PLOS ONE
Retraction: SHP2 improved Late-onset fetal growth restriction via modulating ROS/BRD4/PI3K/YAP/PIGF signaling induced angiogenesis
by The PLOS One Editors
arXiv 등 학술 논문. CC-BY 라이선스로 자유 재사용 가능 — 출처표시 시 상업 사용 OK.
총 453건
by The PLOS One Editors
by The PLOS One Editors
by The PLOS One Editors
by Hriddhi Sarker, Farhad Bin Farid, Marguba Kamrun, Esha Masud, Asif Ahmed, Mamun Miah, Neladre Shaker Roy, Neeraj Kumar, Md Ahad Ali Cancer is characterized as a multifactorial disease due to their complex genetic and molecular mechanisms that often converge across tissue types. Shared oncogenic pathways can help us understand these functions and discover broad-spectrum therapeutics. Earlier, most studies focused on finding specific drivers for individual cancer types. However, researchers are now more interested in identifying common molecular patterns across different cancers and developing therapies that can target multiple pathways at once. This study aimed to understand the common oncogenic pathways between breast, ovarian and colorectal (BOC) cancers and identify possible multitargeted therapeutic drug molecules. To identify the common differentially expressed genes (DEGs), we analyzed three transcriptomic datasets and found a total of 128 DEGs. The protein-protein interaction (PPI) network study reveals the top-ranked, most significant hub targets, AURKA, CDK1 and CCNB1, as drug targets. Enrichment analysis with GO and KEGG pathways, as well as regulatory network (TFs and mRNAs) analysis, revealed common pathogenetic processes among BOC cancers. The AMG-900 exhibits the highest binding affinity scores of −10.8, −9.40, and −9.7 kcal/mol with the target proteins AURKA, CCNB1, and CDK1, respectively. The stability and structural flexibility of the selected protein-ligand complexes were validated by a large-scale (500 ns) molecular dynamics and MM-GBSA analyses, and the results indicate stable interactions for AURKA and CCNB1, while CDK1 showed comparatively reduced stability. The pharmacokinetic analysis revealed favorable drug-likeness and a manageable toxicity profile typical of anticancer agents. Therefore, the findings of this study propose that AMG-900 may serve as a promising multi-targeted candidate for further investigation in multi-target therapeutic strategies within precision oncology. Furthermore, these results require additional experimental (in vivo and in vitro) and clinical validation to confirm the potentiality and efficiency of this (AMG-900) lead compound.
by José Alejandro Valdevila Figueira, María Alejandra Espinoza de los Monteros Andrade, Xavier Rodrigo Yambay-Bautista, Andrés Ramírez, Indira Dayana Carvajal Parra, Rocío Valdevila Santiesteban, María José Pico, Jose A. Rodas Background Alzheimer’s disease represents one of the greatest healthcare challenges of the 21st century due to the aging population and its impact on the quality of life of patients and their families. Preparing future healthcare professionals to address this condition is crucial. Aims This article analyzes the level of knowledge about Alzheimer’s disease held by university students in medicine, nursing, and psychology, highlighting the differences and similarities between disciplines and proposing strategies to improve training in this field. Methods A cross-sectional study was conducted with a convenience sample of 1,023 Ecuadorian students: nursing (n = 727, 71.1%), medicine (n = 170, 16.6%), and psychology (n = 126, 12.3%). Participants completed the Alzheimer’s Disease Knowledge Scale (ADKS) and a demographic survey. The percentage of correct answers on the ADKS was used to assess knowledge levels. Results The overall percentage of correct answers was 54.68%, indicating a limited level of knowledge. Medical students obtained the highest mean score (17.44 [SD: 2.864]), followed by psychology (16.28 [SD: 2.348]) and nursing (16.18 [SD: 2.649]). A weak but significant correlation was found between knowledge level and prior contact with people with dementia (P < 0.001). Conclusions Students across all disciplines demonstrated a broad knowledge gap regarding Alzheimer’s disease, although medical students obtained slightly higher scores than psychology and nursing students. The findings highlight the need for improved educational training and curriculum development to enhance dementia knowledge, especially in psychology and nursing programs.
by Daniele Capitanio, Silvia Mercurio, Ettore Mosca, Cristina Battaglia, Marco Venturin, Roberta Pennati Tunicates, including ascidians, are recognized as the true ‘sister group’ of vertebrates and are emerging as models to study larval and post-larval development, including degeneration of central nervous system (CNS), in chordates. Ascidian larvae have the typical chordate body plan that includes a dorsal neural tube. During their metamorphosis, a deep tissue reorganization takes place, with some tissues that degenerate while others develop to become functional during the adult life. The larval CNS also degenerates, and most neurons disappear, making room for the formation of adult CNS. The genome of the ascidian Ciona intestinalis has been sequenced and annotated, with several CNS specific genes that have been characterized. These features make ascidian metamorphosis a good model to study the mechanisms underlying physiological CNS degeneration. To shed light on the molecular determinants of C. intestinalis metamorphosis, we analyzed the proteome at three stages of development: swimming larva (SwL, Hotta stage 28), settled larva (SetL, Hotta stage 32) and metamorphosing larva (MetL, Hotta stage 34). A total of 405 modulated proteins were identified by mass spectrometry by comparing the three stages. Enrichment and network analysis showed the involvement of several processes/pathways, including autophagy and mTOR pathways, and actin cytoskeleton organization and remodeling among the most significant ones. This study helps to elucidate the molecular processes underlying ascidian metamorphosis and provides insight into mechanisms of physiological neurodegeneration in ascidians.
by Wenfang Wang, Xiaoxue Zhang, Hui Yu, Zhanli Wang In order to establish an efficient method for high-throughput detection of Yersinia pestis (Y. pestis), a quadruple target real-time polymerase chain reaction assay was developed based on the specific target genes of Y. pestis (caf1, pla, ymt, and ypo-1094). Its sensitivity, specificity, and repeatability were evaluated, and clinical serum samples were tested by the established method. The results showed that no cross-reactivity was observed with other bacterial nucleic acids. The optimal linear detection range for caf1, pla, ymt, and ypo-1094 was 12.09 × 10-⁶-12.09 × 101 ng/μL, and the lower limit of detection was 12.09 × 10−4 ng/μL. Four different DNA concentrations of caf1, pla, ymt, and ypo-1094 (10−4, 10−3, 10−2, 10−1and 101 ng/μL) were tested five times, achieving good repeatability. In the clinical sample detection, all the Y. pestis – positive samples were identified. The established method has potential for clinical use for rapid detection of Y. pestis with high specificity and high sensitivity.
by Jiafu Yang, Sicheng Wang, Xin Chen, Liping Liu, Yangkang Li Background Esophageal cancer ranks among the most lethal malignancies worldwide, particularly prevalent in the Guangdong–Chaoshan region of China due to regional dietary habits. Cytokeratin 19 (CK19) is an important immunohistochemical marker reflecting tumor invasiveness and metastatic potential; however, noninvasive preoperative prediction of CK19 expression remains unavailable. This study aimed to develop a CT-based radiomics model combined with machine learning to predict CK19 expression preoperatively. Methods This study included 134 patients with primary esophageal cancer. All patients underwent enhanced CT scans before surgery, and CK19 expression was evaluated by pathological analysis after surgery. Radiomics technology was used to extract multidimensional image features including shape, texture, and first-order features from CT images. A prediction model was established by combining machine learning models such as gradient boosted decision tree (GBDT), random forest (RF), extreme gradient boosting (XGB), and lightweight gradient boosting machine (LGBM), and the interpretability of the model was analyzed by SHAP value. Results The random forest model showed relatively higher accuracy and precision among the compared models, with an AUC value of 0.6765 and an accuracy of 0.8293. GBDT demonstrated a more balanced performance (AUC: 0.6597), while XGB (AUC: 0.6744) and LGBM (AUC: 0.6807) showed comparable but overall slightly lower discriminative ability. Feature importance analysis showed that the features after wavelet transformation made a significant contribution to the prediction results. The results verified the potential of radiomics combined with machine learning technology in the preoperative prediction of CK19 expression. Conclusion This study developed a preoperative noninvasive prediction model based on radiomics and machine learning, which showed modest predictive performance in an exploratory setting in the evaluation of CK19 markers in patients with esophageal cancer, may provide preliminary support for further exploration in precision medicine. In the future, the clinical applicability of this model needs to be further verified and its promotion and application in a larger population needs to be optimized.
by Roberta Misuraca, Maria Carella
by Baorong Guo, Zaifeng Wu, Quan Wen, Yifeng Peng
by Ihssan S. Masad, Khalid A. Rabaeh, Samer I. Awad, Akram A. Almousa, Ahmed M. Masawi, M. Abdullah Al Kafi, Samer M. Alheet, Abdallah J. Nofal, Belal Moftah
by Frida Torell, Robin Rohlén, Michael Dimitriou Mechanical perturbations applied to the arm can elicit reflexive actions. These rapid corrective responses include the stretch reflex, which consists of different components: the short-latency reflex (SLR) and the early and late long-latency reflex (LLR). In this study, we examine how different task factors dynamically influence these reflex components in the context of a specific delayed-reach paradigm. Using multiple linear regression (MLR), we analysed electromyographic (EMG) activity from seven muscles actuating the right arm to examine the effects of mechanical load, preparatory delay, perturbation and target direction, on reflex responses, as well as two-factor interactions. The MLR analysis shows that our delayed-reach tasks engaged shoulder girdle muscles in a task-dependent manner, whereas the biceps and triceps primarily acted as stabilizing muscles, with rapid responses triggered regardless of perturbation direction. Specifically, our analyses show that the earliest corrective response, the SLR, exhibited some task-dependent modulation particularly in muscles of the shoulder girdle, although background (pre-)loading decreased this modulation. The SLR was primarily influenced by the main factors Load and Perturbation, along with the interaction Load × Perturbation. Perturbations aligned with the load direction were associated with increased EMG activity across all examined muscles. While there was a small but significant effect of load during the early LLR, this effect diminished by the late LLR epoch. Task-dependent modulation was most pronounced at the late LLR epoch, suggesting greater top-down modulation of this reflex component. In particular, the late LLR was shaped by the factors Perturbation and Target, as well as the interaction Perturbation × Target. Targets and perturbations in opposite directions resulted in heightened EMG activity, and shoulder muscles exhibited stronger LLR responses for targets located farther along the muscle shortening direction. Our results complement and expand on previous findings concerning stretch reflex modulation and help guide the design of future studies.
by Wyatt H. Bridgman, Cosmin Safta, Jaideep Ray In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.
by Ieva Kubiliute, Edgaras Zaboras, Fausta Majauskaite, Jurgita Urboniene, Birute Zablockiene, Giedre Gefenaite, Aukse Mickiene, Ligita Jancoriene Background Since its emergence, the COVID-19 infection has led to significant morbidity and mortality worldwide. Early identification of patients at risk for severe disease is essential for more effective triage, timely therapeutic intervention, and optimal resource allocation. Differences in population characteristics may contribute to variability in disease outcomes, which emphasizes the need for regional-level data, especially from underrepresented regions. The main aim of this study was to identify the demographic, clinical, and laboratory predictors of severe COVID-19, defined as the need for oxygen therapy, in Lithuania. Materials and methods We conducted an ambispective observational cohort study at Vilnius University Hospital Santaros Klinikos in Vilnius, Lithuania, from March 2020 to December 2021. Adult patients with a confirmed diagnosis of COVID-19 and hospitalized longer than 24 hours were included in this study. Data were collected from the electronic medical records and patient interviews. To identify predictors of severe COVID-19 course, a multivariable binary logistic regression model was performed. Results Among 495 patients, 52.9% were male, the median age was 55 years, and 61.2% had at least one underlying condition. The most common symptoms on admission were malaise (77.1%), subfebrile fever (65.9%), and cough (69.7%). CRP demonstrated the highest predictive value for severe COVID-19 (AUC = 0.84), followed by LDH (AUC = 0.80). Older age (OR 1.04 per year, 95% CI 1.00–1.08), obesity (OR 3.55, 95% CI 1.35–9.30), lymphopenia (OR 3.70, 95% CI 1.37–9.99), higher LDH (OR 1.008, 95% CI 1.00–1.01) and CRP (OR 1.021, 95% CI 1.01–1.04) levels were identified as the strongest predictors for severe COVID-19 disease course. Conclusion Older age, obesity, lymphopenia, and higher CRP and LDH were associated with developing severe COVID-19 disease, indicating that combining patient history and laboratory parameters can provide a practical risk stratification approach to help clinicians identify high-risk patients early upon hospitalisation.
by Tanja Tomašević, Ivana Radić, Vesna Mijatović Jovanović, Snežana Ukropina, Dragana Milijašević, Sonja Čanković, Radmila Petrović, Vladimir Petrović Introduction Alcohol use has been linked to harmful health outcomes. In this study, we examined the association between binge drinking and sociodemographic characteristics as well as health-related lifestyle factors among the adult population in Autonomous Province of Vojvodina (APV). Materials and methods The data for this study were used from the newly established “Surveillance of Behavioural Risk Factors for Non-Communicable Diseases in Vojvodina” (SBRF-NCD-V) system in APV, Serbia. This cross-sectional study involved 3910 healthcare users aged 18 years and older, interviewed in 2024, across all 44 Primary Healthcare Centers in APV. The questionnaire used was adapted from the BRFSS instrument developed by the CDC. A multivariable binary logistic regression model analysed associations between binge drinking and sociodemographic, as well as health-related factors, stratified by gender to calculate odds ratios. Results The prevalence of binge drinking in Vojvodina was high (19.3%). Higher odds of binge drinkers were recorded in males (OR=4.3; 95% CI = 3.5–5.2; p < 0.001), in younger categories compared to age groups 65 and over (p < 0.001), never married females compared to married/living with a partner (OR=2.1; 95% CI = 1.4–3.5; p = 0.002), females with poor/very poor material status (OR=2.1; 95% CI = 1.2–3.9; p = 0.014), females who were former smokers (OR=1.8; 95% CI = 1.1–3.3; p = 0.033) or smokers (OR=2.8; 95% CI = 2.3–3.5; p < 0.001) compared to non-smokers, males who were not physically inactive (OR=1.7; 95% CI = 1.3–2.2; p < 0.001). Conclusion Factors contributing to the high prevalence of binge drinking in APV, Serbia, were gender, younger age, never-married status in females, worse material status in females, former smoking in females, smoking for both genders, and physical activity in males. Introducing a continuous sub-national surveillance system could significantly improve the monitoring of factors associated with alcohol use prevalence. Identified sociodemographic factors could help health workers in primary care settings to screen and support patients at risk of, or engaged in risky alcohol use.
by Rana Muhammad Zahid Mushtaq, Naeem Rasool, Zaka Ur Rehman, Usama Bin Naeem, Muhammad Azeem, Tehreem Fayyaz, Mehmood Ahmad, Waqas Ahmad Background Psychological well-being of pharmacists is necessary for safe and effective healthcare delivery, particularly in high-stress environments. The present study was aimed to investigate the influence of job content on psychological well-being among the registered pharmacists in Pakistan along with the mediating roles of work identity and job crafting. Methods Data were collected from 564 licensed pharmacists on standardized instruments for psychological well-being (Ryff’s PWB Scale), job content (Karasek’s JCQ), work identity, and job crafting behaviors. Structural equation modeling using RStudio tested the direct, indirect, and serial mediation effects, with bootstrapped 95% confidence intervals. Results Confirmatory factor analysis indicated acceptable model reliability (e.g., Job Crafting α = 0.94, CR = 0.96; PWB α = 0.66, CR = 0.85). Bivariate correlations showed that psychological well-being was moderately associated with job crafting (r = 0.49, p < 0.01) and strongly with work identity (r = 0.57, p < 0.01). SEM analysis showed that Job content had no significant direct effect on well-being (β = −0.0362, p = 0.56), two significant indirect effects were observed: via work identity (β = 0.1397, p < 0.05) and job crafting (β = 0.1295, p < 0.05). The sequential pathway; Job content → work identity → job crafting → psychological well-being also yielded a smaller but significant effect (β = 0.0278, p < 0.05). Conclusion Our findings suggest that work identity and job crafting may mediate the relationship between job content and psychological well-being; however, these results should be interpreted cautiously given the model fit limitations.
by Tomofumi Yamazaki, Seiichiro Ito, Yoko Ino, Kana Sugishita, Koichi Kageyama, Mugita Sato, Satoshi Nakao, Kazuya Nonomura, Hirofumi Tamaki, Kazuhiro Iguchi, Mitsuhiro Nakamura Geographic disparities in access to health services are a growing concern in Japan as population aging and decline increase care needs and as resources concentrate in dense urban cores. Focusing on Tokyo Metropolis as a large and internally heterogeneous urban region, we used geographic information systems to evaluate spatial proximity to healthcare-related facilities—pharmacies, hospitals, clinics, dental clinics, and elderly welfare facilities—together with public transportation infrastructure. For pedestrian access, we calculated population coverage from 400 m to 3200 m Euclidean buffers around each facility. For transportation-related proximity, we calculated the proportion of facilities located within 250 m to 3000 m buffers around public transportation features (bus stops, bus routes, and railway stations). Bus stop-based facility–transportation proximity was consistently high across facility types in both the 23 special wards and the Tama region, whereas railway-station proximity displayed larger spatial variation between areas. Interpreted as an indicator for walkable proximity rather than effective service access, these results highlight where transportation connectivity and facility locations align or diverge. These findings underscore the necessity for healthcare and urban planning strategies that integrate local characteristics with transportation infrastructure.
by Weeraphol Saengpanya, Ratchaneekorn Upasen Thinking Ability (THI) and Creativity Quotient (CQ) equips students to navigate a complex, fast-changing world. These cognitive functions drive problem-solving, innovation, and adaptability, essential for academic and personal success. In the basic education landscape (elementary and secondary levels), schools worldwide are focusing on promoting core competencies among students. The comparison for THI and CQ based on students’ demographic characteristics is little known. This study examines the levels and demographic variations of THI and CQ among basic education students in Thailand, using a learning-sufficiency framework and key of cognitive and creative developmental theories to explain their development across diverse contexts. A multi-stage random sampling method was employed, involving a total of 1,494 students from schools across various regions. Participants completed a demographic characteristics questionnaire as well as the THI and CQ scales. The results show that the level of THI and CQ among the participants were low to moderate in total and had a statistically significant difference (p < 0.05) when compared based on the demographic characteristics. The study advances theory by providing evidence of distinct patterns across demographic groups. Key implications include adopting stage-appropriate instruction, integrating inquiry- and creativity-based pedagogies, and promoting equity-focused policies to reduce demographic disparities in cognitive and creative development. These findings provide guidance for teachers, educational psychologists, curriculum developers, school leaders, and administrators in strengthening students’ THI and CQ in accordance with their demographic characteristics.
by Takehiro Kosaka, Keitaro Kubo This study aimed to examine the effects of muscle-tendon mechanical properties and electromyographic activity patterns on individual differences in the force-power relationship during jumping with and without countermovement. Twenty men executed unilateral jumps using only ankle joint under the following conditions: no-countermovement jump (noCMJ) and countermovement jump (CMJ) with five different loads (0, 10, 30, 50, and 70% of 1 repetition maximum (RM)). During concentric phase of each jump, mean power and electromyographic activities were measured. In addition, the power ratio of higher load conditions (50% and 70% 1RM) to lower load conditions (0% and 10% 1RM) was calculated as an indicator of individual differences in the force-power relationship. Active muscle stiffness of medial gastrocnemius muscle was calculated according to changes in estimated muscle force and fascicle length during fast stretching at three different angular velocities (100, 300, and 500 deg·s-1) after submaximal isometric contractions. Tendon stiffness was measured during ramp and ballistic contractions. For noCMJ and CMJ, active muscle stiffness at all angular velocities and the ratios of electromyographic activities were not significantly correlated with the power ratio. Tendon stiffness measured during ramp and ballistic contractions was significantly correlated with the power ratio for noCMJ, but not CMJ. In conclusion, individual differences in the force-power relationship during jumping without countermovement are associated with the tendon mechanical properties, whereas those with countermovement are not related to the muscle-tendon mechanical properties and electromyographic activity patterns.
by Chen Ying, Yong Jin Kim China is both a major producer and consumer of fresh agricultural products, making cold chain logistics essential for preserving quality and reducing post-harvest loss. However, insufficient pre-cooling capacity in production areas often leads to significant quality deterioration during the first-mile stage, which has not been fully addressed in existing cold chain network design studies. To bridge this gap, this study proposes an integrated optimization framework for designing a first-mile pre-cooling distribution center (DC) network. A multi-objective nonlinear mathematical model is developed to simultaneously minimize total logistics cost and maximize product freshness. To better characterize perishability, a stage-specific freshness decay function captures the nonlinear deterioration of products before and after pre-cooling. Transportation-related carbon emissions are also incorporated to enhance environmental relevance. Given the complexity of the location-routing problem, a genetic algorithm (GA) is used to obtain Pareto-optimal solutions. An empirical case study in Shandong Province, China, is conducted under three scenarios: (1) no pre-cooling, (2) decentralized pre-cooling at origins, and (3) centralized pre-cooling at regional DCs. Results show that the centralized strategy achieves superior performance, reducing total daily cost by 3.79% and producing the lowest freshness loss compared with the no-pre-cooling baseline. In contrast, decentralized origin-side pre-cooling improves freshness preservation but increases total cost by 5.27% due to higher equipment investment and weaker route efficiency. These findings demonstrate that an integrated location-routing perspective can provide more effective first-mile cold chain planning than treating pre-cooling as an isolated facility decision.