Search Results (1,568)

Background: Bipolar disorder (BD) is a chronic and disabling psychiatric condition characterized by recurring episodes of mania, hypomania, and depression. Despite the availability of mood stabilizers, antipsychotics, and antidepressants, long-term management remains challenging due to incomplete symptom control, adverse effects, and high relapse rates. Methods: This paper is a narrative review aimed at synthesizing emerging trends and future directions in the pharmacological treatment of BD. Results: Future pharmacotherapy for BD is likely to shift toward precision medicine, leveraging advances in genetics, biomarkers, and neuroimaging to guide personalized treatment strategies. Novel drug development will also target previously underexplored mechanisms, such as inflammation, mitochondrial dysfunction, circadian rhythm disturbances, and glutamatergic dysregulation. Physiological endophenotypes, such as immune-metabolic profiles, circadian rhythms, and stress reactivity, are emerging as promising translational tools for tailoring treatment and reducing associated somatic comorbidity and mortality. Recognition of the heterogeneous longitudinal trajectories of BD, including chronic mixed states, long depressive episodes, or intermittent manic phases, has underscored the value of clinical staging models to inform both pharmacological strategies and biomarker research. Disrupted circadian rhythms and associated chronotypes further support the development of individualized chronotherapeutic interventions. Emerging chronotherapeutic approaches based on individual biological rhythms, along with innovative monitoring strategies such as saliva-based lithium sensors, are reshaping the future landscape. Anti-inflammatory agents, neurosteroids, and compounds modulating oxidative stress are emerging as promising candidates. Additionally, medications targeting specific biological pathways implicated in bipolar pathophysiology, such as N-methyl-D-aspartate (NMDA) receptor modulators, phosphodiesterase inhibitors, and neuropeptides, are under investigation. Conclusions: Advances in pharmacogenomics will enable clinicians to predict individual responses and tolerability, minimizing trial-and-error prescribing. The future landscape may also incorporate digital therapeutics, combining pharmacotherapy with remote monitoring and data-driven adjustments. Ultimately, integrating innovative drug therapies with personalized approaches has the potential to enhance efficacy, reduce adverse effects, and improve long-term outcomes for individuals with bipolar disorder, ushering in a new era of precision psychiatry. Full article

Background: Minimally invasive cardiac surgery (MICS) via right minithoracotomy is a safe, reproducible approach with excellent outcomes and reduced costs compared to median sternotomy. Despite careful patient selection, conversion to sternotomy occurs in 1–3% of cases and is associated with significantly higher morbidity and mortality. Small body habitus, particularly a short anteroposterior (AP) diameter, may increase the risk of conversion, but this has not been previously studied. This study aims to identify preoperative factors associated with conversion to improve patient selection for MICS. As cardiovascular surgery becomes increasingly personalized, identifying anatomical factors that predict technical complexity is essential. Methods: This retrospective study included 254 adult patients who underwent elective MICS between 2015 and 2024 at a tertiary hospital. Patient characteristics, computed tomography (CT) scans, intraoperative parameters, and postoperative outcomes were reviewed. AP diameter was defined as the distance from the posterior sternum to the anterior vertebral body at the mitral valve level on CT. Statistical analyses included Mann−Whitney and Fisher’s exact/chi-square tests. Results: Conversion to sternotomy occurred in 1.6% of patients (n = 4). All converted patients were female. The converted group had a significantly shorter median AP diameter (100 mm vs. 124 mm, p = 0.020). Conversion was associated with higher rates of stroke and infection (25.0% vs. 0.8%, p = 0.047 for both), but no significant differences in hospital stay, bleeding, or renal failure. Conclusions: An AP diameter of less than 100 mm was associated with a higher risk of conversion to sternotomy in MICS. Incorporating simple, reproducible preoperative imaging metrics into surgical planning may advance precision-guided cardiac surgery and optimize patient outcomes. Full article

Steel is an important product in many engineering sectors; however, steelmaking remains one of the largest ${\mathrm{CO}}_2$ emitters. Therefore, new governmental policies drive the steelmaking industry toward a cleaner and more sustainable operation such as the gas-based direct reduction–electric arc furnace process. To become carbon neutral, utilizing more scrap is one of the feasible solutions to achieve this goal. Addressing knowledge gaps regarding scrap heterogeneity (size, shape, and composition) is essential to evaluate the effects of increased scrap ratios in basic oxygen furnace (BOF) operations. This review systematically examines heat and mass transfer correlations relevant to scrap melting in BOF steelmaking, with a focus on low Prandtl number fluids (thick thermal boundary layer) and dense particulate systems. Notably, a majority of these correlations are designed for fluids with high Prandtl numbers. Even for the ones tailored for low Prandtl, they lack the introduction of the porosity effect which alters the melting behavior in such high temperature systems. The review is divided into two parts. First, it surveys heat transfer correlations for single elements (rods, spheres, and prisms) under natural and forced convection, emphasizing their role in predicting melting rates and estimating maximum shell size. Second, it introduces three numerical modeling approaches, highlighting that the computational fluid dynamics–discrete element method (CFD–DEM) offers flexibility in modeling diverse scrap geometries and contact interactions while being computationally less demanding than particle-resolved direct numerical simulation (PR-DNS). Nevertheless, the review identifies a critical gap: no current CFD–DEM framework simultaneously captures shell formation (particle growth) and non-isotropic scrap melting (particle shrinkage), underscoring the need for improved multiphase models to enhance BOF operation. Full article

Chronic kidney disease (CKD) has now reached epidemic proportions in many parts of the world, primarily due to the high incidence of diabetes and hypertension. By 2040, CKD is predicted to be the fifth-leading cause of years of life lost. Developing strategies to prevent CKD and to reduce its progression to kidney failure is thus of great public health significance. Hypertension is known to be both a cause and a consequence of kidney damage and an eminently modifiable risk factor. An increased risk of hypertension, especially among women, has been linked to chronic exposure to the ubiquitous food contaminant cadmium (Cd). The mechanism is unclear but is likely to involve its action on the proximal tubular cells (PTCs) of the kidney, where Cd accumulates. Here, it leads to chronic tubular injury and a sustained drop in the estimated glomerular filtration rate (eGFR), a common sequela of ischemic acute tubular necrosis and acute and chronic tubulointerstitial inflammation, all of which hinder glomerular filtration. The present review discusses exposure levels of Cd that have been associated with an increased risk of hypertension, albuminuria, and eGFR ≤ 60 mL/min/1.73 m² (low eGFR) in environmentally exposed people. It highlights the potential role of 20-hydroxyeicosatetraenoic acid (20-HETE), the second messenger produced in the kidneys, as the contributing factor to gender-differentiated effects of Cd-induced hypertension. Use of GFR loss and albumin excretion in toxicological risk calculation, and derivation of Cd exposure limits, instead of β₂-microglobulin (β₂M) excretion at a rate of 300 µg/g creatinine, are recommended. Full article

Multimodal review helpfulness prediction (MRHP) aims to identify the most helpful reviews by leveraging both textual and visual information. However, prior studies have primarily focused on modeling interactions between these modalities, often overlooking the consistency between review content and ratings, which is a key indicator of review credibility. To address this limitation, we propose CRCNet (Content–Rating Consistency Network), a novel MRHP model that jointly captures the semantic consistency between review content and ratings while modeling the complementary characteristics of text and image modalities. CRCNet employs RoBERTa and VGG-16 to extract semantic and visual features, respectively. A co-attention mechanism is applied to capture the consistency between content and rating, and a Gated Multimodal Unit (GMU) is adopted to integrate consistency-aware representations. Experimental results on two large-scale Amazon review datasets demonstrate that CRCNet outperforms both unimodal and multimodal baselines in terms of MAE, MSE, RMSE, and MAPE. Further analysis confirms the effectiveness of content–rating consistency modeling and the superiority of the proposed fusion strategy. These findings suggest that incorporating semantic consistency into multimodal architectures can substantially improve the accuracy and trustworthiness of review helpfulness prediction. Full article

Lung cancer is one of the most common cancers and the leading cause of cancer-related death worldwide. Despite advancements, the overall survival rate for lung cancer remains between 10% and 20% in most countries. However, recent progress in diagnostic tools and therapeutic strategies has led to meaningful improvements in survival outcomes, highlighting the growing importance of personalized management based on accurate disease assessment. ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become essential in the management of lung cancer, serving as a key imaging modality for initial diagnosis, staging, treatment response assessment, and follow-up evaluation. Recent developments in radiomics and artificial intelligence (AI), including machine learning and deep learning, have revolutionized the analysis of complex imaging data, enhancing the diagnostic and predictive capabilities of FDG PET/CT in lung cancer. However, the limitations of FDG, including its low specificity for malignancy, have driven the development of novel oncologic radiotracers. One such target is fibroblast activation protein (FAP), a type II transmembrane glycoprotein that is overexpressed in activated cancer-associated fibroblasts within the tumor microenvironment of various epithelial cancers. As a result, FAP-targeted radiopharmaceuticals represent a novel theranostic approach, offering the potential to integrate PET imaging with radioligand therapy (RLT). In this review, we provide a comprehensive overview of FDG PET/CT in lung cancer, along with recent advances in AI. Additionally, we discuss FAP-targeted radiopharmaceuticals for PET imaging and their potential application in RLT for the personalized management of lung cancer. Full article

Background/Objectives: Myelodysplastic syndrome (MDS) is a heterogeneous clonal hematopoietic disorder characterized by ineffective hematopoiesis and leukemic transformation risk. Current therapies show limited efficacy, with ~50% of patients failing hypomethylating agents. This review aims to synthesize recent discoveries through an integrated network model and examine translation into precision therapeutic approaches. Methods: We reviewed breakthrough discoveries from the past three years, analyzing single-cell multi-omics technologies, epitranscriptomics, stem cell architecture analysis, and precision medicine approaches. We examined cell-type-specific splicing aberrations, distinct stem cell architectures, epitranscriptomic modifications, and microenvironmental alterations in MDS pathogenesis. Results: Four interconnected mechanisms drive MDS: genetic alterations (splicing factor mutations), aberrant stem cell architecture (CMP-pattern vs. GMP-pattern), epitranscriptomic dysregulation involving pseudouridine-modified tRNA-derived fragments, and microenvironmental changes. Splicing aberrations show cell-type specificity, with SF3B1 mutations preferentially affecting erythroid lineages. Stem cell architectures predict therapeutic responses, with CMP-pattern MDS achieving superior venetoclax response rates (>70%) versus GMP-pattern MDS (<30%). Epitranscriptomic alterations provide independent prognostic information, while microenvironmental changes mediate treatment resistance. Conclusions: These advances represent a paradigm shift toward personalized MDS medicine, moving from single-biomarker to comprehensive molecular profiling guiding multi-target strategies. While challenges remain in standardizing molecular profiling and developing clinical decision algorithms, this systems-level understanding provides a foundation for precision oncology implementation and overcoming current therapeutic limitations. Full article

In the trauma resuscitation literature, there are inconsistent definitions of what constitutes massive transfusion and a unit of blood, complicating the use of transfusion cut-points to declare futility. This is problematic as it can lead to the inefficient use of blood products, further exacerbating current blood product shortages. Previous studies have used various transfusion cut-points per hour to define futility in retrospective analyses but have not accurately defined futility at the bedside due to patient survival even at large rates and volumes of blood transfused. In an attempt to use transfusion cut-points as a marker to help define futility, guidelines have been proposed to limit blood product waste in transfusions for severely bleeding trauma patients, such as Suspension of Transfusion and Other Procedures (STOP) for patients older than 15 and the Futility of Resuscitation Measure (FoRM), used to determine futility in patients older than 60. In an effort to construct effective bedside futile resuscitation criteria with 100% positive predictive value and specificity, this review proposes the use of specific blood component transfusion cut-points combined with parameters from both STOP and FoRM to allow for a comprehensive and accurate method of declaring futility in severely bleeding trauma patients. Full article

Background/Objectives: Bladder cancer is a common malignancy with a high rate of recurrence and progression. Recent studies have identified that the urinary microbiome can be a key factor in tumor pathogenesis, progression, and outcomes. This narrative review is designed to summarize current evidence regarding the urobiome and explore its diagnostic and therapeutic potential. Methods: Studies between 2019 and 2024 were identified through the PubMed/MEDLINE and Google Scholar databases. Case reports and non-English-language articles were excluded. Results: The main findings revealed that specific bacteria, viruses, and taxa are linked to bladder cancer presence, progression, and response to immunotherapy treatment. Urinary microbiota differ by tumor type, sex, smoking status, and occupational exposure to toxins. Conclusions: Urinary microbiome and certain types of viruses present in urine may serve as promising tools to enhance bladder cancer diagnosis and predict treatment response. However, larger longitudinal studies are needed to confirm and establish these findings. Furthermore, integration of the urinary microbiome in clinical practice and public health strategies may reduce disease-related burden. Full article

The integration of artificial intelligence (AI) and advanced digital workflows is revolutionising full-arch implant dentistry, particularly for geriatric patients with edentulous and atrophic arches, for whom achieving both prosthetic passivity and optimal aesthetic outcomes is critical. This narrative review evaluates current challenges in intraoral scanning accuracy—such as scan distortion, angular deviation, and cross-arch misalignment—and presents how innovations like the Medit SmartX AI-guided workflow and the Scan Ladder system can significantly enhance precision in implant position registration. These technologies mitigate stitching errors by using real-time scan body recognition and auxiliary geometric references, yielding mean RMS trueness values as low as 11–13 µm, comparable to dedicated photogrammetry systems. AI-driven prosthetic design further aligns implant-supported restorations with facial symmetry and smile aesthetics, prioritising predictable midline and occlusal plane control. Early clinical data indicate that such tools can reduce prosthetic misfits to under 20 µm and lower complication rates related to passive fit, while shortening scan times by up to 30% compared to conventional workflows. This is especially valuable for elderly individuals who may not tolerate multiple lengthy adjustments. Additionally, emerging AI applications in design automation, scan validation, and patient-specific workflow adaptation continue to evolve, supporting more efficient and personalised digital prosthodontics. In summary, AI-enhanced scanning and prosthetic workflows do not merely meet functional demands but also elevate aesthetic standards in complex full-arch rehabilitations. The synergy of AI and digital dentistry presents a transformative opportunity to consistently deliver superior precision, passivity, and facial harmony for edentulous implant patients. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Introduction: Following up on treated high-grade cervical intraepithelial neoplasia (HSIL/CIN) lesions poses a challenge. Cervical cytology often has a high false-negative rate, while high-risk human papillomavirus (HR-HPV) DNA testing, though sensitive, lacks specificity. The detection of messenger RNA of the HR-HPV E6 and E7 oncoproteins (E6/E7 mRNA) is proposed as an indicator of viral integration, which is crucial for identifying severe lesions. Additionally, HPV vaccination could reduce recurrence rates in patients treated for high-grade cervical intraepithelial neoplasia. Objective: Our study aimed to assess the clinical utility of E6/E7 mRNA determination in the follow-up of HPV-immunized patients who were treated for HSIL/CIN. Methods: We conducted a retrospective observational study including 407 patients treated for HSIL/CIN. The recurrence rate and the validity parameters of E6/E7 mRNA testing were analyzed. Results: The recurrence rate for high-grade lesions was 1.7%. This low percentage might be related to the vaccination of patients who were not immunized before treatment. The sensitivity of the E6/E7 mRNA test was 88% at the first clinical visit, reaching 100% in the second and third reviews. Specificity was 91% at the first visit, 92% at the second, and 85% at the third. Regarding predictive values, the positive predictive value was 18% at the first visit, 10% at the second, and 14% at the third, while the negative predictive value was 100% across all follow-up visits. Conclusions: The E6/E7 mRNA test appears to be an effective tool for ruling out recurrence after treatment for HSIL/CIN lesions in HPV-immunized patients. Full article

The constitutive model is widely employed to characterize the rheological properties of metallic materials under high-temperature conditions. It is typically derived from a series of high-temperature tests conducted at varying deformation temperatures, strain rates, and strains, including hot stretching, hot compression, separated Hopkinson pressure bar testing, and hot torsion. The original experimental data used for establishing the constitutive model serves as the foundation for developing phenomenological models such as Arrhenius and Johnson–Cook models, as well as physical-based models like Zerilli–Armstrong or machine learning-based constitutive models. The resulting constitutive equations are integrated into finite element analysis software such as Abaqus, Ansys, and Deform to create custom programs that predict the distributions of stress, strain rate, and temperature in materials during processes such as cutting, stamping, forging, and others. By adhering to these methodologies, we can optimize parameters related to metal processing technology; this helps to prevent forming defects while minimizing the waste of consumables and reducing costs. This study provides a comprehensive overview of commonly utilized experimental equipment and methods for developing constitutive models. It discusses various types of constitutive models along with their modifications and applications. Additionally, it reviews recent research advancements in this field while anticipating future trends concerning the development of constitutive models for high-temperature deformation processes involving metallic materials. Full article

The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems. Full article

Atmospheric corrosion significantly impacts infrastructure worldwide, with traditional assessment methods being time-intensive and costly. This study developed a comprehensive machine learning framework for predicting atmospheric corrosion rates using environmental and material parameters. Three regression models (Linear Regression, Random Forest, and Gradient Boosting) were trained on a scientifically informed synthetic dataset incorporating established corrosion principles from ISO 9223 standards and peer-reviewed literature. The Gradient Boosting model achieved superior performance with cross-validated R² = 0.835 ± 0.024 and RMSE = 98.99 ± 16.62 μm/year, significantly outperforming the Random Forest (p < 0.001) and Linear Regression approaches. Feature importance analysis revealed the copper content (30%), exposure time (20%), and chloride deposition (15%) as primary predictors, consistent with the established principles of corrosion science. Model diagnostics demonstrated excellent predictive accuracy (R² = 0.863) with normally distributed residuals and homoscedastic variance patterns. This methodology provides a systematic framework for ML-based corrosion prediction, with significant implications for protective coating design, material selection, and infrastructure risk assessment, pending comprehensive experimental validation. Full article