A rare case of iron deficiency anaemia with xylophagia was successfully managed through a multidisciplinary approach involving a haematologist, psychiatrist, and gynaecologist. This highlights the importance of collaborative care in addressing complex conditions and ensuring comprehensive treatment.

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial component of the global climate system and is believed to have slowed down in recent decades. While model simulations indicate that anthropogenic Arctic sea-ice melting drives this slowdown, observational evidence for this connection remains lacking, leaving the attribution of the AMOC decrease unresolved. In this study, we present causal observational evidence, beyond mere correlation, that the recent weakening of the AMOC is influenced by the decline in summer Arctic sea-ice concentration over the past century, particularly between the 1950s and 1980s, coinciding with the Great Salinity Anomaly. Using two methods—Convergent Cross Mapping from dynamical systems theory and Information Flow from information theory—we demonstrate that modes of AMOC variability are influenced by sea-ice melting. The AMOC slowdown and its modes exhibit a causal lag of 1 to 3 decades relative to sea-ice melting, aligning with model predictions.

The neural foundations of the Dark Triad (DT) personality traits—narcissism, Machiavellianism, and psychopathy—remain largely unexplored despite their profound impact on social behavior. Previous research has indicated potential structural and functional alterations in the default mode network (DMN) and reward-related regions in individuals exhibiting high levels of DT traits. However, these studies have often been limited by univariate analytical methods and small, unbalanced sample sizes. To address these gaps, we analyzed brain scans from a robust sample of 200 participants (mean age: 32.43 years; 105 females) using group Independent Component Analysis (gICA), an unsupervised machine learning approach. This method allowed us to extract temporal variability associated with resting-state macro-networks without relying on predefined regions of interest. Our analysis identified 20 macro-networks, of which two were significantly predictive of DT traits: the Central Executive Network (CEN) and the posterior hub of the Default Mode Network (DMN). Notably, higher DT scores correlated with increased blood-oxygen-level-dependent (BOLD) temporal variability within the CEN and decreased variability within the DMN. The heightened activity in the CEN may reflect enhanced manipulative abilities and strategic planning commonly observed in individuals with high DT traits, which is supported by the association between the CEN and the Machiavellianism subscale. Conversely, reduced functionality in the DMN may correspond to diminished self-reflective and emotional capacities in these individuals. These findings contribute to a deeper understanding of the neurobiological basis of DT traits and hold implications for future research and interventions.

The diagnosis of superior vena cava syndrome is always challenging, even more so when it happens intraoperatively. A novel transgastric-transesophageal projection allows for the visualisation of caval flow acceleration, critical in leading to rapid diagnosis and quick reaction in the operative setting. We present the case of a patient who underwent successful mitral valve reconstruction and left atrial appendage closure via median sternotomy on cardiopulmonary bypass and intraoperatively developed iatrogenic superior vena cava syndrome. This novel transgastric-transesophageal echocardiographic projection proved crucial in identifying it and immediate intraoperative management.

Queuing up for a service is sometimes an inevitable experience. The inefficiencies brought on by extended waiting times can be considerably decreased by precise waiting time prediction. Accurate prediction can substantially improve consumer satisfaction by reducing uncertainty. It is possible to introduce a robust approach to the prediction of waiting times based on previous queuing data and artificial intelligence (AI) algorithms. This paper contributes to the field by offering a robust approach to waiting time prediction and suggests potential directions for further research. The investigation leverages ensemble tree-based methods, supplemented by various data preprocessing techniques for regression analysis to forecast precise waiting times. The following regression models have been used to assess the performance: Random Forest (RF), Extra Trees (ET), Gradient Boosting (GBR), Histogram-Based Gradient Boosting (HGBR), and Voting (VR). Among these, the Extra Trees Regressor demonstrates superior performance. Dimensionality reduction via Principal Component Analysis (PCA) proved less effective than using the original feature sets. Furthermore, the challenge of data imbalance in classification tasks has also been addressed here using the Synthetic Minority Oversampling Technique (SMOTE). This process impressively enhances classification accuracy, especially for minority classes.

A document by Smriti Lama Thing. Click on the document to view its contents.

Title page:Enteric Duplication of the Small Intestine Imaged by Meckel’s ScanAiah Alatoum MBBS.1, Parren S. McNeely M.D.1, Michael M. Graham M.D1., Riad M. Rahhal M.D.2, Ahmad Shariftabrizi1 M.D.11Department of Radiology, University of Iowa, Iowa City, IA, 52242, USA.2 Department of Pediatrics, University of Iowa, Iowa City, IA, 52242, USA.Corresponding author: Ahmad Shariftabrizi M.D.Clinical Assistant ProfessorDivision of Nuclear Medicine, Department of Radiology-Room 3951 JPPUniversity of Iowa Carver College of Medicine, 200 Hawkins Drive, Iowa City, IA 52242Office: 319-887-4942; Fax: 319-339-7123; Cell: 857-526-4145.Email: ashariftabrizi@healthcare.uiowa.edu

Dapsone Hypersensitivity Syndrome with Hepatic Involvement: A Rare Case ReportBishal Koirala (Corresponding Author), Medical Officer, Shreegaun Primary Health Centre, Dang, NepalEmail: bishalkoirala22@gmail.comContact number: +9779845567541Priyanka Devkota, Lab Research Analyst, Duke Human Vaccine Institute, North Carolina, USAEmail: priyankadevkota0@gmail.comContact number: +16624970236

An enduring challenge in automated text generation lies in sustaining semantic coherence across extended sequences, where conventional memory mechanisms often fail to maintain contextual consistency over time. Introducing a syntax-aware retention approach, this study proposes the Layered Syntax-Aware Memory Retention Mechanism (LSMRM) as a novel framework that dynamically prioritizes memory retention based on syntactic cues, thereby enabling enhanced thematic continuity and structural coherence in long-form text. Through integrating LSMRM into a state-of-the-art open-source model, experiments revealed marked improvements in semantic coherence, syntactic alignment, and memory efficiency, particularly across sequences that demand stable narrative progression. Coherence metrics, such as context overlap ratio and syntactic alignment score, demonstrated that syntax-sensitive retention yielded sustained context and lexical diversity, effectively reducing thematic drift and enhancing narrative consistency in longer outputs. Computational analysis further indicated that LSMRM maintains processing efficiency with modest increases in latency, making it suitable for real-world applications requiring both long-form consistency and computational economy. Overall, the syntaxlayered approach of LSMRM signifies an advance in retention strategies for language models, with broad implications for domains that demand structured, coherent, and contextually adaptive text generation.

Farmlands occupy more than half the inhabitable Earth, sustaining much of the planet's wildlife. Many farmland species have not been able to adapt to the severely anthropogenically modified environments of modern agricultural lands. The northern lapwing has declined steadily in most countries since the 1980s. Reports of sudden, inexplicable local collapses are reported, where the population is all but gone within a decade, suggest that observed slow regional population declines could just be the result of many small population implosions spread out in time and space. With the northern lapwing as model species, the proposed trap-implosion theory unveils how increases in the proportion of intensively farmed cropland, act as an ecological trap, triggering local population implosions. The simulations show the ratio of high-to-low-quality breeding habitat to be the main trigger. This demonstrates that expansion of modern farmlands alone, rather than loss of natural habitat, may cause population collapse. The rate of habitat-area changes, declines in yearly survival, and breeding success mainly affect the timing of the implosion, but not its pace. Observations of local lapwing-population collapses suggest that the declines of many farmland species, especially farmland bird species, may be the accumulative result of local trap implosions. A conservational focus on farming practices and preserving or improving high-quality habitat may explain why the mechanism of ecological-trap-induced implosions have gone unnoticed, and why farmland-bird conservation measures have largely failed.

Introduction: BBRVT is a type of ventricular tachycardia that occurs when there is a re-entrant circuit involving the His-Purkinje system and the bundle branches. The condition is characterized by a rapid heart rate (200–300 beats/min), and can lead to symptoms such as palpitations, dizziness, and syncope. BBRVT is often associated with underlying structural heart disease, such as cardiomyopathy, but can also occur in individuals with structurally normal hearts. We present a case of a 20-year-old male with exercise-induced palpitations and syncope, who was found to have sustained VT due bundle re-entrant tachycardia in the absence of overt structural heart disease.

Background: New 2023 CF liver disease (CFLD) guidelines advocate for additional screening in people with cystic fibrosis PwCF, including biennial abdominal ultrasound. As a first step towards effective and equitable guidelines implementation, we examined our current practice of CFLD screening and hepatobiliary involvement (HBI) evaluation. We identified characteristics of PwCF at-risk for incomplete screening and factors affecting evaluation. Methods We retrospectively reviewed medical records of PwCF aged 0-21yrs, with native liver and ≥2 outpatient CF clinic visits 2017-23. Logistic regression was used to identify characteristics associated with incomplete screening and with HBI. Results Amongst 112 PwCF at our center: 37% self-reported as mixed race, 27% as Hispanic; 53% had public insurance. Incomplete lab screening was identified in 19% of our cohort. GGT was the most frequently missed component (17%). Hispanics and publicly insured people were more likely to have incomplete screening. Of the 112, 45 met criteria for HBI. Demographics did not predict HBI. Five with CF and HBI had the full hepatitis workup recommended by the new guidelines. Those with HBI documented (42%) were more likely to receive additional workup. PwCF who were seen by a gastroenterologist were more likely to have additional diagnostic work-up for HBI. Conclusions: One in five PwCF at our center were incompletely screened for CFLD, with Hispanics and publicly insured at higher risk. Accurate diagnosis and adequate documentation are the first steps to identifying HBI in PwCF. A dedicated CF gastroenterologist is key to completing CFLD screening and liver diagnosis.

Tattoo inks contain several substances, including organic and inorganic pigments, additives, and solvents, which may pose a health risk to not only the tattooed skin but also to other parts of the human body due to intradermal exposure. Substances in tattoo inks are regulated by entry 75 in Annex XVII of REACH Regulation (EC) No. 1907/2006. However, despite these legal requirements, a well-defined criterion for the safety assessment of tattoo inks remains lacking. In this context, 2021 BfR opinion titled “Tattoo inks: minimum requirements and test methods” proposed a comprehensive risk assessment of pigments using in-vitro/in-chemico data in accordance with the OECD Guidelines and CLP Regulation. However, in the absence of experimental data, new approach methodologies (NAMs) can be used for data-gap filling. This work evaluates the applicability of in silico NAMs for data-gap filling to a list of tattoo ink ingredients identified by the JRC and BfR for genotoxicity assessment. The experimental in vitro genotoxicity data was acquired from the International Uniform ChemicaL Information Database (IUCLID) which makes the non-confidential REACH Study Results publicly accessible. The specific aims of this analysis were evaluation of in silico genotoxicity predictions from publicly available QSAR tools and structural alerts, development and validation of new QSAR models specific to tattoo ink ingredients, and application of in silico models for categorization and prioritization of data-poor ingredients for further screening. Based on the workflow developed in this study, 4 high priority, 11 medium priority and 2 low priority substances were identified for further assessment.

AbstractStroke-induced brain remodeling involves a complex interplay of neurovascular components, including endothelial cells, microglia, astrocytes, and pericytes, which collectively contribute to the restoration of brain function. These processes are crucial for repairing the blood-brain barrier, regulating inflammation, and promoting neurogenesis. This review examines the mechanisms underlying brain remodeling and the role of advanced neuroimaging techniques—such as functional MRI (fMRI), positron emission tomography (PET), functional near-infrared spectroscopy (fNIRS), and functional ultrasound (fUS)—in assessing these changes. We also discuss various therapeutic approaches aimed at enhancing brain remodeling, including pharmacological agents, stem cell therapy, and rehabilitation strategies that target neurovascular repair and functional recovery. Despite significant progress, challenges remain in translating imaging insights into effective treatments. Future research should focus on integrating multiple imaging modalities to provide a comprehensive view of neurovascular changes and refining therapeutic interventions to optimize recovery and functional outcomes in stroke patients.Keywords : Stroke; Neuroimaging; Brain Remodeling; Magnetic Resonance Imaging; Blood-Brain Barrier

Introduction: A decline in fertility rates is observed in Colombia and globally, with contraceptive use significantly contributing to this trend. Daily Defined Doses (DDD) of contraceptives per 1,000 females aged 15 to 49 (DID) provide insights into strategies for reducing unwanted pregnancies. This study aims to identify patterns of contraceptive use in Colombia by type and develop explanatory hypotheses regarding changes over time. Methods: Data on the female population of childbearing age (15 to 49 years) from the Unique Database of Affiliates (BDUA) and units sold of contraceptives from the Drug Price Information System (SISMED) were extracted and integrated by date. The units per commercial presentation and type of contraceptive were manually verified using health records from INVIMA, allowing for the assignment of DID for each contraceptive and analysis of trends over time. Results: The DID reveals a significant increase from 101 in 2020 to 128 in 2022, followed by a return to almost previous levels (102) in 2023. The share of combined oral contraceptives (COC) progressively increased, replacing monthly injections and Depot Medroxyprogesterone Acetate by 2023. A month-by-month analysis shows a trend toward increased use of high-dose levonorgestrel-releasing intrauterine devices and subcutaneous implants, alongside reduced use of oral contraceptives and Depot Medroxyprogesterone Acetate. Conclusions: These trends reflect a shift in contraceptive preferences among practitioners and women of reproductive age. Future research should continue to monitor these patterns and assess the implications for public health policies related to reproductive health and family planning in Colombia.

Introduction Studies have reported differences in the gut microbiome of neuropsychiatric disorder cases associated with increased intestinal permeability and inflammatory conditions. In support of inflammatory conditions, differences in cytokine concentration between cases and controls have also been reported in Parkinson’s disease (PD), schizophrenia (SCZ), and posttraumatic stress disorder (PTSD). This study correlated the gut microbiome with the existing inflammatory cytokine concentration data within each cohort including PD, SCZ, and PTSD. Methods Utilising the 16S rRNA (V4) gut microbial data available for PD, SCZ, and PTSD, the associations between significantly different cytokine concentrations and the gut microbiome were assessed. Alpha-diversity was evaluated using Shannon and Simpson diversity measures, whereas beta-diversity was assessed with the permutational multivariate analysis variance (PERMANOVA) adonis test. The differential relative abundance of gut microbial taxa associated with altered cytokine concentrations was determined using the Multivariate Association with Linear Models 2 (MaAsLin2) test. The significance threshold was set at α = 0.05 for all tests (both p-value or q-value) and used the Benjamini-Hochberg method to correct for multiple testing. Results The differences in cytokine concentrations between cases and controls were found not to be associated with alpha- or beta-diversity. We observed an association between IL-6 and the enriched relative abundance of Lactobacillus (q = 0.023, PD) and RFN20 (q = 0.011, SCZ), and depleted relative abundance of Coprococcus (q = 0.023, PD). Conclusion Elevated levels of pro-inflammatory cytokines, particularly IL-6, are observed across neuropsychiatric disorders and may be associated with a decrease in SCFA-producing taxa such as Faecalibacterium. Further research is needed to understand how dysregulated cytokine concentrations contribute to neuropsychiatric disorders and to explore molecular pathways involving cytokines and disease-specific proteins such as alpha-synuclein for potential therapeutic strategies.

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Phased array antennas, with high directivity and low sidelobe levels, are extensively employed in radar and communication systems. As the array scale increases, the complexity and cost of controlling each element individually are increased significantly. To overcome this challenge and improve the efficiency, one of the effective engineering solutions is subarray partitioning. Traditional clustering algorithms, such as K-means, require a predefined number of subarrays, and limits the flexibility, especially in scenarios with complex geometries or irregular distributions. A subarray partitioning approach is presented in this paper using the Iterative Self-Organizing Data Analysis (ISODATA) algorithm, and the proposed scheme dynamically adjusts the number of subarrays based on the geometric characteristics of the array. According to numerical experiments results, good performance of sidelobe suppression, beamforming and partitioning flexibility have been achieved, and shows better characteristic compared with the K-means. This algorithm has potential application in large-scale phased arrays.

IoT Edge improves the Quality of Service of the network by decentralizing the system with the help of edge servers. The limited resources of IoT devices make it inefficient to communicate with the cloud server incessantly. The Edge servers with the help of Edge devices communicate with the IoT devices and perform various QoS actions. However, the growing attention on IoT Edge systems requires a more resilient system to manage the workload efficiently. Compression is the technique used to utilize the resources skilfully. But the IoT system is more dynamic, requiring the nonlinearity-based system to function more proficiently. Therefore this paper proposes the Nonlinear Information Bottleneck method for compressing the data in the Edge server. The Nonlinear Information Bottleneck using Variational Autoencoder deep learning algorithm that efficiently compressed the data with good accuracy and profoundly utilized the resources.

A document by Yajiao Shi. Click on the document to view its contents.

In acoustic resolution PA microscopy (AR-PAM), high signal-to-noise ratio (SNR) images require averaging pulse-illuminated A-scans, slowing data acquisition. Multi-wavelength PA imaging further reduces scan speed, leading to issues like photobleaching of exogenous contrast agents due to prolonged exposure. Traditional noise removal algorithms fall short, while deep learning models like U-Net, though effective, also compromise image contrast. We propose a platform-flexible denoising conditional GAN (modified Pix2Pix) to generate high SNR images with single pulse illumination, reducing AR-PAM scan time by 30-fold. Tested across various systems, our model minimizes photobleaching, enhances efficiency, and remains adaptable to diverse hardware setups without retraining, broadening its preclinical and clinical applications.

A document by Omama Islam. Click on the document to view its contents.

Based on the limitation of visual working memory, interference suppression is one of abilities to keep attentional resources on targets rather than distractor items. There was still a controversy on how the mechanism of interference suppression optimized visual working memory, which was derived from distinct paradigms. Change detection combined systematic lateralization task was proposed to solve this controversy. Therefore, this study aimed to explore the attentional processing and its potential mechanism of target and distractor items through the new paradigm. The behavioral results indicated significantly higher accuracy and shorter response times for targets when the distractor was horizontal compared to vertical. For the ERPs results, the N2pc component (223~273 ms) exhibited a more negative amplitude contralateral to the target than ipsilateral in the target lateral condition. Conversely, the latency of N2pc was longer ipsilateral to the target than contralateral. In the distractor lateral condition, moreover, the amplitude of Pd at the electrodes contralateral of distractor was more positive than at the electrodes ipsilateral. However, no significant difference was found in Pd latency. Analysis of difference waves revealed that the N2pc amplitude was more negative in target lateral conditions compared to distractor lateral conditions, while the distractor lateral condition elicited a stronger Pd component. No significant latency differences were observed in either N2pc or Pd difference waves. In conclusion, this study supported the signal suppression hypothesis that active suppression processing prevents distractor items from capturing attention, even if it produces a priority signal of ”attended to me”.

A document by Ha Vu. Click on the document to view its contents.