survival rates were found to be lower in the groups where the regional lymph nodes had not been removed (HR 0.645; 95% CI 0.508–0.821; P < 0.05). Chemotherapy plus radiotherapy was found to not be associated with improved 5-year overall survival rates. However, chemotherapy may be a better treatment option for patients with primary stage IB-III uterine serous carcinoma who have undergone surgery.
Macro-meso damage characteristics of coal body under different pressure relief conditions
Authors: Li Jinyu , Pang Jiawei , Wang Lei , Zhao Hongbao , Zhang Yilong , Wang Tao 
Published: March 2024

AbstractThe mining of the protective coal seam usually produces different pressure relief effects on the different areas of protected coal seam, the reason is that the stress paths of protected seam coal body in different areas caused by mining effect are different. In order to explore the differential pressure relief damage effect of coal body under different pressure relief conditions, the stress evolution path of coal body in different areas of the protected coal seam is obtianed by using theoretical analysis and the macro–micro damage characteristics of coal body under different stress paths by using numerical simulation in this paper. The results show that: The damage characteristics of the sample models are basically the same in the in-situ stress recovery stage and the mining disturbance stage of the two stress paths. With ith the sequence of stress stages experienced by the sample model, the distribution of acoustic emission events concentrates in the high-intensity area and the porosity continues to decrease. The number of cracks increases slowly in the stage of in-situ stress recovery stage, most of which are tensile cracks, while the number of cracks increases sharply in the mining disturbance stage, most of which are shear cracks. The difference of the deformation and macro meso damage characteristics of the sample models under the two stress paths is mainly reflected in the post mining pressure relief stage. At the post mining pressure relief stage of path 1, the number of cracks in the sample has little growth, and most of them are small energy tensile cracks, and the porosity increases, which verifies its obvious pressure relief activation antireflection effect; At this stage of path 2, the crack growth of the sample is obvious, and most of them are high-energy shear cracks, and the porosity continues to decrease. Compared with path 1, the pressure relief expansion effect of the sample model is suppressed and the compression damage continues to develop in this stage of path 2.
Therapy effect of cochleural alternating acoustic beam therapy versus traditional sound therapy for managing chronic idiopathic tinnitus patients
Authors: Liu Chunli , Zhang Jie , Qi Zhiwei , Yue Wenhui , Yuan Yujie , Jiang Tao , Zhang Shenglin , Zhang Shujun 
Published: March 2024

AbstractIdiopathic tinnitus is a common and complex disorder with no established cure. The CAABT (Cochleural Alternating Acoustic Beam Therapy CAABT), is a personalized sound therapy designed to target specific tinnitus frequencies and effectively intervene in tinnitus according to clinical tinnitus assessment. This study aimed to compare the effectiveness of the CAABT and Traditional Sound Therapy (TST) in managing chronic idiopathic tinnitus. This was a randomized, double-blind, parallel-group, single-center prospective study. Sixty adult patients with tinnitus were recruited and randomly assigned to the CAABT or TST group in a 1:1 ratio using a computer-generated randomization. The treatment lasted for 12 weeks, and participants underwent assessments using the tinnitus handicap inventory (THI), visual analog scale (VAS), tinnitus loudness measurements, and resting-state functional magnetic resonance imaging (rs-fMRI). Both groups showed significant reductions in THI scores, VAS scores, and tinnitus loudness after treatment. However, CAABT showed superiority to TST in THI Functional (p = 0.018), THI Emotional (p = 0.015), THI Catastrophic (p = 0.022), THI total score (p = 0.005) as well as VAS score (p = 0.022). More interesting, CAABT showed superiority to TST in the changes of THI scores, and VAS scores from baseline. The rs-fMRI results showed significant changes in the precuneus before and after treatment in both groups. Moreover, the CAABT group showed more changes in brain regions compared to the TST. No side effects were observed. These findings suggest that CAABT may be a promising treatment option for chronic idiopathic tinnitus, providing significant improvements in tinnitus-related symptoms and brain activity.Trial registration: ClinicalTrials.gov:NCT02774122.
Anomaly detection in IoT-based healthcare: machine learning for enhanced security
Authors: Khan Maryam Mahsal , Alkhathami Mohammed 
Published: March 2024

AbstractInternet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced representation of classes is used in generating a non-biased supervised (Random Forest, Adaptive Boosting, Logistic Regression, Perceptron, Deep Neural Network) machine learning models. These models are analyzed further by eliminating highly correlated features, reducing dimensionality, minimizing overfitting, and speeding up training times. Random Forest was found to perform optimally across binary and multiclass classification of IoT Attacks with an approximate accuracy of 99.55% under both reduced and all feature space. This improvement was complimented by a reduction in computational response time which is essential for real-time attack detection and response.
Twitter users perceptions of AI-based e-learning technologies
Authors: Stracqualursi Luisa , Agati Patrizia 
Published: March 2024

AbstractToday, teaching and learning paths increasingly intersect with technologies powered by emerging artificial intelligence (AI).This work analyses public opinions and sentiments about AI applications that affect e-learning, such as ChatGPT, virtual and augmented reality, microlearning, mobile learning, adaptive learning, and gamification. The way people perceive technologies fuelled by artificial intelligence can be tracked in real time in microblog messages promptly shared by Twitter users, who currently constitute a large and ever-increasing number of individuals. The observation period was from November 30, 2022, the date on which ChatGPT was launched, to March 31, 2023. A two-step sentiment analysis was performed on the collected English-language tweets to determine the overall sentiments and emotions. A latent Dirichlet allocation model was built to identify commonly discussed topics in tweets. The results show that the majority of opinions are positive. Among the eight emotions of theSyuzhetpackage, ‘trust’ and ‘joy’ are the most common positive emotions observed in the tweets, while ‘fear’ is the most common negative emotion. Among the most discussed topics with a negative outlook, two particular aspects of fear are identified: an ‘apocalyptic-fear’ that artificial intelligence could lead the end of humankind, and a fear for the ‘future of artistic and intellectual jobs’ as AI could not only destroy human art and creativity but also make the individual contributions of students and researchers not assessable. On the other hand, among the topics with a positive outlook, trust and hope in AI tools for improving efficiency in jobs and the educational world are identified. Overall, the results suggest that AI will play a significant role in the future of the world and education, but it is important to consider the potential ethical and social implications of this technology. By leveraging the positive aspects of AI while addressing these concerns, the education system can unlock the full potential of this emerging technology and provide a better learning experience for students.
A drug stabilizable GAL80ds for conditional control of gene expression via GAL4-UAS and CRISPR-Cas9 systems in Drosophila
Authors: Kogenaru Vaishnavi , Isalan Mark , Kogenaru Manjunatha 
Published: March 2024

AbstractThe binary GAL4-UAS expression system has been widely used in Drosophila to achieve tissue-specific expression of genes. To further allow for simultaneous spatial and conditional control of gene expression in existing GAL4 expression lines backgrounds, temperature and chemical controllable GAL80 variants have been engineered. Here we add a new drug stabilizable GAL80ds variant, by fusing it to a low-background DHFR-22-DD. We first quantify both single (DD-GAL80) and double (DD-GAL80-DD) architectures and show varied background and activation levels. Next, we demonstrate the utility of GAL80dsDrosophila line to regulate a cell death gene ectopically, in a drug-dependent manner, by utilizing an existing tissue-specific GAL4 driver that regulates the expression of a cell death gene under a UAS. Finally, we showcase the usefulness of GAL80ds in tight drug-mediated regulation of a target gene, from an endogenous locus, by utilizing an existing tissue-specific GAL4 to drive the expression of a dead Cas9 variant fused to the transcriptional coactivator nejire, under a UAS and in gRNA lines. Overall, these new GAL80ds lines expand the use of the wide variety of existing tissue-specific GAL4 and gene-specific gRNA lines. This enables conditional control of genes, both ectopically and endogenously, for a broad array of gene expression control applications.
An autonomous wheelchair with health monitoring system based on Internet of Thing
Authors: Hou Lei , Latif Jawwad , Mehryar Pouyan , Withers Stephen , Plastropoulos Angelos , Shen Linlin , Ali Zulfiqur 
Published: March 2024

AbstractAssistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users’ four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.
Identification of diagnostic markers for moyamoya disease by combining bulk RNA-sequencing analysis and machine learning
Authors: Xu Yifan , Chen Bing , Guo Zhongxiang , Chen Cheng , Wang Chao , Zhou Han , Zhang Chonghui , Feng Yugong 
Published: March 2024

AbstractMoyamoya disease (MMD) remains a chronic progressive cerebrovascular disease with unknown etiology. A growing number of reports describe the development of MMD relevant to infection or autoimmune diseases. Identifying biomarkers of MMD is to understand the pathogenesis and development of novel targeted therapy and may be the key to improving the patient’s outcome. Here, we analyzed gene expression from two GEO databases. To identify the MMD biomarkers, the weighted gene co-expression network analysis (WGCNA) and the differential expression analyses were conducted to identify 266 key genes. The KEGG and GO analyses were then performed to construct the protein interaction (PPI) network. The three machine-learning algorithms of support vector machine-recursive feature elimination (SVM-RFE), random forest and least absolute shrinkage and selection operator (LASSO) were used to analyze the key genes and take intersection to construct MMD diagnosis based on the four core genes found (ACAN, FREM1, TOP2A and UCHL1), with highly accurate AUCs of 0.805, 0.903, 0.815, 0.826. Gene enrichment analysis illustrated that the MMD samples revealed quite a few differences in pathways like one carbon pool by folate, aminoacyl-tRNA biosynthesis, fat digestion and absorption and fructose and mannose metabolism. In addition, the immune infiltration profile demonstrated that ACAN expression was associated with mast cells resting, FREM1 expression was associated with T cells CD4 naive, TOP2A expression was associated with B cells memory, UCHL1 expression was associated with mast cells activated. Ultimately, the four key genes were verified by qPCR. Taken together, our study analyzed the diagnostic biomarkers and immune infiltration characteristics of MMD, which may shed light on the potential intervention targets of moyamoya disease patients
Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology
Authors: Ozbozduman Kaan , Loc Irem , Durmaz Selahattin , Atasoy Duygu , Kilic Mert , Yildirim Hakan , Esen Tarik , Vural Metin , Unlu M. Burcin 
Published: March 2024

AbstractThis study aimed to enhance the accuracy of Gleason grade group (GG) upgrade prediction in prostate cancer (PCa) patients who underwent MRI-guided in-bore biopsy (MRGB) and radical prostatectomy (RP) through a combined analysis of prebiopsy and MRGB clinical data. A retrospective analysis of 95 patients with prostate cancer diagnosed by MRGB was conducted where all patients had undergone RP. Among the patients, 64.2% had consistent GG results between in-bore biopsies and RP, whereas 28.4% had upgraded and 7.4% had downgraded results. GG1 biopsy results, lower biopsy core count, and fewer positive cores were correlated with upgrades in the entire patient group. In patients with $$\hbox {GG}>1$$

GG
>
1

, larger tumor sizes and fewer biopsy cores were associated with upgrades. By integrating MRGB data with prebiopsy clinical data, machine learning (ML) models achieved 85.6% accuracy in predicting upgrades, surpassing the 64.2% baseline from MRGB alone. ML analysis also highlighted the value of the minimum apparent diffusion coefficient ($$\hbox {ADC}_{\text{min}}$$

ADC
min

) for $$\hbox {GG}>1$$

GG
>
1

patients. Incorporation of MRGB results with tumor size, $$\hbox {ADC}_{\text{min}}$$

ADC
min

value, number of biopsy cores, positive core count, and Gleason grade can be useful to predict GG upgrade at final pathology and guide patient selection for active surveillance.