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Human umbilical cord mesenchymal stem cell-derived exosomes mitigate acute radiation-induced intestinal oxidative damage via the Nrf2/HO-1/NQO1 signaling pathway
Acute radiation-induced intestinal injury (ARII), a prevalent complication of abdominal radiotherapy, remains clinically challenging due to limited therapeutic options. This study demonstrates the therapeutic efficacy of human umbilical cord mesenchymal stem cell-derived exosomes (hucMSC-Exos) in mitigating ARII through Nrf2/HO-1/NQO1 pathway activation. In a rat model receiving 12 Gy abdominal irradiation, systemic hucMSC-Exos administration significantly restored intestinal mucosal integrity and reduced oxidative damage markers. Mechanistically, hucMSC-Exos potentiated the antioxidant axis by upregulating Nrf2 signaling, as evidenced by histopathological, biochemical, and molecular analyses. Complementary in vitro experiments revealed hucMSC-Exos protected irradiated IEC-6 cells from oxidative dysfunction while enhancing proliferation, effects substantially attenuated upon Nrf2 silencing via siRNA. These findings establish that hucMSC-Exos orchestrate redox equilibrium through targeted Nrf2 pathway modulation, effectively counteracting radiation-induced enterocyte apoptosis. The elucidated mechanism expands the therapeutic paradigm of MSC-derived exosomes in radioprotection and provides a clinically translatable strategy for managing ARII in oncological radiotherapy.
Comparative sustainability investigation on a novel industrial-waste-based soil stabilizer and cement based on life cycle assessment
Parental experience of the diagnostic process and its role in the decision to terminate pregnancy due to fetal abnormality; A qualitative interview study
Introduction The detection of fetal malformation is a shock to expectant parents and often initiates a diagnostic process of additional tests and ultrasound scans, that may be uncertain and stressful to the parents. The aim of the present study was to investigate how expectant parents experienced the diagnostic process and how their decision to terminate the pregnancy was reached during that process. Methods Semi-structured interviews with 11 Danish women and nine male partners one to five months after termination of pregnancy. All interviews were conducted in the participants’ homes and lasted 97–135 minutes. Thematic analysis was performed. Results 1) The theme, ‘In no man’s land’, describes the two-phased diagnostic process: First, the initial shock of a potential ultrasound finding, and the uncertain – but still hopeful – days of waiting for a follow-up scan and specialist consultation. Second, the feeling of professionalism and companionship when interacting with the fetal medicine specialists, who still could not always provide the hoped-for answers. 2) The theme, ‘Inescapable decision’, describes how decision-making oscillated as new information or potential interpretations entered the diagnostic process. The participants described a continuous contemplation of the inevitable final choice regarding continuation or termination. Being in this process – for days or weeks – was described as an emotional rollercoaster with feelings of both hope and despair until the final decision to terminate was made. Conclusion During a prenatal diagnostic process parents must endure uncertainty, waiting times and an ongoing oscillation between hope and no-hope for the pregnancy. However, the diagnostic process may also be understood as an opportunity for dialogue, reflection and adjustment, allowing for a personal and well-considered decision, even if painful.
Air quality prediction based on factor analysis combined with Transformer and CNN-BILSTM-ATTENTION models
Examining Chat GPT with nonwords and machine psycholinguistic techniques
Strings of letters or sounds that lack meaning (i.e., nonwords) have been used in cognitive psychology and psycholinguistics to provide foundational knowledge of human processing and representation, and insights into language-related performance. The present set of studies used the machine psycholinguistic approach (i.e., using nonword stimuli and tasks similar to those used with humans) to gain insight into the performance of Chat GPT in comparison to human performance. In Study 1, Chat GPT was able to provide correct definitions to many extinct words (i.e., real English words that are no longer used). In Study 2 the nonwords were real words in Spanish, and Chat GPT was prompted to provide a word that sounded similar to the nonword. Responses tended to be Spanish words unless the prompt specified that the similar sounding word should be an English word. In Study 3 Chat GPT provided subjective ratings of wordlikeness (and buyability) that correlated with ratings provided by humans, and with the phonotactic probabilities of the nonwords. In Study 4, Chat GPT was prompted to generate a new English word for a novel concept. The results of these studies highlight certain strengths and weaknesses in human and machine performance. Future work should focus on developing AI that complements or extends rather than duplicates or competes with human abilities. The machine psycholinguistic approach may help to discover additional strengths and weaknesses of human and artificial intelligences.
Early replacement of re-induction therapy following failed intensive induction treatment enhances the therapeutic efficacy of newly diagnosed AML
Long-COVID is associated with increased absenteeism from work
Long-COVID, defined as COVID-19 symptoms persisting for more than 3 months, may lead to persistent health issues requiring extensive medical care. Despite its long-term health impact, the economic impact of long-COVID remains understudied. This study examined whether individuals with long-COVID had more missed workdays compared to those without long-COVID. Adults (≥18 years old) with full-time jobs were identified from the 2022 Full-Year Population Characteristics file of the Medical Expenditure Panel Survey (MEPS). A weighted two-part model was used to identify factors associated with missed workdays due to illness. The total population analyzed included 131,685,516 adults (unweighted n = 8,210), with an average (SD) age of 43 (14) years. Among them, 46% were female and 62% were non-Hispanic White. Approximately 7% of the population experienced long-COVID. Individuals with long-COVID reported an average of 8 days missed from work (SD: 12 days), while those without long-COVID reported an average of 4 days missed (SD: 9 days). The two-part model revealed that individuals with long-COVID had 2.54 more missed workdays compared to those without long-COVID (p < 0.01), after controlling for relevant variables. These results underscore significant productivity losses associated with long-COVID, highlighting the need for policymakers and employers to implement effective strategies to address this condition.
Biomechanics characterization of an implantable ultrathin intracortical electrode through finite element method
Research on social bot identification through behavioral feature analysis
Accurately identifying social bot accounts is the key to preventing the use of artificial intelligence technology to forge social accounts, which can interfere with public opinion and thus cause public opinion crises. However, at present, relying only on manual identification of bot accounts has the challenges of low efficiency, high cost, and low accuracy, while existing research on batch identification of social bots lacks research on the system of behavioural characteristics of social bots, and thus lacks the construction of a model for the analysis of the behavioural characteristics of social bots. In this paper, we propose a diverse set of behavioural features for social robots based on the differences between the behavioural features of social robot accounts and normal users. The feature selection method based on OOB estimation is chosen for excluding redundant features in the constructed feature set; meanwhile, Random Forest, as a combined classification method, overcomes the problem of limitations of decision boundaries when classifying with a single decision tree, and has the characteristics of high accuracy, fast speed and stable performance. Through experiments, this paper applies it to the construction of social robot recognition model for detecting robot accounts in social platforms. The experiments prove that the effective indicators screened by the feature selection method based on OOB estimation can help improve the stability of the model. Specifically, the filtered features contribute about 20% more to the model accuracy and F1 score than other features. The social robot recognition model constructed based on random forest has higher accuracy and stability compared to the decision tree model and neural network model. Specifically, the accuracy rate is about 5% higher than other models, and other indicators are also better than other models. The experimental results show that the feature selection method based on OOB estimation and the random forest model show excellent performance in the experiments of social robot recognition, which can meet the requirements of the actual social robot recognition research and can be applied to the practical scenarios of robot account detection on social platforms.
Duloxetine deteriorates prefrontal noradrenergic pain facilitation, but reduces locus coeruleus activity to restore endogenous analgesia in chronic neuropathic pain state
The role of S100A9 as a diagnostic and prognostic biomarker in septic shock
Background Sepsis is a severe and potentially fatal systemic condition marked by excessive immune defense against infection. Within this research, we explored the serum concentration of S100A9 during hospital admission, aiming to assess its role and reliability as a viable biomarker for identifying septic shock and predicting mortality risk in sepsis. Furthermore, we explored whether combining S100A9 with conventional assessment tools could enhance diagnostic precision and prognostic accuracy, offering valuable insights to support early intervention and personalized treatment strategies for sepsis. Methods This study comprised 575 participants overall, with sepsis patients classified into non-shock and shock groups based on the severity of their condition. Additionally, age- and gender-matched ICU control cohort and healthy control group were recruited to ensure wide applicability and strong comparability of the findings. Enzyme-linked immunosorbent assay utilized for detecting serum S100A9 levels in subjects within 24 hours of admission, ROC curves were used to evaluate the disease identification and prognostic analysis. Differences in survival outcomes among patients with varying levels of S100A9 expression were analyzed using the Kaplan-Meier method. Results Serum S100A9 concentrations were elevated in septic patients upon admission. In the diagnosis of patients with septic shock, S100A9 performed similarly to APACHE II, and a considerable enhancement was noted in the sensitivity of detecting septic shock when S100A9 was combined with lactate and APACHE II. At the initiation of ICU stay, the area under the receiver operating characteristic curve (AUC) for the association between serum S100A9 levels and 28-day mortality was 0.78. This value surpassed the AUCs for IL-6 (0.66), procalcitonin (0.60), lactate (0.58) and C-reactive protein (0.47). Furthermore, septic patients with elevated serum S100A9 levels (≥ 630.77 pg/ml) exhibited lower survival rates compared to those with lower concentrations (< 630.77 pg/ml). Conclusion S100A9 is a promising biomarker for diagnosing septic shock and forecasting clinical outcomes in patients with sepsis. In addition, S100A9 has good predictive efficacy for the risk of death in sepsis patients.
PTPN2 inhibits TG-induced ERS-initiated TNBC apoptosis through the mitochondrial pathway
Prevalence and trends of major congenital anomalies in Brazil: A study from 2011 to 2020
Background Congenital anomalies contribute significantly to morbidity and mortality among newborns and infants. In Brazil, the estimated prevalence of malformations in newborns is < 1%, which is comparatively lower than that recorded in other regions worldwide. This study aimed to analyze the prevalence of congenital anomalies in Brazil over a 10-year period and to identify potential associations of this prevalence with socioeconomic, gestational, and regional factors by performing an analysis using data sourced from the Live Birth Information System (Sistema de Informações sobre os Nascidos Vivos – SINASC) covering the period from 2011 to 2020. Methods From a total population of 29,025,461 live births, we included a cohort of 240,405 newborns with congenital anomalies. For the purpose of this study, we categorized newborns with congenital anomalies into two groups: one group with newborns with a single major malformation and another group with newborns with multiple major malformations (minor malformations not considered). Results The prevalence of congenital anomalies was 8.0 per 1,000 live births, with variations across different years and regions within the country. The Southeast region of Brazil, with the highest human development index, displayed the highest prevalence of congenital anomalies. The most frequent congenital anomalies were limb deformities (29.7%), neural tube defects (14.7%), and heart defects (11.6%). Conclusion The prevalence of major congenital anomalies in Brazil during the study period varied with the geographic region and was lower than that in developed nations, likely due to lower prenatal detection rates and underreporting.
Retrospective analysis of curative rectal cancer surgery outcomes in elderly patients
Integrated decision-control for social robot autonomous navigation considering nonlinear dynamics model
Reinforcement learning (RL) has demonstrated significant potential in social robot autonomous navigation, yet existing research lacks in-depth discussion on the feasibility of navigation strategies. Therefore, this paper proposes an Integrated Decision-Control Framework for Social Robot Autonomous Navigation (IDC-SRAN), which accounts for the nonlinearity of social robot model and ensures the feasibility of decision-control strategy. Initially, inverse reinforcement learning (IRL) is employed to tackle the challenge of designing pedestrian walking reward. Subsequently, the Four-Mecanum-Wheel Robot dynamic model is constructed to develop IDC-SRAN, resolving the issue of dynamics mismatch of RL system. The actions of IDC-SRAN are defined as additional torque, with actual torque and lateral/longitudinal velocities integrated into the state space. The feasibility of the decision-control strategy is ensured by constraining the range of actions. Furthermore, a critical challenge arises from the state delay caused by model transient characteristics, which complicates the articulation of nonlinear relationships between states and actions through IRL-based rewards. To mitigate this, a driving-force-guided reward is proposed. This reward guides the robot to explore more appropriate decision-control strategies by expected direction of driving force, thereby reducing non-optimal behaviors during transient phases. Experimental results demonstrate that IDC-SRAN achieves peak accelerations approximately 8.3% of baseline methods, significantly enhancing the feasibility of decision-control strategies. Simultaneously, the framework enables goal-oriented autonomous navigation through active torque modulation, attaining a task completion rate exceeding 90%. These outcomes further validate the intelligence and robustness of the proposed IDC-SRAN.
Dysregulation of the low-level replication stress response in transformed cell lines
An efficient low-shot class-agnostic counting framework with hybrid encoder and iterative exemplar feature learning
Few-shot learning techniques have enabled the rapid adaptation of a general AI model to various tasks using limited data. In this study, we focus on class-agnostic low-shot object counting, a challenging problem that aims to achieve accurate object counting with only a few annotated samples (few-shot) or even in the absence of any annotated data (zero-shot). In existing methods, the primary focus is often on enhancing performance, while relatively little attention is given to inference time—an equally critical factor in many practical applications. We propose a model that achieves real-time inference without compromising performance. Specifically, we design a multi-scale hybrid encoder to enhance feature representation and optimize computational efficiency. This encoder applies self-attention exclusively to high-level features and cross-scale fusion modules to integrate adjacent features, reducing training costs. Additionally, we introduce a learnable shape embedding and an iterative exemplar feature learning module, that progressively enriches exemplar features with class-level characteristics by learning from similar objects within the image, which are essential for improving subsequent matching performance. Extensive experiments on the FSC147, Val-COCO, Test-COCO, CARPK, and ShanghaiTech datasets demonstrate our model’s effectiveness and generalizability compared to state-of-the-art methods.
The repetition principle of traumatic dreams
SARS-COV-2 mutations in North Rift, Kenya
The rise of new SARS-CoV-2 mutations brought challenges and progress in the global fight against COVID-19. Mutations in spike and accessory genes affect transmission, vaccine efficacy, treatments, testing, and public health strategies. Monitoring emerging variants is crucial to halt re-emergency of the virus and spread. 44 nasopharyngeal/oropharyngeal swabs from Kenyan patients were sequenced with the Illumina platform. Galaxy’s bioinformatic tools were used for genomic analysis. SARS-CoV-2 genome classification was done using PANGOLIN and mutation annotation with the COVID-19 Annotator tool. From this study, 5 clades of SARS-CoV-2 were identified of whom 38 (86%) were BA.1.1; 2 (5%) were BA.1.1.1; 1 (2%) was BA.1; 1 (2%) was BA.1.14 and 2 (5%) were AY.46. Symptomatic patients were 16 out of 18 males and 22 out of 26 females. Out of these, symptomatic patients, BA.1.1 was found in 14 males and 18 females. In these clades we found 53 significant mutations of which 42 were non-synonymous, 10 synonymous, 7 deletions, 4 insertions and 2 extragenic. Out of the 42 non-synonymous mutations, 7 were exclusively found in symptomatic patients. Two new mutations, S:R214R, and NSP2:A555A, were also found and were dominant in symptomatic patients. These findings add to the understanding of the SARS-CoV-2 virus future evolution in the region.