bluuebunny/crossref_metadata_2025 huggingface dataset:
Inhibition of mitochondrial folate metabolism drives differentiation through mTORC1 mediated purine sensing
Authors: Zarou Martha M. , Rattigan Kevin M. , Sarnello Daniele , Shokry Engy , Dawson Amy , Ianniciello Angela , Dunn Karen , Copland Mhairi , Sumpton David , Vazquez Alexei , Helgason G. Vignir
Published: March 2024

Supporting cell proliferation through nucleotide biosynthesis is an essential requirement for cancer cells. Hence, inhibition of folate-mediated one carbon (1C) metabolism, which is required for nucleotide synthesis, has been successfully exploited in anti-cancer therapy. Here, we reveal that mitochondrial folate metabolism is upregulated in patient-derived leukaemic stem cells (LSCs). We demonstrate that inhibition of mitochondrial 1C metabolism through impairment of de novo purine synthesis has a cytostatic effect on chronic myeloid leukaemia (CML) cells. Consequently, changes in purine nucleotide levels lead to activation of AMPK signalling and suppression of mTORC1 activity. Notably, suppression of mitochondrial 1C metabolism increases expression of erythroid differentiation markers. Moreover, we find that increased differentiation occurs independently of AMPK signalling and can be reversed through reconstitution of purine levels and reactivation of mTORC1. Of clinical relevance, we identify that combination of 1C metabolism inhibition with imatinib, a frontline treatment for CML patients, decreases the number of therapy-resistant CML LSCs in a patient-derived xenograft model. Our results highlight a role for folate metabolism and purine sensing in stem cell fate decisions and leukaemogenesis.
fhl2b mediates extraocular muscle protection in zebrafish models of muscular dystrophies and its ectopic expression ameliorates affected body muscles
Authors: Dennhag Nils , Kahsay Abraha , Nissen Itzel , Nord Hanna , Chermenina Maria , Liu Jiao , Arner Anders , Liu Jing-Xia , Backman Ludvig J. , Remeseiro Silvia , von Hofsten Jonas , Pedrosa Domellöf Fatima
Published: March 2024

In muscular dystrophies, muscle fibers loose integrity and die, causing significant suffering and premature death. Strikingly, the extraocular muscles (EOMs) are spared, functioning well despite the disease progression. Although EOMs have been shown to differ from body musculature, the mechanisms underlying this inherent resistance to muscle dystrophies remain unknown. Here, we demonstrate important differences in gene expression as a response to muscle dystrophies between the EOMs and trunk muscles in zebrafish via transcriptomic profiling. We show that the LIM-protein Fhl2 is increased in response to the knockout ofdesmin,plectinandobscurin, cytoskeletal proteins whose knockout causes different muscle dystrophies, and contributes to disease protection of the EOMs. Moreover, we show that ectopic expression offhl2bcan partially rescue the muscle phenotype in the zebrafish Duchenne muscular dystrophy modelsapje, significantly improving their survival. Therefore, Fhl2 is a protective agent and a candidate target gene for therapy of muscular dystrophies.
Silver-catalyzed direct conversion of epoxides into cyclopropanes using N-triftosylhydrazones
Authors: Li Linxuan , Sivaguru Paramasivam , Wei Dandan , Liu Menglin , Zhu Qingwen , Dong Shuai , Casali Emanuele , Li Nan , Zanoni Giuseppe , Bi Xihe
Published: March 2024

Epoxides, as a prominent small ring O-heterocyclic and the privileged pharmacophores for medicinal chemistry, have recently represented an ideal substrate for the development of single-atom replacements. The previous O-to-C replacement strategy for epoxides to date typically requires high temperatures to achieve low yields and lacks substrate range and functional group tolerance, so achieving this oxygen-carbon exchange remains a formidable challenge. Here, we report a silver-catalyzed direct conversion of epoxides into trifluoromethylcyclopropanes in a single step using trifluoromethyl N-triftosylhydrazones as carbene precursors, thereby achieving oxygen-carbon exchange via a tandem deoxygenation/[2 + 1] cycloaddition. The reaction shows broad tolerance of functional groups, allowing routine cheletropic olefin synthesis in a strategy for the net oxygen-carbon exchange reaction. The utility of this method is further showcased with the late-stage diversification of epoxides derived from bioactive natural products and drugs. Mechanistic experiments and DFT calculations elucidate the reaction mechanism and the origin of the chemo- and stereoselectivity.
Alternative low-populated conformations prompt phase transitions in polyalanine repeat expansions
Authors: Antón Rosa , Treviño Miguel Á. , Pantoja-Uceda David , Félix Sara , Babu María , Cabrita Eurico J. , Zweckstetter Markus , Tinnefeld Philip , Vera Andrés M. , Oroz Javier
Published: March 2024

Abnormal trinucleotide repeat expansions alter protein conformation causing malfunction and contribute to a significant number of incurable human diseases. Scarce structural insights available on disease-related homorepeat expansions hinder the design of effective therapeutics. Here, we present the dynamic structure of human PHOX2B C-terminal fragment, which contains the longest polyalanine segment known in mammals. The major α-helical conformation of the polyalanine tract is solely extended by polyalanine expansions in PHOX2B, which are responsible for most congenital central hypoventilation syndrome cases. However, polyalanine expansions in PHOX2B additionally promote nascent homorepeat conformations that trigger length-dependent phase transitions into solid condensates that capture wild-type PHOX2B. Remarkably, HSP70 and HSP90 chaperones specifically seize PHOX2B alternative conformations preventing phase transitions. The precise observation of emerging polymorphs in expanded PHOX2B postulates unbalanced phase transitions as distinct pathophysiological mechanisms in homorepeat expansion diseases, paving the way towards the search of therapeutics modulating biomolecular condensates in central hypoventilation syndrome.
Widespread stable noncanonical peptides identified by integrated analyses of ribosome profiling and ORF features
Authors: Yang Haiwang , Li Qianru , Stroup Emily K. , Wang Sheng , Ji Zhe
Published: March 2024

Studies have revealed dozens of functional peptides in putative ‘noncoding’ regions and raised the question of how many proteins are encoded by noncanonical open reading frames (ORFs). Here, we comprehensively annotate genome-wide translated ORFs across five eukaryotes (human, mouse, zebrafish, worm, and yeast) by analyzing ribosome profiling data. We develop a logistic regression model named PepScore based on ORF features (expected length, encoded domain, and conservation) to calculate the probability that the encoded peptide is stable in humans. Systematic ectopic expression validates PepScore and shows that stable complex-associating microproteins can be encoded in 5’/3’ untranslated regions and overlapping coding regions of mRNAs besides annotated noncoding RNAs. Stable noncanonical proteins follow conventional rules and localize to different subcellular compartments. Inhibition of proteasomal/lysosomal degradation pathways can stabilize some peptides especially those with moderate PepScores, but cannot rescue the expression of short ones with low PepScores suggesting they are directly degraded by cellular proteases. The majority of human noncanonical peptides with high PepScores show longer lengths but low conservation across species/mammals, and hundreds contain trait-associated genetic variants. Our study presents a statistical framework to identify stable noncanonical peptides in the genome and provides a valuable resource for functional characterization of noncanonical translation during development and disease.
Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability
Authors: Chen Zhenjia , Lin Zhenyuan , Yang Ji , Chen Cong , Liu Di , Shan Liuting , Hu Yuanyuan , Guo Tailiang , Chen Huipeng
Published: March 2024

Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models. However, hardware neural network reports still mainly focus on shallow networks (2 to 5 layers). Implementing deep neural networks in hardware is challenging due to the layer-by-layer structure, resulting in long training times, signal interference, and low accuracy due to gradient explosion/vanishing. Here, we utilize negative ultraviolet photoconductive light-emitting memristors with intrinsic parallelism and hardware-software co-design to achieve electrical information’s optical cross-layer transmission. We propose a hybrid ultra-deep photoelectric neural network and an ultra-deep super-resolution reconstruction neural network using light-emitting memristors and cross-layer block, expanding the networks to 54 and 135 layers, respectively. Further, two networks enable transfer learning, approaching or surpassing software-designed networks in multi-dataset recognition and high-resolution restoration tasks. These proposed strategies show great potential for high-precision multifunctional hardware neural networks and edge artificial intelligence.
Earthquake forecasting from paleoseismic records
Authors: Wang Ting , Griffin Jonathan D. , Brenna Marco , Fletcher David , Zeng Jiaxu , Stirling Mark , Dillingham Peter W. , Kang Jie
Published: March 2024

Forecasting large earthquakes along active faults is of critical importance for seismic hazard assessment. Statistical models of recurrence intervals based on compilations of paleoseismic data provide a forecasting tool. Here we compare five models and use Bayesian model-averaging to produce time-dependent, probabilistic forecasts of large earthquakes along 93 fault segments worldwide. This approach allows better use of the measurement errors associated with paleoseismic records and accounts for the uncertainty around model choice. Our results indicate that although the majority of fault segments (65/93) in the catalogue favour a single best model, 28 benefit from a model-averaging approach. We provide earthquake rupture probabilities for the next 50 years and forecast the occurrence times of the next rupture for all the fault segments. Our findings suggest that there is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors.
A structural and dynamic visualization of the interaction between MAP7 and microtubules
Authors: Adler Agnes , Bangera Mamata , Beugelink J. Wouter , Bahri Salima , van Ingen Hugo , Moores Carolyn A. , Baldus Marc
Published: March 2024

Microtubules (MTs) are key components of the eukaryotic cytoskeleton and are essential for intracellular organization, organelle trafficking and mitosis. MT tasks depend on binding and interactions with MT-associated proteins (MAPs). MT-associated protein 7 (MAP7) has the unusual ability of both MT binding and activating kinesin-1-mediated cargo transport along MTs. Additionally, the protein is reported to stabilize MTs with its 112 amino-acid long MT-binding domain (MTBD). Here we investigate the structural basis of the interaction of MAP7 MTBD with the MT lattice. Using a combination of solid and solution-state nuclear magnetic resonance (NMR) spectroscopy with electron microscopy, fluorescence anisotropy and isothermal titration calorimetry, we shed light on the binding mode of MAP7 to MTs at an atomic level. Our results show that a combination of interactions between MAP7 and MT lattice extending beyond a single tubulin dimer and including tubulin C-terminal tails contribute to formation of the MAP7-MT complex.
Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics
Authors: Zhong Zhixing , Hou Junchen , Yao Zhixian , Dong Lei , Liu Feng , Yue Junqiu , Wu Tiantian , Zheng Junhua , Ouyang Gaoliang , Yang Chaoyong , Song Jia
Published: March 2024

Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.
A comprehensive dataset of environmentally contaminated sites in the state of São Paulo in Brazil
Authors: Samlani Nouha , Pino Daphne Silva , Bertolo Reginaldo , Pak Tannaz
Published: March 2024

In the Brazilian state of São Paulo, contaminated sites (CSs) constitute threats to health, environment and socioeconomic situation of populations. Over the past two decades, the Environmental Agency of São Paulo (CETESB) has monitored these known CSs. This paper discusses the produced dataset through digitising the CETESB reports and making them accessible to the public in English. The dataset reports on qualitative aspects of contamination within the registered sites (e.g., contamination type and spread) and their management status. The data was extracted from CETESB reports using a machine-learning computer vision algorithm. It comprises two components: an optical character recognition (OCR) engine for text extraction and a convolutional neural network (CNN) image classifier to identify checked boxes. The digitisation was followed by harmonisation and quality assurance processes to ensure the consistency and validity of the data. Making this dataset accessible will allow future work on predictive analysis and decision-making and will inform the required policy-making to improve the management of the CSs in Brazil.
Interactive design generation and optimization from generative adversarial networks in spatial computing
Authors: Hu Xiaochen , Lin Cun , Chen Tianyi , Chen Weibo
Published: March 2024

This paper focuses on exploring the application possibilities and optimization problems of Generative Adversarial Networks (GANs) in spatial computing to improve design efficiency and creativity and achieve a more intelligent design process. A method for icon generation is proposed, and a basic architecture for icon generation is constructed. A system with generation and optimization capabilities is constructed to meet various requirements in spatial design by introducing the concept of interactive design and the characteristics of requirement conditions. Next, the generated icons can effectively maintain diversity and innovation while meeting the conditional features by integrating multi-feature recognition modules into the discriminator and optimizing the structure of conditional features. The experiment uses publicly available icon datasets, including LLD-Icon and Icons-50. The icon shape generated by the model proposed here is more prominent, and the color of colored icons can be more finely controlled. The Inception Score (IS) values under different models are compared, and it is found that the IS value of the proposed model is 7.05, which is higher than that of other GAN models. The multi-feature icon generation model based on Auxiliary Classifier GANs performs well in presenting multiple feature representations of icons. After introducing multi-feature recognition modules into the network model, the peak error of the recognition network is only 2.000 in the initial stage, while the initial error of the ordinary GAN without multi-feature recognition modules is as high as 5.000. It indicates that the improved model effectively helps the discriminative network recognize the core information of icon images more quickly. The research results provide a reference basis for achieving more efficient and innovative interactive space design.
Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach
Authors: Elhadad Ahmed , Jamjoom Mona , Abulkasim Hussein
Published: March 2024

Magnetic resonance imaging is a medical imaging technique to create comprehensive images of the tissues and organs in the body. This study presents an advanced approach for storing and compressing neuroimaging informatics technology initiative files, a standard format in magnetic resonance imaging. It is designed to enhance telemedicine services by facilitating efficient and high-quality communication between healthcare practitioners and patients. The proposed downsampling approach begins by opening the neuroimaging informatics technology initiative file as volumetric data and then planning it into several slice images. Then, the quantization hiding technique will be applied to each of the two consecutive slice images to generate the stego slice with the same size. This involves the following major steps: normalization, microblock generation, and discrete cosine transformation. Finally, it assembles the resultant stego slice images to produce the final neuroimaging informatics technology initiative file as volumetric data. The upsampling process, designed to be completely blind, reverses the downsampling steps to reconstruct the subsequent image slice accurately. The efficacy of the proposed method was evaluated using a magnetic resonance imaging dataset, focusing on peak signal-to-noise ratio, signal-to-noise ratio, structural similarity index, and Entropy as key performance metrics. The results demonstrate that the proposed approach not only significantly reduces file sizes but also maintains high image quality.
Copper oxides supported sulfur-doped porous carbon material as a remarkable catalyst for reduction of aromatic nitro compounds
Authors: Amirjan Marzie , Nemati Firouzeh , Elahimehr Zeinab , Rangraz Yalda
Published: March 2024

Synthesis and manufacturing of metal–organic framework derived carbon/metal oxide nanomaterials with an advisable porous structure and composition are essential as catalysts in various organic transformation processes for the preparation of environmentally friendly catalysts. In this work, we report a scalable synthesis of sulfur-doped porous carbon-containing copper oxide nanoparticles (marked CuxO@CS-400) via direct pyrolysis of a mixture of metal–organic framework precursor called HKUST-1 and diphenyl disulfide for aromatic nitro compounds reduction. X-ray diffraction, surface area analysis (BET), X-ray energy diffraction (EDX) spectroscopy, thermal gravimetric analysis, elemental mapping, infrared spectroscopy (FT-IR), transmission electron microscope, and scanning electron microscope (FE-SEM) analysis were accomplished to acknowledge and investigate the effect of S and CuxO as active sites in heterogeneous catalyst to perform the reduction-nitro aromatic compounds reaction in the presence of CuxO@CS-400 as an effective heterogeneous catalyst. The studies showed that doping sulfur in the resulting carbon/metal oxide substrate increased the catalytic activity compared to the material without sulfur doping.
Numerical flow experiment for assessing predictors for cerebrovascular accidents in patients with PHACES syndrome
Authors: Wiśniewski Karol , Tyfa Zbigniew , Reorowicz Piotr , Brandel Michael G. , Adel Thomas , Obidowski Damian , Jóźwik Krzysztof , Levy Michael L.
Published: March 2024

There is an increased risk of cerebrovascular accidents (CVA) in individuals with PHACES, yet the precise causes are not well understood. In this analysis, we aimed to examine the role of arteriopathy in PHACES syndrome as a potential contributor to CVA. We analyzed clinical and radiological data from 282 patients with suspected PHACES syndrome. We analyzed clinical features, including the presence of infantile hemangioma and radiological features based on magnetic resonance angiography or computed tomography angiography, in individuals with PHACES syndrome according to the Garzon criteria. To analyze intravascular blood flow, we conducted a simulation based on the Fluid–Structure Interaction (FSI) method, utilizing radiological data. The collected data underwent statistical analysis. Twenty patients with PHACES syndrome were included. CVAs were noted in 6 cases. Hypoplasia (p = 0.03), severe tortuosity (p < 0.01), absence of at least one main cerebral artery (p < 0.01), and presence of persistent arteries (p = 0.01) were associated with CVAs, with severe tortuosity being the strongest predictor. The in-silico analysis showed that the combination of hypoplasia and severe tortuosity resulted in a strongly thrombogenic environment. Severe tortuosity, combined with hypoplasia, is sufficient to create a hemodynamic environment conducive to thrombus formation and should be considered high-risk for cerebrovascular accidents (CVAs) in PHACES patients.
Physician marriage survey reveals sex and specialty differences in marital satisfaction factors
Authors: Dutta Rajeev R. , Wu Anthony T. , Picton Bryce , Shah Saloni , Chernyak Michelle , Bauer Kelly , Solomon Sean , Chang Josephine , Nguyen Britney , Jiang Mio , Hurria Anju
Published: March 2024

Physician marriage is a valuable indicator of how vocational factors (e.g. work hours, stressors) impact satisfaction in relationships and physician wellness overall. Previous studies suggest that gender and specialty influence marriage satisfaction for physicians, though these often come from limited, local, cohorts. A cross-sectional survey was designed and distributed to publicly available email addresses representing academic and private practice physician organizations across the United States, receiving 321 responses (253 complete). Responses included data on demographics, medical specialty, age at marriage, stage of training at marriage, number of children, and factors leading to marital satisfaction/distress. A multivariable ordinal logistic regression was conducted to find associations between survey variables and marriage satisfaction. Survey results indicated that 86.5% of physicians have been married (average age at first marriage was 27.8 years old), and the rate of first marriages ending is at least 14.7%. Men had significantly more children than women. Physicians married at least once averaged 1.98 children. “Other” specialty physicians had significantly more children on average than psychiatrists. Marrying before medical school predicted practicing in private practice settings. Job stress, work hours, children, and sex were most frequently sources of marital distress, while strong communication, finances, and children were most frequently sources of marital stability. Sex differences were also found in distressing and stabilizing marital factors: Female physicians were more likely to cite their spouse’s work hours and job stress as sources of marital distress. Finally, surgery specialty and Judaism were associated with higher marriage satisfaction, whereas possession of an M.D. degree was associated with lower marriage satisfaction. This study elucidated new perspectives on physician marriage and families based on specialty, practice setting, and stage of training at marriage. Future studies may focus on factors mediating specialty and sex’s impact on having children and marriage satisfaction. To our knowledge, this study is the first physician marriage survey which integrates multiple factors in the analysis of physician marriages.
Optimizing lifestyle profiles is potential for preventing nonalcoholic fatty liver disease and enhancing its survival
Authors: Tu Beilin , Li Wei , Xiao Haitao , Xu Xuewen , Zhang Yange
Published: March 2024

The aim of this study was to evaluate the association between lifestyle profile and disease incidence/mortality in patients with non-alcoholic fatty liver disease (NAFLD). Lifestyle profiles ascertainment was based on the latent profile analysis. The associations of lifestyle profile and outcomes were analyzed by multivariate logistic or Cox regressions. Four lifestyle profiles (profile 1 and 2 for male, profile 3 and 4 for female) were established for all participants. Compared to profile 1, profile 2 (P = 0.042) and profile 3 (P = 0.013) had lower incidence for NAFLD. In contrast, profile 4 showed similar NAFLD prevalence compared to profile 1 (P = 0.756). Individuals with NAFLD within profile 3 had the best long-term survival, and the HR was 0.55 (95% CI 0.40–0.76) for all-cause mortality (compared to profile 1). Profile 4 (P = 0.098) and profile 2 (P = 0.546) had similar all-cause survival compared to profile 1. We explored the associations of healthy lifestyle score with mortality and incidence of NAFLD stratified by lifestyle profiles. We observed that with the increase of healthy lifestyle score, participants within profile 2 did not display lower NAFLD incidence and better long-term survival in NAFLD cases. In this study, lifestyle profiles were constructed in NHANES participants. The distinct lifestyle profiles may help optimize decision-making regarding lifestyle management in preventing NAFLD development, as well as selection of a more personalized approach for improving NAFLD survival.
A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data
Authors: Salehi Amir Reza , Khedmati Majid
Published: March 2024

In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a combination of over-sampling, under-sampling, and different ensemble algorithms, including Extreme Gradient Boosting (XGBoost), random forest, and bagging, is employed in order to achieve a balanced dataset and address the issues including redundancy of data after over-sampling, information loss in under-sampling, and random sample selection for sampling and sample generation. The performance of the proposed algorithm is evaluated and compared to different state-of-the-art competing algorithms based on 20 benchmark imbalanced datasets in terms of the harmonic mean of precision and recall (F1) and area under the receiver operating characteristics curve (AUC) measures. Based on the results, the proposed CSBBoost algorithm performs significantly better than the competing algorithms. In addition, a real-world dataset is used to demonstrate the applicability of the proposed algorithm.
Analysis of immunoinfiltration and EndoMT based on TGF-β signaling pathway-related genes in acute myocardial infarction
Authors: Shen Jun , Liang Junqing , Rejiepu Manzeremu , Ma Zhiqin , Zhao Jixian , Li Jia , Zhang Ling , Yuan Ping , Wang Jianing , Tang Baopeng
Published: March 2024

Acute myocardial infarction (AMI), a critical manifestation of coronary heart disease, presents a complex and not entirely understood etiology. This study investigates the potential role of immune infiltration and endothelial-mesenchymal transition (EndoMT) in AMI pathogenesis. We conducted an analysis of the GSE24519 and MSigDB datasets to identify differentially expressed genes associated with the TGF-β signaling pathway (DE-TSRGs) and carried out a functional enrichment analysis. Additionally, we evaluated immune infiltration in AMI and its possible link to myocardial fibrosis. Key genes were identified using machine learning and LASSO logistic regression. The expression of MEOX1 in the ventricular muscles and endothelial cells of Sprague–Dawley rats was assessed through RT-qPCR, immunohistochemical and immunofluorescence assays, and the effect of MEOX1 overexpression on EndoMT was investigated. Our study identified five DE-TSRGs, among which MEOX1, SMURF1, and SPTBN1 exhibited the most significant associations with AMI. Notably, we detected substantial immune infiltration in AMI specimens, with a marked increase in neutrophils and macrophages. MEOX1 demonstrated consistent expression patterns in rat ventricular muscle tissue and endothelial cells, and its overexpression induced EndoMT. Our findings suggest that the TGF-β signaling pathway may contribute to AMI progression by activating the immune response. MEOX1, linked to the TGF-β signaling pathway, appears to facilitate myocardial fibrosis via EndoMT following AMI. These novel insights into the mechanisms of AMI pathogenesis could offer promising therapeutic targets for intervention.
Knowledge, attitude, and practice toward photoaging in the Chinese population: a cross-sectional study
Authors: Li Yaoying , Hu Tianxing , Xia Xiaoqin , Ge Lan
Published: March 2024

To investigate the knowledge, attitude, and practice (KAP) of photoaging in the Chinese population. This web-based cross-sectional study was conducted between January 2023 and March 2023 among the Chinese population aged 18–80 years old. Participants’ knowledge, attitude, and practice toward photoaging were collected through a self-administered questionnaire. A total of 830 questionnaires were collected, with 826 valid questionnaires and an efficiency rate of 99.52%. There were 274 (33.17%) males and 532 (64.41%) aged 31–51 years old. The average knowledge, attitude, and practice scores were 7 (4, 9) (possible range 0–12), 31.5 (28, 34) (possible range 8–40), and 33 (24, 42) (possible range 11–55), respectively, indicating poor knowledge, good attitude, and moderate practice. Spearman correlation analysis showed that knowledge was negatively correlated with attitude (r = − 0.111, P < 0.05) and practice (r = − 0.113, P < 0.05), and attitude was positively correlated with practice (r = 0.992, P < 0.05). The multivariable linear regression model showed that for each point increase in attitude score, the practice score increased by 2.96 points (β = 2.96, 95% CI 2.91–3.01, P < 0.001). The Chinese population has poor knowledge, good attitude, and moderate practice toward photoaging. A good attitude toward photoaging would lead to good practice, and more outreach and education for the Chinese population might be needed.
Impact of COVID-19 versus other pneumonia on in-hospital mortality and functional decline among Japanese dialysis patients: a retrospective cohort study
Authors: Ikenouchi Ken , Takahashi Daiei , Mandai Shintaro , Watada Mizuki , Koyama Sayumi , Hoshino Motoki , Takahashi Naohiro , Shoda Wakana , Kuyama Tamaki , Mori Yutaro , Ando Fumiaki , Susa Koichiro , Mori Takayasu , Iimori Soichiro , Naito Shotaro , Sohara Eisei , Fushimi Kiyohide , Uchida Shinichi
Published: March 2024

Coronavirus disease 2019 (COVID-19) affects both life and health. However, the differentiation from other types of pneumonia and effect of kidney disease remains uncertain. This retrospective observational study investigated the risk of in-hospital death and functional decline in ≥ 20% of Barthel Index scores after COVID-19 compared to other forms of pneumonia among Japanese adults, both with and without end-stage kidney disease (ESKD). The study enrolled 123,378 patients aged 18 years and older from a national inpatient administrative claims database in Japan that covers the first three waves of the COVID-19 pandemic in 2020. After a 1:1:1:1 propensity score matching into non-COVID-19/non-dialysis, COVID-19/non-dialysis, non-COVID-19/dialysis, and COVID-19/dialysis groups, 2136 adults were included in the analyses. The multivariable logistic regression analyses revealed greater odds ratios (ORs) of death [5.92 (95% CI 3.62–9.96)] and functional decline [1.93 (95% CI 1.26–2.99)] only in the COVID-19/dialysis group versus the non-COVID-19/non-dialysis group. The COVID-19/dialysis group had a higher risk of death directly due to pneumonia (OR 6.02, 95% CI 3.50–10.8) or death due to other diseases (OR 3.00, 95% CI 1.11–8.48; versus the non-COVID-19/non-dialysis group). COVID-19 displayed a greater impact on physical function than other types of pneumonia particularly in ESKD.
Psychophysiological strategies for enhancing performance through imagery–skin conductance level analysis in guided vs. self-produced imagery
Authors: Budnik-Przybylska Dagmara , Syty Paweł , Kaźmierczak Maria , Przybylski Jacek , Doliński Łukasz , Łabuda Marta , Jasik Patryk , Kastrau Adrian , di Fronso Selenia , Bertollo Maurizio
Published: March 2024

Athletes need to achieve their optimal level of arousal for peak performance. Visualization or mental rehearsal (i.e., Imagery) often helps to obtain an appropriate level of activation, which can be detected by monitoring Skin Conductance Level (SCL). However, different types of imagery could elicit different amount of physiological arousal. Therefore, this study aims: (1) to investigate differences in SCL associated with two instructional modalities of imagery (guided vs. self-produced) and six different scripts; (2) to check if SCL could differentiate respondents according to their sport expertise. Thirty participants, aged between 14 and 42 years (M = 22.93; SD = 5.24), with different sport levels took part in the study. Participants listened to each previously recorded script and then were asked to imagine the scene for a minute. During the task, SCL was monitored. We analysed the mean value, variance, slope and number of fluctuations per minute of the electrodermal signal. Unsupervised machine learning models were used for measuring the resemblance of the signal. The Wilcoxon signed-rank test was used for distinguishing guided and self-produced imagery, and The Mann–Whitney U test was used for distinguishing results of different level athletes. We discovered that among others, self-produced imagery generates lower SCL, higher variance, and a higher number of fluctuations compared to guided imagery. Moreover, we found similarities of the SCL signal among the groups of athletes (i.e. expertise level). From a practical point of view, our findings suggest that different imagery instructional modalities can be implemented for specific purposes of mental preparation.
Exploring the impact of immersive virtual reality on depression knowledge and stigma reduction: a cross-over RCT fMRI study
Authors: Lem Wey Guan , Kawata Kelssy Hitomi dos Santos , Oyama Hiroshi
Published: March 2024

The stigma of mental illness is a form of negative judgmental knowledge and is a barrier to individual seeking treatment. Contact-based educational interventions with first-person perspective (1PP) combined with immersive virtual reality (IVR) is promising as an anti-stigma intervention. This study aims to investigate the effectiveness of 1PP anti-stigma IVR intervention compared to video in enhancing depression knowledge and reducing stigma, as well as to examine the corresponding depression knowledge brain activity change using functional magnetic resonance imaging (fMRI). Participants engaged in a 1PP anti-stigma intervention using both IVR and conventional video, focusing on the daily life and recovery of a patient with mild depression. The change in depression knowledge, stigma-related behavioral, and brain activity using fMRI were measured at pre- and post-interventions. Depression knowledge improved for both interventions; however, only the IVR intervention reduced stigma. In the IVR intervention, depression knowledge score was positively associated with neural response in the right superior frontal gyrus activation, indicative of empathic concern. Conversely, the video intervention correlated with increased activity in the right anterior insula, suggesting a distress-related response. The findings demonstrate that the immersive nature of IVR can reduce stigma more effectively than video intervention. This effectiveness is underpinned by the change in depression knowledge on neural activity, with IVR fostering empathy-related behavioral responses. The results highlight the potential of IVR in enhancing empathic understanding and reducing stigma towards mental illness, emphasizing the need for further exploration into immersive technologies for mental health education.
Migraine headache (MH) classification using machine learning methods with data augmentation
Authors: Khan Lal , Shahreen Moudasra , Qazi Atika , Jamil Ahmed Shah Syed , Hussain Sabir , Chang Hsien-Tsung
Published: March 2024

Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing techniques that use subjective pain intensity measures are insufficiently accurate to make a reliable diagnosis. Even though headaches are a common condition with poor diagnostic specificity, they have a significant negative influence on the brain, body, and general human function. In this era of deeply intertwined health and technology, machine learning (ML) has emerged as a crucial force in transforming every aspect of healthcare, utilizing advanced facilities ML has shown groundbreaking achievements related to developing classification and automatic predictors. With this, deep learning models, in particular, have proven effective in solving complex problems spanning computer vision and data analytics. Consequently, the integration of ML in healthcare has become vital, especially in developing countries where limited medical resources and lack of awareness prevail, the urgent need to forecast and categorize migraines using artificial intelligence (AI) becomes even more crucial. By training these models on a publicly available dataset, with and without data augmentation. This study focuses on leveraging state-of-the-art ML algorithms, including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DST), and deep neural networks (DNN), to predict and classify various types of migraines. The proposed models with data augmentations were trained to classify seven various types of migraine. The proposed models with data augmentations were trained to classify seven various types of migraine. The revealed results show that DNN, SVM, KNN, DST, and RF achieved an accuracy of 99.66%, 94.60%, 97.10%, 88.20%, and 98.50% respectively with data augmentation highlighting the transformative potential of AI in enhancing migraine diagnosis.
eHealth tools use and mental health: a cross-sectional network analysis in a representative sample
Authors: Ochnik Dominika , Cholewa-Wiktor Marta , Jakubiak Monika , Pataj Magdalena
Published: March 2024

eHealth tools usage is vital for health care systems and increased significantly after the COVID-19 pandemic, which aggravated mental health issues. This cross-sectional study explored whether sociodemographic characteristics and mental health indices (stress and symptoms of anxiety and depression) were linked to the behavioral intention to use eHealth tools and eHealth tools usage in a representative sample from Poland using a network approach. Measurements were conducted in March 2023 among 1000 participants with a mean age of 42.98 (18–87) years, with 51.50% women. The measures included the behavioral intention to use eHealth tools (BI) based on the UTUAT2; eHealth tool use frequency (use behavior) including ePrescription, eSick leave, eReferral, electronic medical documentation (EMD), Internet Patient Account (IKP), telephone consultation, video consultation, mobile health applications, and private and public health care use; and the PSS-4, GAD-2, and PHQ-2. Furthermore, sociodemographic factors (sex, age, children, relationship status, education, and employment) were included in the research model. Network analysis revealed that mental health indices were weakly related to eHealth tools use. Higher stress was positively linked with mobile health application use but negatively linked to video consultation use. Use of various eHealth tools was intercorrelated. Sociodemographic factors were differentially related to the use of the eight specific eHealth tools. Although mental health indices did not have strong associations in the eHealth tools use network, attention should be given to anxiety levels as the factor with the high expected influence.
Associations of concentrations of eight urinary phthalate metabolites with the frequency of use of common adult consumer and personal-care products
Authors: Jang Eun A , Kim Kyu Nam , Bae Sang Hyuk
Published: March 2024

This study analyzed the relationship between urine concentrations of phthalate metabolites (UCOM) and personal care products (PCPs) used in adults and examined the change in UCOM according to the usage frequency of PCPs based on raw data from the 3rd Korean National Environmental Health Survey conducted between 2015 and 2017. The relationship between PCP use frequency and UCOM was analyzed using multiple regression analysis, adjusting for baseline factors. The regression model consisted of a Crude Model with log-transformed UCOM before and after adjustment for urine creatinine concentrations. Model 1 was additionally adjusted for age, sex, and obesity, while Model 2 was additionally adjusted for smoking, alcohol consumption, pregnancy history, average monthly income of the household, and PCP exposure within the past 2 days. PCP usage frequency was significantly associated with the UCOM without adjustment for urine creatinine and correlated with demographic characteristics, urine creatinine concentration, and PCP exposure within the past 2 days. This study on exposure to urinary phthalates will play a crucial role in Korean public health by aligning with the fundamentals of research priorities and providing representative data on phthalate exposure for conducting population-level studies.
Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms
Authors: Zhang Yufeng , Li Gang
Published: March 2024

Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.
Rational design of an artificial tethered enzyme for non-templated post-transcriptional mRNA polyadenylation by the second generation of the C3P3 system
Authors: Le Boulch Marine , Jacquet Eric , Nhiri Naïma , Shmulevitz Maya , Jaïs Philippe H.
Published: March 2024

We have previously introduced the first generation of C3P3, an artificial system that allows the autonomous in-vivo production of mRNA with m7GpppN-cap. While C3P3-G1 synthesized much larger amounts of capped mRNA in human cells than conventional nuclear expression systems, it produced a proportionately much smaller amount of the corresponding proteins, indicating a clear defect of mRNA translatability. A possible mechanism for this poor translatability could be the rudimentary polyadenylation of the mRNA produced by the C3P3-G1 system. We therefore sought to develop the C3P3-G2 system using an artificial enzyme to post-transcriptionally lengthen the poly(A) tail. This system is based on the mutant mouse poly(A) polymerase alpha fused at its N terminus with an N peptide from the λ virus, which binds to BoxBr sequences placed in the 3′UTR region of the mRNA of interest. The resulting system selectively brings mPAPαm7 to the target mRNA to elongate its poly(A)-tail to a length of few hundred adenosine. Such elongation of the poly(A) tail leads to an increase in protein expression levels of about 2.5–3 times in cultured human cells compared to the C3P3-G1 system. Finally, the coding sequence of the tethered mutant poly(A) polymerase can be efficiently fused to that of the C3P3-G1 enzyme via an F2A sequence, thus constituting the single-ORF C3P3-G2 enzyme. These technical developments constitute an important milestone in improving the performance of the C3P3 system, paving the way for its applications in bioproduction and non-viral human gene therapy.
Comparative analysis of hemoglobin, potassium, sodium, and glucose in arterial blood gas and venous blood of patients with COPD
Authors: Tang Sitian , Mei Zhu , Huang Dongmei , Liu Ling , Yang Lunyu , Yin Dan , Hu Liyi
Published: March 2024

The study aims to assess the accuracy of the arterial blood gas (ABG) analysis in measuring hemoglobin, potassium, sodium, and glucose concentrations in comparison to standard venous blood analysis among patients diagnosed with chronic obstructive pulmonary disease (COPD). From January to March 2023, results of ABG analysis and simultaneous venous blood sampling among patients with COPD were retrospectively compared, without any intervention being applied between the two methods. The differences in hemoglobin, potassium, sodium, and glucose concentrations were assessed using a statistical software program (R software). There were significant differences in the mean concentrations of hemoglobin (p < 0.001), potassium (p < 0.001), and sodium (p = 0.001) between the results from ABG and standard venous blood analysis. However, the magnitude of the difference was within the total error allowance (TEa) of the United States of Clinical Laboratory Improvement Amendments (US-CLIA). As for the innovatively studied glucose concentrations, a statistically significant difference between the results obtained from ABG (7.8 ± 3.00) mmol·L−1 and venous blood (6.72 ± 2.44) mmol·L−1 was noted (p < 0.001), with the difference exceeding the TEa of US-CLIA. A linear relationship between venous blood glucose and ABG was obtained: venous blood glucose (mmol·L−1) =  − 0.487 + 0.923 × ABG glucose (mmol·L−1), with R2 of 0.882. The hemoglobin, potassium, and sodium concentrations in ABG were reliable for guiding treatment in managing COPD emergencies. However, the ABG analysis of glucose was significantly higher as compared to venous blood glucose, and there was a positive correlation between the two methods. Thus, a linear regression equation in this study combined with ABG analysis could be helpful in quickly estimating venous blood glucose during COPD emergency treatment before the standard venous blood glucose was available from the medical laboratory.