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Retraction: Embracing the digital revolution: Examining the relationship between ICT adoption and carbon emissions in the Persian Gulf
Author Correction: AAV-based delivery of RNAi targeting ataxin-2 improves survival and pathology in TDP-43 mice
Immunosuppressive therapy in patients with biopsy-proven inflammatory myocardial disease: a systematic review and meta-analysis
Abstract The aim of this study was to evaluate and update the effects of immunosuppressive therapy on heart failure outcomes in patients aged 16 years or older with biopsy-proven inflammatory myocardial disease. We performed a systematic review and an aggregate data meta-analysis of studies evaluating the effects of such therapy in acute and chronic myocarditis or inflammatory dilated cardiomyopathy. Studies were chosen based on criteria related to clinical relevance for therapeutic decisions and measured outcomes in left ventricular ejection fraction (LVEF) and New York Heart Association classification (NYHA) at 3, 6, and 12 months. Data sources included PubMed, Embase, Ovid, and ClinicalTrials.gov, with manual reference checks. Data extraction and risk-of-bias assessments were performed by two independent reviewers. We used a random-effects model and assessed heterogeneity with I 2 and τ 2 statistics. . Seven eligible studies with a total of 594 patients were included, that investigated clinical course of acute and chronic myocarditis and inflammatory dilated cardiomyopathy. At 3 months, the pooled standardized mean difference (SMD) for LVEF was 0.97 (95% CI –0.05 to 1.99; p > 0.05). At 6 months, the pooled SMD was 0.48 (95% CI –1.31 to 2.28), and at 12 months, –0.01 (95% CI –1.65 to 1.62). For NYHA classification, pooled SMDs were 0.02 (95% CI –0.21 to 0.25) at 3 months and –0.22 (95% CI –1.56 to 1.11) at 12 months. In all cases, confidence intervals crossed zero. Heterogeneity remained high (I 2 = 92–97%), reflecting substantial variability across studies. In patients with acute or chronic myocarditis or inflammatory dilated cardiomyopathy, immunosuppressive therapy does not improve key echocardiographic or clinical parameters when adding new data from randomized controlled trials (RCTs). High heterogeneity suggests variability in patient populations and protocols, highlighting the need for well-designed RCTs. There is still no evidence to support routine endomyocardial biopsy in non-severe disease, as it will not impact symptomatic treatment, even if inflammation is present.
Retraction: Business credit network characteristics and impact on green economy efficiency: Evidence from the Greater Bay Area around Hangzhou Bay of China
Categorical and phenotypic image synthetic learning as an alternative to federated learning
Abstract Multi-center collaborations are crucial in developing robust and generalizable machine learning models in medical imaging. Traditional methods, such as centralized data sharing or federated learning (FL), face challenges, including privacy issues, communication burdens, and synchronization complexities. We present CATegorical and PHenotypic Image SyntHetic learnING (CATphishing), an alternative to FL using Latent Diffusion Models (LDM) to generate synthetic multi-contrast three-dimensional magnetic resonance imaging data for downstream tasks, eliminating the need for raw data sharing or iterative inter-site communication. Each institution trains an LDM to capture site-specific data distributions, producing synthetic samples aggregated at a central server. We evaluate CATphishing using data from 2491 patients across seven institutions for isocitrate dehydrogenase mutation classification and three-class tumor-type classification. CATphishing achieves accuracy comparable to centralized training and FL, with synthetic data exhibiting high fidelity. This method addresses privacy, scalability, and communication challenges, offering a promising alternative for collaborative artificial intelligence development in medical imaging.
Parameter influence on the mechanical properties of FFF printed PLA/copper for EV lightweight dashboard analysis
Limitations of hormonal and clinical markers in predicting GnRH agonist trigger success in polycystic ovary syndrome: A critical reappraisal
Background Polycystic ovary syndrome (PCOS) is associated with altered hypothalamic-pituitary-ovarian function, which may affect the success of GnRH agonist triggers used during IVF. Identifying reliable predictors of oocyte yield in these patients remains a clinical challenge. Purpose To evaluate possible predictors of suboptimal oocyte retrieval per aspirated follicle when ovulation is triggered with GnRH agonist in PCOS patients. Methods Between 30/04/2021 and 30/12/2022, a prospective cohort study was conducted among women with PCOS (n = 104) in which a GnRH agonist trigger was employed during a GnRH antagonist protocol. Hormonal and clinical parameters were tested for their ability to predict the oocyte per aspirated follicle (OPF) rate in in-vitro fertilization (IVF) treatment. Results The mean age of the patients, mean number of aspirated follicles, number of collected oocyte cumulus complexes and OPF were 28.6 ± 3.9 years, 31.4 ± 10.2, 21.7 ± 8.9, and 70.2 ± 19.1%, respectively. When patients were stratified into three groups according to their OPF percentiles (Q1:0–25th percentile, Q2:26–75th percentile, Q3:76–100th percentile), body mass index (BMI) and antral follicle count (AFC) were significantly higher in the Q1 group compared to the Q2 and Q3 groups. However, regression analysis revealed that only AFC was an independent predictor of the OPF rate (RR: −0.005, 95% CI: −0.008 to −0.002, p = 0.001), but not BMI or serum LH levels on the day of triggering. Notably, the predictive validity of AFC to recognize a low OPF rate was poor (AUC = 0.561). Conclusion A high AFC was the only identifiable predictor of the OPF rate and a suboptimal response when a GnRH agonist trigger was used for final follicular maturation. However, since a low AUC for AFC suggests a poor performance, we conclude that this study was not able to find any reliable prediction markers for the OPF rate in PCOS patients triggered with GnRHa.
Economics Nobel prize won by researchers who showed how science boosts growth
Programmable molecular dethreading towards tunable drug release
Programmed cell death-related genes define distinct molecular subtypes and risk profiles in hepatocellular carcinoma
Retraction: Socioeconomic shocks, social protection and household food security amidst COVID-19 pandemic in Africa’s largest economy
Polarity-dependent ferroelectric modulations in two-dimensional hybrid perovskite heterojunction transistors
Oculomotor dysfunction May not subside upon clinical resolution of sport related concussion
Abstract Sport-related concussion (SRC) is a public health crisis that results in growing diagnoses each year. Recent evidence suggests that there are oculomotor deficits present in patients with SRC. It is unclear if this oculomotor deficit is linked to other clinical outcomes or lingers beyond clinical symptom resolution. The purpose of this study is to investigate the progression of oculomotor and clinical deficits following SRC at the acute stage of injury and again when they are clinically considered fully symptom-free for at least 24 h. 13 NCAA athletes completed a multifaceted concussion battery that included postural (tandem gait), symptom provocation (Vestibular/Ocular Motor Screening), and oculomotor assessments (eye-tracking) once within 48 h of diagnosis of SRC (AC) and again once free of symptoms (SF). Significant group differences were observed in several oculomotor metrics. Both AC and SF groups exhibited elevated peak saccadic velocity and acceleration compared to controls ( p < 0.01), with no significant difference between AC and SF. Saccadic amplitude was significantly reduced in both AC and SF groups relative to controls ( p < 0.001). The number of masked saccades during SP was lower in the AC group than in controls ( p = 0.05), but this difference was not observed in the SF group. No significant group differences were found for saccade duration or SP velocity. There are still oculomotor deficits that persist when SRC student-athlete patients’ injuries are deemed clinically resolved. Lingering issues are not uncommon; however, these functional eye movement deficits are concerning and warrant additional research.
Evaluating Uighur literary translation: A comparative study of ChatGPT, Google Translate, and Bing Translator
This study compares generative artificial intelligence (GenAI) and neural machine translation (NMT) systems in translating Uighur literary text (قۇتادغۇ بىلىك)into English. Two NMT systems, Google Translate and Bing Translator, were evaluated alongside ChatGPT, a GenAI large language model, under two prompt strategies. Translation quality was assessed through automatic metrics (BLEU, ROUGE-N/L, METEOR, and BERT-based semantic similarity), automated error counts (grammar, spelling, style), and expert ratings across four dimensions. Qualitative examples of culturally sensitive excerpts were also examined to illustrate success and failure cases. Results show that ChatGPT, especially with a concise instruction prompt, generally outperforms NMT systems in semantic accuracy, fluency, and cultural adequacy. Bing Translator produced the highest number of errors, particularly spelling mistakes, while Google Translate demonstrated more stable but moderate performance. Statistical testing and expert evaluations supported these patterns, and case analyses revealed how NMT outputs often distorted meaning through polarity reversal and semantic shifts. The findings highlight prompt engineering as a key factor for improving GenAI-based literary translation while recognizing the complementary strengths of GenAI adaptability and NMT stability. Future research should expand language and system coverage and examine the role of human post-editing in enhancing translation quality.
Chemical valorization of waste polyacrylonitrile polymers via shuttle catalysis
Dance classification using pretrained deep learning models integrated with the circular Fermatean fuzzy MARCOS method
Abstract Pretrained deep learning models offer a method for classifying dance by analyzing movement behaviors in videos or using sensors to assess the degree of automation in recognizing different types of dances. These models utilize transfer learning strategies to enhance recognition accuracy across various datasets. This study presents a hybrid framework for identifying and classifying dance styles using pretrained deep learning models, evaluated under multiple performance criteria. To address the inherent uncertainty and complexity in selecting the best-performing models, a novel circular Fermatean fuzzy measurement of alternatives and ranking based on the compromise solution (CFF-MARCOS) approach is proposed. Unlike existing methods, the integration of circular Fermatean fuzzy sets (CFFS) into the MARCOS framework enables more refined handling of hesitation and ambiguity in expert evaluations. A case study involving ten pretrained models, seven criteria, and three experts demonstrates the superiority of the proposed method in generating robust and interpretable rankings. Results highlight improved decision reliability and clarity in model selection for automated dance classification tasks.
Retraction: Does financial innovation foster financial inclusion in Arab world? examining the nexus between financial innovation, FDI, remittances, trade openness, and gross capital formation
Correlative voltage imaging and cryo-electron tomography bridge neuronal activity and molecular structure
Abstract Neurons exhibit varying electrophysiological properties due to dynamic changes in spatiotemporal molecular networks. In situ cryo-electron tomography (cryo-ET) provides advantages for high-resolution visualization of macromolecular complexes within their cellular context. Although correlation with fluorescent labeling allows cryo-ET to target specific cellular regions, it does not adequately reflect the electrophysiological properties of heterogeneous neurons. To bridge high-resolution molecular imaging with electrophysiological properties of individual neurons, we develop a Correlative Voltage Imaging and cryo-ET (CoVET) technique. The nondestructive nature of voltage imaging is compatible with cryo-ET, enabling a direct correlation between neuronal electrophysiology and molecular structures. Neurons are clustered based on their electrophysiological properties, allowing for single-cell-guided structural analysis using cryo-ET. We analyze the translational landscapes of individual neurons and find distinct structural characteristics and spatial networks among ribosomes from different electrophysiological clusters. Our results highlight the importance of the correlation between the electrophysiological properties and molecular structures.