Search Results (2,096)

Background: Irritable Bowel Syndrome (IBS) is a heterogeneous gastrointestinal disorder characterized by complex brain–gut interactions. Patient Similarity Networks (PSNs) offer a novel approach for exploring this heterogeneity and identifying clinically relevant patient subgroups. Methods: We analyzed data from 78 participants (49 IBS patients and 29 healthy controls) with 36 brain morphometric measures (FreeSurfer v7.4.1) and 6 measures of cognitive functions (5 RBANS domain indices plus a Total Scale score). PSNs were constructed using multiple similarity measures (Euclidean, cosine, correlation-based) with Gaussian kernel transformation. We performed community detection (Louvain algorithm), centrality analyses, feature importance analysis, and correlations with symptom severity. Statistical validation included bootstrap confidence intervals and permutation testing. Results: The PSN comprised 78 nodes connected by 469 edges, with four communities detected. These communities did not significantly correspond to diagnostic groups (Adjusted Rand Index = 0.011, permutation $p=0.212$), indicating IBS patients and healthy controls were intermixed. However, each community exhibited distinct neurobiological profiles: Community 1 (oldest, preserved cognition) showed elevated intracranial volume but reduced subcortical gray matter; Community 2 (youngest, most severe IBS symptoms) had elevated cortical volumes but reduced white matter; Community 3 (most balanced IBS/HC ratio, mildest IBS symptoms) showed the largest subcortical volumes; Community 4 (lowest cognitive performance across multiple domains) displayed the lowest RBANS scores alongside high IBS prevalence. Top network features included subcortical structures, corpus callosum, and cognitive indices (Language, Attention). Conclusions: PSN identifies brain–cognition communities that cut across diagnostic categories, with distinct feature profiles suggesting different hypothesis-generating neurobiological patterns within IBS that may inform personalized treatment strategies. Full article

Methicillin-resistant coagulase-negative staphylococci (MRCoNS) are a major cause of infectious diseases, owing to their ability to form biofilms and colonize community and hospital environments. MRCoNS strains were identified using biochemical tests, an MALDI-TOF MS analyzer, and PCR-based 16S rRNA gene confirmation. This study was designed to assess antibiotic resistance and biofilm-forming capacity and to determine the presence of the mecA, mecC, agrA, srtA, icaABCD, bap, fnbAB, and clfAB genes in MRCoNS isolates. From patients undergoing random screening during hospitalization in the Orthopedics Clinic in Slovakia, 28 strains of MRCoNS were identified: S. epidermidis (n = 10), S. hominis (n = 8), S. haemolyticus (n = 4), S. lugdunensis (n = 3), while S. simulans, S. pasteuri, and S. warneri were detected only once. The highest rates of resistance were observed for ampicillin, oxacillin, rifampicin, trimethoprim (100%), and erythromycin (62%). The mecA gene was detected in 12 analyzed isolates. In 12 isolates, MDR, strong efflux pump activity, and strong or moderate biofilm formation were simultaneously detected. Our findings highlight the problems posed by biofilm-forming, resistant CoNS in hospitalized patients and the importance of diagnostics, separation, rapid treatment, and proper hospital hygiene. Full article

Gut microbiota is a potential therapeutic target for type 2 diabetes (T2D), but its role remains unclear. Investigating causal associations between them could further our understanding of their biological and clinical significance. A two-sample Mendelian randomization (MR) analysis was conducted to assess the causal relationship between gut microbiota and T2D. Key genes and mechanisms were identified through the integration of Genome-Wide Association Studies (GWAS) and cis-expression quantitative trait loci (cis-eQTL) data. Network pharmacology was applied to identify potential drugs and targets. Additionally, gut microbiota community analysis and machine learning models were used to construct a diagnostic model for T2D. MR analysis identified 17 gut microbiota taxa associated with T2D, with three showing significant associations: Actinomyces (odds ratio [OR] = 1.106; 95% confidence interval [CI]: 1.06–1.15; p < 0.01; adjusted p-value [p_adj] = 0.0003), Ruminococcaceae (UCG010 group) (OR = 0.897; 95% CI: 0.85–0.95; p < 0.01; p_adj = 0.018), and Deltaproteobacteria (OR = 1.072; 95% CI: 1.03–1.12; p < 0.01; p_adj = 0.029). Ten key genes, such as EXOC4 and IGF1R, were linked to T2D risk. Network pharmacology identified INSR and ESR1 as target driver genes, with drugs like Dienestrol showing promise. Gut microbiota analysis revealed reduced α-diversity in T2D patients (p < 0.05), and β-diversity showed microbial community differences (R² = 0.012, p = 0.001). Furthermore, molecular docking confirmed the binding affinity of potential therapeutic agents to their targets. Finally, we developed a class-weight optimized Extreme Gradient Boosting (XGBoost) diagnostic model, which achieved an area under the curve (AUC) of 0.84 with balanced sensitivity (95.1%) and specificity (83.8%). Integrating machine learning predictions with MR causal inference highlighted Bacteroides as a key biomarker. Our findings elucidate the gut microbiota-T2D causal axis, identify therapeutic targets, and provide a robust tool for precision diagnosis. Full article

The sensory system plays a critical role in development, as it enables the processing and integration of internal and external stimuli. Dysfunctions in this system lead to sensory processing disorders (SPDs), which affect approximately 5–13% of children aged 4–6 years, impacting not only sensory responsiveness but also social interaction, emotional regulation, motor coordination, learning, attention, communication, and sleep. Although SPDs have been extensively investigated from molecular to behavioral levels, their underlying neurobiological mechanisms remain debated, and reliable biomarkers are still lacking. Moreover, due to overlapping behavioral manifestations, SPDs are frequently misdiagnosed as attention deficit hyperactivity disorder (ADHD), leading to challenges in accurate diagnosis and treatment planning. This narrative review aims to synthesize current evidence on the neurofunctional and molecular underpinnings of SPDs in relation to ADHD, providing an integrated perspective on their converging and diverging pathways. By comparing neuroimaging and neurophysiological findings across the two conditions, we seek to deepen understanding of their shared mechanisms, clarify diagnostic boundaries, and inform the development of targeted, evidence-based interventions to address a critical gap in the field. Full article

Diagnostic errors have been a critical concern in healthcare, leading to substantial financial burdens and serious threats to patient safety. The Improving Diagnosis in Health Care report by the National Academies of Sciences, Engineering, and Medicine (NASEM) defines diagnostic errors, focusing on accuracy, timeliness, and communication, which are influenced by clinical knowledge and the broader healthcare system. This review aims to integrate existing literature on diagnostic error from a systems-based perspective and examine the factors across various domains to present a comprehensive picture of the topic. A narrative literature review was structured upon the Systems Engineering Initiative for Patient Safety (SEIPS) model that focuses on six domains central to the diagnostic process: Diagnostic Team Members, Tasks, Technologies and Tools, Organization, Physical Environment, and External Environment. Studies on contributing factors for diagnostic error in these domains were identified and integrated. The findings reveal that the effectiveness of diagnostics is influenced by complex, interconnected factors spanning all six SEIPS domains. In particular, socio-behavioral factors, such as team communication, cognitive bias, and workload, and environmental pressures, stand out as significant but difficult-to-capture contributors in traditional and commonly used data resources like electronic health records (EHRs), which limits the scope of many studies on diagnostic errors. Factors associated with diagnostic errors are often interconnected across healthcare system stakeholders and organizations. Future research should address both technical and behavioral elements within the diagnostic ecosystem to reduce errors and enhance patient outcomes. Full article

Background/Objectives: This study seeks to evaluate the clinical characteristics of newly diagnosed male breast cancers within the traditionally underserved Bronx population at risk for poorer health outcomes. Methods: We retrospectively searched our database for male patients who presented for mammographic evaluation between 1 January 2016 and 1 October 2024. The primary outcomes were the prevalence of biopsy-proven male breast cancer and its association with gynecomastia and TNM stage at diagnosis. Clinical data, including TNM staging, receptor status, risk factors, and patient demographics, were recorded for patients with biopsy-proven breast cancer based on biopsy results. Two dedicated breast imagers retrospectively evaluated mammograms of these patients to determine by consensus the presence of gynecomastia. Analyses were descriptive in nature. Results: During the study period, 423 screening mammograms and 1775 diagnostic mammograms were performed on male patients. Twenty-six male patients with biopsy-proven breast cancer were identified (two were bilateral and four were multifocal). In total, 69% of our male breast cancer patients (18 out of 26) demonstrated gynecomastia, which was similar across demographic groups, ranging from 63 to 75%. Out of the three patients with Stage 4 disease, two were Black and one was White. Stage 3 or higher disease was seen in 29% of our Black patients, 12% of our White patients, and 0% of our Hispanic patients. Conclusions: Male breast cancer in this Bronx population was frequently associated with gynecomastia and showed notable demographic disparities. Black patients presented with more advanced disease than other demographic groups. These descriptive findings highlight areas of further investigation and may help inform future outreach and early detection efforts in high-risk, underserved communities. This retrospective, single-institution analysis was limited by a small sample size and did not include formal statistical testing; therefore, the findings are descriptive and warrant validation with larger cohorts. Full article

This study investigated the spatial and seasonal dynamics of phytoplankton communities in Jaran Bay, inner Hansan Bay, and outer Hansan Bay, with particular emphasis on size structure and pigment-based indicators of productivity and physiological status. Water sampling was conducted during May, August, and October in 2020, 2022, and 2023 and phytoplankton communities were analyzed using size-fractionated chlorophyll a measurements and high-performance liquid chromatography (HPLC) pigment analysis. Chlorophyll a concentrations exhibited pronounced seasonality, with consistently elevated values in August across all bays. Diatoms were predominant throughout the study period; however, their relative contribution declined in outer Hansan Bay during summer, coinciding with increased contributions from cryptophytes and cyanobacteria. Size-fractionated analyses revealed that large-sized phytoplankton (>20 µm) predominantly consisted of diatoms, whereas small-sized phytoplankton (<20 µm) were composed of diatoms and cryptophytes. Comparisons between fluorometric and pigment-based approaches indicated that pigment-based diagnostics overestimated microphytoplankton contributions, attributable to the presence of small-sized diatoms. Pigment indices further revealed that large-sized phytoplankton were characterized by higher photosynthetic carotenoid concentrations and lower photoprotective carotenoid ratios, indicative of enhanced photosynthetic activity and productivity. Overall, these findings highlight the critical role of phytoplankton size structure in regulating productivity and physiological responses in aquaculture-dominated coastal bays. Full article

Background: Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental condition influenced by both genetic and non-genetic factors, although the underlying pathomechanisms remain unclear. We systematically analyzed microRNA (miRNA) expression and associated functional pathways in ASD to evaluate their potential as prenatal/postnatal, diagnostic, and prognostic biomarkers. Methods: Peripheral blood mononuclear cells from 12 Sicilian patients with ASD (eight with normal cognitive function) and 15 healthy controls were analyzed using small RNA sequencing. Differential expression analysis was performed with DESeq2 (|fold change| ≥ 1.5; adjusted p ≤ 0.05). Functional enrichment and network analyses were conducted using Ingenuity Pathway Analysis, focusing on Diseases and Biofunctions. Results: 998 miRNAs were differentially expressed in ASD, 424 upregulated and 553 downregulated. Enriched pathways were primarily associated with psychological and neurological disorders. Network analysis highlighted three principal interaction clusters related to inflammation, cell survival and mechanotransduction, synaptic plasticity, and neuronal excitability. Four miRNAs (miR-296-3p, miR-27a, miR-146a-5p, and miR-29b-3p) emerged as key regulatory candidates. Conclusions: The marked divergence in miRNA expression between ASD and controls suggests distinct regulatory patterns, thus reinforcing the central involvement of inflammatory, autoimmune, and infectious mechanisms in ASD, mediated by miRNAs regulating S100 family genes, neuronal migration, and synaptic communication. However, rather than defining a predictive biomarker panel, this study identified candidate miRNAs and regulatory networks that may be relevant to ASD pathophysiology. As such, further validation in appropriately powered cohorts with predictive modeling frameworks are warranted before any biomarker or diagnostic implications can be inferred. Full article

Objective: To analyse written submissions from individuals and families with lived experience of attention-deficit hyperactivity disorder (ADHD) to the 2023 Australian Senate Inquiry, using artificial intelligence (AI)-assisted thematic analysis. The aim was to identify priority concerns, service needs, and community-proposed solutions. Methods: A mixed-methods study of 505 publicly available submissions from individuals with ADHD and their families. Submissions were analysed using large language model (LLM)-assisted data extraction and thematic clustering, with human validation and review. Main Outcome Measures: Frequency and thematic distribution of (1) problems experienced; (2) services wanted; and (3) solutions suggested. Results: Thematic analysis of 480 eligible submissions revealed high costs and long wait times for assessment and treatment (each cited by 46%), lack of specialised care (39%), diagnostic delays (36%), and gender bias (27%). The most common service request was for affordable and accessible ADHD-specific care (71%), followed by services tailored to diverse populations and life stages. Proposed solutions focused on Medicare-funded access to psychological and psychiatric services (68%), expanded roles for general practitioners, improved provider training (39%), and recognition of ADHD under the National Disability Insurance Scheme. Submissions also highlighted misalignment between current clinical guidelines and public expectations. Conclusions: The findings highlight substantial unmet needs and systemic barriers in ADHD diagnosis and care in Australia. The AI-assisted analysis of consumer submissions offers a scalable method for integrating lived experience into policy development, providing numerical weighting to the individuals’ responses. Coordinated reforms in access, funding, and workforce training are needed to align services with both clinical evidence and community expectations. Full article

Children and adolescents with disabilities experience disproportionate challenges in achieving recommended levels of physical activity (PA) and adequate sleep, two core determinants of health and functional well-being. This systematic review examined associations between meeting PA guidelines and sleep duration among youth with disabilities. Following PRISMA guidelines, MEDLINE, PsycARTICLES, and SPORTDiscus were searched through Spring 2024 for studies assessing PA and sleep in children and adolescents (<18 years) with disabilities using subjective or objective measures. Data were extracted from 28 studies (N = 138,016) and synthesized using qualitative methods and regression-based quantitative analyses to examine the effects of diagnosis category and PA guideline adherence on sleep duration. The diagnosis type was associated with sleep duration, with youth with autism spectrum disorder (ASD) exhibiting shorter sleep than those with physical disabilities. Meeting PA guidelines (≥60 min/day) was associated with longer sleep duration among youth with ASD, but not consistently across other diagnostic groups. Qualitative findings further indicated diagnosis-specific variability, with PA positively associated with sleep outcomes in ASD, attention deficit/hyperactivity disorder, and epilepsy, and mixed associations observed for cerebral palsy and intellectual disability. These findings suggest that PA may support sleep health in specific disability groups. Given persistently low PA participation among youth with disabilities, integrating accessible, diagnosis-specific PA opportunities within school, community, and clinical settings may represent a feasible strategy to improve sleep and overall health. Full article

Biophotons are non-thermal and non-bioluminescent ultraweak photon emissions, first hypothesised by Gurwitsch as a regulatory mechanism in cell division, and then experimentally observed in living organisms. Today, two main hypotheses explain their origin: stochastic decay of excited molecules and coherent electromagnetic fields produced in biochemical processes. Recent interest focuses on the role of biophotons in cellular communication and disease monitoring. This study presents the first campaign of biophoton emission measurements from cultured astrocytes and glioblastoma cells, conducted at Fondazione Pisana per la Scienza (FPS) using two ultra-sensitive setups developed in collaboration between the National Laboratories of Frascati (LNF-INFN) and the University of Rome II Tor Vergata. The statistical analyses of the collected data revealed a clear separation between cellular signals and dark noise, confirming the high sensitivity of the apparatus. The Diffusion Entropy Analysis (DEA) was applied to the data to uncover dynamic patterns, revealing anomalous diffusion and long-range memory effects that may be related to intercellular signaling and cellular communication. These findings support the hypothesis that biophoton emissions encode rich information beyond intensity, reflecting metabolic and pathological states. The differences revealed by applying the Diffusion Entropy Analysis to the biophotonic signals of Astrocytes and Glioblastoma are highlighted and discussed in the paper. This work lays the groundwork for future studies on neuronal cultures and proposes biophoton dynamics as a promising tool for non-invasive diagnostics and the study of cellular communication. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a multifaceted systemic condition, with the mechanisms linking intrahepatic lesions to systemic complications remaining a significant enigma in the field. This review posits that extracellular vesicles (EVs) serve as pivotal mediators facilitating communication between the liver and the entire organism. Within the hepatic environment, lipotoxic hepatocyte-derived EVs modulate macrophage populations and stellate cells, thereby promoting inflammatory and fibrotic processes. Systemically, the liver engages in bidirectional communication with adipose tissue, the intestinal tract, the cardiovascular system, and the pancreas via EVs, thus orchestrating metabolic homeostasis. Furthermore, we critically evaluate non-invasive diagnostic strategies and emerging therapies, including both natural and engineered EVs, based on EV-based interventions. We highlight the substantial potential and current challenges associated with achieving precision medicine in MASLD through targeted modulation of this specific communication network. Full article

Abdominal pain is the cardinal symptom of irritable bowel syndrome (IBS) and the primary determinant of disease burden and healthcare utilization. Despite its diagnostic centrality and high prevalence across all IBS subtypes, effective management remains a clinical challenge. This narrative review explores the pathophysiological mechanisms underlying IBS-related pain, emphasizing the role of visceral hypersensitivity, altered brain–gut communication, and luminal factors such as gas and distension. We examine current guideline recommendations, real-world treatment patterns, and evidence supporting both pharmacological and non-pharmacological interventions. Particular focus is placed on the fixed-dose combination of alverine citrate/simeticone, which targets both motor and sensory pathways. Mechanistic studies demonstrate its smooth muscle relaxant, antinociceptive, and anti-inflammatory actions. Clinical trials support its efficacy in reducing pain, improving quality of life, and lowering healthcare resource use. Despite these advances, several unmet needs remain, including subtype-specific treatment strategies, mechanistic biomarkers, and broader access to integrated care. The review concludes with a call for more personalized, mechanism-based approaches to pain management in IBS, with alverine citrate/simeticone offering a pragmatic option within this evolving therapeutic framework. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

Midline shift (MLS) is one of the conditions that strongly affects mortality and prognosis in critical neurological emergencies such as traumatic brain injury (TBI). Especially, MLS over 5 mm requires urgent diagnosis and treatment. Despite widespread tomography imaging capabilities, the lack of radiologists capable of interpreting the images causes delays in the diagnosis process. Therefore, there is a need for AI-supported diagnostic systems specifically tailored to the field for MLS detection. However, the lack of open, disorder-specific datasets in the literature has limited research in the field and hindered the ability to make comparisons against a reliable reference point. Therefore, the current state of deep learning (DL) methods in the field is not sufficiently addressed. Within the scope of this study, a DL architecture is proposed for MLS detection as a classification task, with millimeter-scale MLS measurements used for evaluation and stratified analysis. This process also comprehensively addresses the status of MLS detection in contemporary DL architecture. Furthermore, to address the lack of open datasets in the literature, two publicly available datasets originally collected with a primary focus on TBI have been annotated for MLS detection. The proposed model was tested on two different open datasets and achieved mean sensitivity values of 0.9467–0.9600 for the Radiological Society of North America (RSNA) dataset and 0.8623–0.8984 for the CQ500 dataset in detecting MLS presence above 5 mm across two different scenarios. It achieved a mean Area Under the Curve-Receiver Operating Characteristic (AUC-ROC) value of 0.9219–0.9816 for the RSNA dataset and 0.9443–0.9690 for the CQ500 dataset. The aim of the study is to detect not only emergency cases but also small MLSs independent of quantity for patient follow-up, so the overall performance of the proposed model (MLS present/absent) was calculated without an MLS quantity threshold. Mean F1 Score values of 0.7403 for the RSNA dataset and 0.7271 for the CQ500 dataset were obtained, along with mean AUC-ROC values of 0.8941 for the RSNA dataset and 0.9301 for the CQ500 dataset. The study presents a clinically applicable, optimized, fast, reliable, up-to-date, and successful DL solution for the rapid diagnosis of MLS, intervention in emergencies, and monitoring of small MLS. It also contributes to the literature by enabling a high level of reproducibility in the scientific community with labeled open data. Full article