Synthesis and biological evaluation of some pyrimidine derivatives as multifunctional ligands for the treatment of Alzheimer's disease.

Predicting suicide attempts in early-onset major depressive disorder: A nomogram-based approach.

Artificial intelligence has become one of the rapidly transformative forces in the pharma sector, also offering innovative solutions toovercome the longstanding challenges indrug discovery by the tally or analyzing the complex data set. AI finds short and accurate solutions to make the complex to simple. From the traditional method to modernization is the best example of AI. Making a personalized drug which is suitable for a person is set milestone in the Pharma sector, as it also optimizes the process of manufacturingbyensuringthequality.Whentheysupportpharmacovigilance,theythereeasilydetect adverse drug reactions. Though that leads face several challenges, such as data privacy modelling and transparency. The AI promises the Pharma sector to shape it into a more efficient, safe and patient- centered healthcare. s. In summary, there are immense possibilities with AI holds to enhance drug developmentbyimprovingefficiency, reducingcosts,andenablingmore personalizedtreatments.This review outlines the role of AI and current pharmaceutical challenges. AI has revolutionized drug discovery and development by enabling rapid and effective analysis of vast volumes of biological and chemical data during the identification of new therapeutic compounds. The algorithms developed can predict the efficacy, toxicity, and possible adverse effects of new drugs, optimize the steps involved in clinical trials, reduce associated time and costs, and facilitate the implementation of innovative drugs in the market,making iteasier to develop precise therapies tailored to the individual geneticprofile of patients. Despitesignificantadvancements,therearestill gapsintheapplicationof AI,particularlydue to the lack of comprehensive regulation. The constant evolution of this technology requires ongoing and in-depth legislative oversight to ensure its use remains safe, ethical, and free from bias. This review explores the role of AI in drug development, assessing its potential to enhance formulation,accelerate discovery,and repurpose existing medications. Ithighlights AI’s impactacross all stages, frominitialresearchtoclinicaltrials,emphasizingitsabilitytooptimizeprocesses,driveinnovation, and improve therapeutic outcomes.

Systemic sclerosis (SSc) is an autoimmune disease characterized by autoantibody production, fibrosis, and vasculopathy. The coexistence of ANCA-associated vasculitides (AAV) in SSc is rare and poorly characterized, with limited data on the impact of treatments, particularly high-dose glucocorticoids (GCs), on both conditions. This study aimed to describe the clinical phenotype, management, and outcomes of patients with overlapping SSc and AAV. We conducted a multicenter retrospective study in 18 French centers, including patients who met the 2013 ACR/EULAR criteria for SSc and the 2022 ACR/EULAR criteria for AAV. Clinical, biologic, and radiologic data were collected. We included 30 patients (median age 51.5 years, 83% female). SSc preceded AAV in all cases; 27% had diffuse cutaneous SSc, while 73% had limited cutaneous SSc. Anti-Scl70 antibodies were detected in 50%, and interstitial lung disease (ILD) was present in 80%, predominantly with a fibrosing non-specific interstitial pneumonia pattern (54%). AAV was microscopic polyangiitis in 90%, with MPO-ANCA positivity in 93%. Renal involvement was common (76%), with a median serum creatinine of 170 μmol/l (IQR 120-361) and proteinuria of 2 g/g (IQR 0.9-2.3). All patients received GCs in combination with cyclophosphamide (50%) or rituximab (47%). No cases of scleroderma renal crisis were observed. SSc manifestations, including ILD and skin involvement, remained stable during follow-up. AAV, predominantly microscopic polyangiitis with MPO-ANCA, can occur in SSc, particularly in patients with fibrosing ILD and anti-Scl70. Standard vasculitis treatments appear to be effective and do not worsen outcomes in SSc.

Sepsis is a life-threatening condition caused by a dysregulated host response to infection and remains a leading cause of death in intensive care units. Although antimicrobials and supportive care are vital, patient outcomes are hindered by two conflicting immune states: excessive inflammation and immune paralysis, both contributing to organ failure. Immunomodulatory nanotechnology provides a means to target both aspects of this immune response. Early nanocarriers improved the pharmacokinetics of antibiotics and anti-inflammatory drugs, while modern nanoplatforms enhance this approach with biomimetic coatings, toxin nanosponges, and extracellular vesicles. These tools neutralize Pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs), modulate Toll-like receptor (TLR) signaling, and reprogram macrophages with spatial and stimulus control. New nanodrugs combine pathway modulation with co-delivery of antimicrobials, and theranostic designs enable treatment tailored to real-time biological data. This review traces the evolution of nanomedicine for sepsis, discussing early advances, current therapies, and future innovations that may hasten clinical application. Literature for this review were searched for through PubMed and Google Scholar (2000-November 2025).

The Libyan coastline extends for nearly 2,000 km and hosts diverse marine habitats that support numerous vulnerable elasmobranch species. This study provides the first integrative assessment of artisanal fisheries targeting cartilaginous fishes in the Gulf of Sirte, central Libya. A frame survey was conducted from March to June 2024 following the FAO-GFCM protocol to document landing-site infrastructure, fishing fleets, gear types, species composition, catch estimates, and biological characteristics of selected species. A total of 43 landing sites were identified, of which 81% operate seasonally. The local fleet is composed entirely of small artisanal craft (“Fluka”), with 303 units recorded. The predominant fishing gears used for elasmobranchs were the traditional Kellabia net and longlines. Sixteen shark and ray species were documented, including several threatened taxa such as Carcharhinus plumbeus, Squatina squatina, and Rhinobatos cemiculus. Biological data were collected from embryos of Mustelus mustelus. Bycatch of turtles, dolphins, and seabirds was also recorded. Numerous ecological and socio-economic challenges were identified, including fishing during breeding seasons, pollution from oil operations, lack of management, and insufficient awareness among fishers. Recommendations are proposed to support sustainable elasmobranch conservation and fisheries management in the region.

Deep Learning (DL) and Machine Learning (ML) algorithms are adept at managing and classifying a wide range of data formats, including time series, text, and images, addressing challenges in both supervised and unsupervised learning. However, the practical applications of specific algorithms—particularly convolutional neural networks (CNNs) and vision transformers (VTs)—are often constrained by the need for large datasets, extensive training, and complex parameter tuning, which frequently relies on a trial-and-error approach. Other approaches, such as visibility graphs (VGs), often produce networks with an exceedingly high number of nodes, resulting in significant computational costs related to runtime and memory usage. Recent research has explored alternative feature extraction and classification solutions to address these challenges. One noteworthy innovation is the use of quantile graphs (QGs), initially applied to time series data, which transform data points into a complex network of quantiles. This method effectively identifies key structural patterns while minimizing computational requirements. These graphs have produced promising outcomes in analyzing physiological time series related to brain function and disorders, including Alzheimer’s disease. This research enhances quantile graphs for image identification and introduces a method for feature extraction applicable in ML and DL processes within the domain of computer vision. The novelty of this work is extending the QGs framework from one-dimensional time series to two-dimensional images, introducing a scalable graph-based approach for image classification, and providing an open-source implementation of the method. The study utilized two well-established benchmark datasets: the Modified National Institute of Standards and Technology (MNIST) handwritten digit database and Fashion MNIST. The performance of the proposed QGs was evaluated in comparison to that of CNNs and VTs. Our findings reveal that, while CNNs and VTs demonstrate superior accuracy in certain circumstances, the proposed QGs outperform these methods in other scenarios, particularly when training data is limited. Additionally, QGs yielded more consistent results across all situations, suggesting the choice of training components has less influence on them than CNNs and VTs. Moreover, the QGs were applied to a medical imaging dataset to illustrate their relevance to real biological data, indicating potential for integration into applications to detect brain diseases.

Laryngeal squamous cell carcinoma (LSCC) is recognized as the second most common malignant tumor of the respiratory tract. The study aimed to identify the roles of FOXO1, hsa-miR-96-5p, and lncRNA ADAMTS9-AS2 in the molecular pathogenesis of LSCC patients based on the systems biology data. The LSCC patient tissue samples (n = 50) and the same individual's adjacent normal tissues (n = 50) were collected from the candidates (aged 57.75 ± 9.3 years) of surgery. The miR-96-5p and lncRNA ADAMTS9-AS2 were predicted using the specific servers. The Kaplan Meier analysis was employed using TCGA data. The FOXO1and ncRNA gene expression levels were measured with the RT-qPCR technique. The Western blot technique was applied to estimate FOXO1/pFOXO1 protein values. A FOXO1/miR-96-5p/ADAMTS9-AS2 gene network was constructed and enriched using the bioinformatics data. The FOXO1 (p 0.037) correlated with ADAMTS9-AS2 (p 0.04) gene expression levels and was reduced in the LSCC patient tissue samples despite the elevated miR-96-5p expression levels (p 0.047). Moreover, the FOXO1 (p < 0.01) and pFOXO1 (p < 0.0001) protein values were reduced in the LSCC. The high FOXO1 and ADAMTS9-AS2 gene expression levels significantly increased the survival probability (HR 0.61 and 0.65, respectively). The FOXO1 and ADAMTS9-AS2 genes might act as molecular suppressors in the cell growth pathways. Furthermore, miR-96-5p is suggested as an oncogenic miRNA in the LSCC.

CD155, an emerging immune checkpoint, contributes to tumor immune evasion and progression, but its roles in metabolic reprogramming and tumor-associated macrophages (TAMs) polarization in lung adenocarcinoma (LUAD) remain uncharacterized. This study combines molecular biology, metabolic imaging, and clinical data to elucidate CD155's dual role in driving LUAD progression through glycolytic rewiring and immunosuppressive TAMs polarization. Through immunohistochemistry (IHC) and 18F-FDG PET/CT imaging, we analyzed CD155 expression and its association with glycolysis in 80 LUAD patients. Functional assays and molecular studies revealed CD155-mediated regulation of tumor glycolysis and macrophage polarization via YAP/TEAD1-GLUT1 signaling. Xenograft models validated the in vivo findings, with 18F-FDG micro-PET imaging performing noninvasive metabolic profiling. CD155 was significantly overexpressed in LUAD tissues and positively correlated with advanced TNM stage, lymph node metastasis and elevated 18F-FDG uptake. Mechanistically, CD155 interacts with YAP, reducing YAP phosphorylation at Ser127 to promote its nuclear translocation and TEAD1 activation, thereby upregulating GLUT1 transcription. This signaling axis enhanced glycolysis, thereby fueling LUAD proliferation and migration. Notably, CD155-induced lactate production and extracellular acidification drove macrophage polarization toward the immunosuppressive M2 phenotype. In vivo, CD155 silencing suppressed tumor glucose metabolism and growth, whereas overexpression accelerated tumor progression, both dynamically monitored through 18F-FDG PET visualization. We identify a novel CD155/YAP/TEAD1/GLUT1 axis that reprograms LUAD metabolism and facilitates immunosuppressive tumor microenvironment formation. CD155 functions as a metabolic-immune hub in LUAD, and its targeting could simultaneously suppress tumor growth and restore antitumor immunity, offering dual therapeutic advantages. Clinically, 18F-FDG PET/CT represents a noninvasive biomarker for CD155-driven metabolic aggression, potentially guiding precision immunotherapy.

Context Rutilus kutum stocks in the Caspian Sea have experienced long-term declines owing to intensified fishing and environmental stressors. Despite its ecological and economic importance, no integrative assessment of population dynamics has been conducted over an extended period. Aims To evaluate the status and productivity of R. kutum stocks over three decades, by using biological indicators and environmental drivers, and to inform adaptive fisheries management strategies. Methods Biological data were analysed to estimate growth parameters, mortality rates and exploitation levels. Novel application of generalised additive models was applied to assess recruitment relationships with seawater temperature, sea level, marine heatwaves and hatchery releases. Key results Exploitation rates frequently exceeded 0.5, indicating unsustainable fishing pressure. The length at 50% capture declined below the maturity threshold, increasing juvenile vulnerability. By 2021, abundance had dropped to less than one-third of 2006 levels. Generalised additive models explained 72.8% of recruitment variation, showing strong non-linear associations with environmental variables and hatchery output. Stock condition was rated ‘good’ in only 1 year. Conclusions Climate variability significantly affects stock productivity, and overfishing continues to undermine population resilience. Implications These findings support the need for ecosystem-based, adaptive management approaches that integrate environmental variability and regulate exploitation to conserve R. kutum stocks.

PurposeThe PRECISE-DYAD study is a prospective observational cohort, designed to investigate health outcomes among mother-child pairs (dyads), over the first three years of life in two contexts from sub-Saharan Africa. The primary objective of the study was to explore the effects of selected placenta-related complications, such as pregnancy hypertension, fetal growth restriction, and preterm birth, on 1) child health and development, and 2) women’s health and well-being, including outcomes after stillbirthParticipantsThe PRECISE-DYAD study enrolled women (and their children) originally recruited into the PRECISE regnancy cohort study in The Gambia and Kenya between July 2021 and April 2024. Participants were seen at 6 weeks to 6 months, 12 months, 24 months, and 36 months post-partum. Clinical and health data, including anthropometry and diet were collected for both mothers and children. Mother assessment included a cardiology assessment and collection of data about symptoms of COVID-19 infection. In a subset of participants, mothers were asked about their mental health, their health care costs during and after pregnancy, and experiences of care during labour and childbirth / delivery. Additonally, a personal environmental exposure assessement was performed for a subset of the cohort, by collecting air and water quality data alongside geographical, demographic, and behavioural factors. Child development was assessed using neurodevelopmental assessments, home environment evaluation, and quality of life measures. Biological samples were collected from mothers and children, processed promptly and biobanked locally. Sample data were entered into an OpenSpecimen database and linked to each individual, as well as to their corresponding social determinants and clinical data.Findings to dateA total of 2,980 women and 2,909 children completed at least one PRECISE-DYAD study visit. The biorepository contains 108,897 biological samples from mothers and children. Baseline descriptive analysis of the cohort are reported here.Future plansAnalysis of data and samples will include biomarker studies, social determinants of health, and epidemiological investigations. These analyses will explore how placenta-related complications and environmental exposures, such as nutrition and air quality, interact to shape maternal health, mental well-being, subsequent pregnancies, and mother-child interaction, as well as child growth and neurodevelopment through early childhood. Additional work will examine the biological pathways linking these exposures to outcomes and the impacts of caring for children with moderate-to-severe disabilities on maternal well-being. Findings will be disseminated through scientific publications, conference presentations, engagement with local stakeholders, and continued community outreach.Strengths and limitationsThis is a unique pregnancy-enrolled, population-based cohort with extensive social, clinical, and biological data, including biospecimens, collected across two geographically diverse settings in sub-Saharan Africa. Women were recruited at the time of booking for antenatal care, allowing early identification and longitudinal follow-up of those with placenta-related complications. The integration of PRECISE and PRECISE-DYAD data enables the comprehensive investigation of the drivers and impacts of placental disorders on maternal and child health, and outcomes related to the COVID-19 pandemic.Data were collected on women’s social and physical environments, including air quality, and water, sanitation, and hygiene (WASH) conditions. In-depth data were also gathered on children, with a focus on neurodevelopmental assessments.Consistent data collection procedures and standardised methodologies were used across both study sites.Extensive and sustained community engagement, including 108 sensitisation meetings with nearly 4,000 participants, enhanced trust, study understanding, and acceptability.A limitation of the study is the loss to follow-up of participants who relocated outside of the study area during pregnancy or after the child’s birth, or changed their contact details.A second limitation is that the Mozambique pregnancy cohort has provided only air quality data through PRECISE-DYAD, and has not been followed up otherwise at this time.

In recent years, major psychiatric disorders have been intensively researched. Studies have investigated the pathophysiology of these disorders in detail and at various molecular levels with several omics techniques, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. However, although the results of asingle omics study can help shed light on some of the unclear aspects of the biological circuits involved in the pathophysiology of major psychiatric disorders, the complexity of the biological mechanisms underlying these conditions makes it necessary to consider multiple types of omics data and multiple levels of analysis, including various conceptional, methodological, and quality control criteria. Currently, dealing with high-dimensional data and sparse heterogeneous data structures remains one of the biggest challenges to integrating data from multi-omics approaches. The hope is that eventually the development and application of methods to integrate biological and phenotypic data through multi-omics and machine learning-based algorithms may allow early diagnosis of major psychiatric disorders, perhaps even before disease onset, and enable accurate, personalized treatment. In this mini-review, we summarized the main findings of the field by reviewing systematic reviews, meta-analyses, and narrative reviews on the major psychiatric disorders schizophrenia, bipolar disorder, and major depressive disorder.

Past research has shown that inflammation is reduced among marijuana-using HIV-negative people but not those living with HIV. We take this work a step further by assessing differences based on pre-exposure prophylaxis (PrEP) use among HIV-negative individuals. National Couple's Health and Time is a nationally representative cohort study of 3642 adult respondents who are married or cohabiting. Their ages range from 20 to 60 years with 45% self-identifying as nonheterosexual. Biologic data (n = 573; C-reactive protein [CRP], interleukin-6 [IL-6], and Epstein Barr virus [EBV] antibody levels) were collected through finger stick dried blood spots as part of National Couple's Health and Time-BIO, a substudy. Participants self-reported demographic characteristics, PrEP use, and marijuana use. Multivariable regression analyses were used to assess the relationship between these variables and each of the measured biomarkers, adjusting for known confounders. In adjusted models, neither lifetime nor current PrEP use was associated with CRP, IL-6, or EBV antibody levels. Moreover, marijuana use did not differ among those who used PrEP versus those who did not. Among PrEP users, those who reported marijuana use had lower CRP than those who did not (B = -2.31; 95% CI: -4.23 to -0.40). Among non-PrEP users, no association was observed between marijuana use and CRP. The current preliminary data suggest inflammation is reduced among PrEP users who also use marijuana, but the same is not true among non-PrEP users. These findings may suggest that PrEP increases inflammation that is then partially mitigated by the individual cannabinoids or cannabidiols found in marijuana, although more research is needed to confirm this hypothesis.

Decoding of Inconsistent Biological Data: A Critical Step toward Enhanced AI Predictivity in Drug Discovery

Understanding terrestrial locomotion in walking fish species can unlock new insights into vertebrate evolution and inspire versatile robotic systems capable of traversing diverse environments. We introduce a novel, single-actuator continuum robot inspired by the terrestrial locomotion of the gray bichir (Polypterus senegalus), which employs a simple rotating helix to reproduce realistic undulatory movements. We hypothesized that a simplified robotic model with minimal actuation could accurately replicate the terrestrial locomotion patterns observed inP. senegalus. Using a 'robot-twin' methodology, we developed four helix configurations directly informed by the observed gait postures of real fish specimens and compared robotic performance and kinematics against biological data. We found that helix geometry significantly influenced both locomotion speed and lateral stability, with designs closely mimicking biological curvatures often exhibiting trade-offs between accuracy and performance. The fastest helix configuration produced the greatest lateral oscillation, whereas the most biologically accurate shape resulted in reduced locomotion efficiency. Additionally, integrating passive leg structures greatly enhanced stability, mirroring the biomechanical function of pectoral fins in the real fish. These findings underscore the value of minimalistic robotic designs in understanding fish-like locomotion and pave the way for future robotic platforms using reduced degrees of freedom.

ABSTRACT Objective Variability in somatic growth of marine fish can affect their reproductive potential and survival and, therefore, the productivity of a population. Understanding how growth might vary among species can improve predictions of population status and responses to environmental change. Our objective was to characterize the variability in growth and body condition of groundfish species along the U.S. West Coast to support their monitoring and assessment. Methods We used geostatistical models to estimate growth rate and body condition, two interrelated traits associated with somatic growth, across space and time for nine commercially important U.S. West Coast groundfish species. We fit generalized linear mixed models with Gaussian Markov random fields to biological data collected from annual bottom trawl surveys to estimate variability at a 4- × 4-km spatial resolution. Results Our models uncover spatiotemporal variability in growth rate and body condition in all nine groundfish species with limited trends shared among species with similar traits, suggesting a greater influence from niche partitioning acting on local scales. Such interspecific differences in growth rate and body condition also occurred at regional scales, with some species exhibiting positive responses while others declined. Conclusions These findings reveal the dynamic nature of somatic growth among groundfish species and provide insight into potential mechanisms of its variability that could be considered within climate-enhanced assessments of population status for marine fish.

The National Genomics Data Center (NGDC), as part of the China National Center for Bioinformation (CNCB), provides a suite of database resources for worldwide researchers. As multi-omics big data and artificial intelligence reshape the paradigm of biology research, CNCB-NGDC continuously updates its database resources to enhance data usability, foster knowledge discovery, and support data-driven innovative research. Over the past year, notable progress has been achieved in expanding the scope of high-quality multi-omics datasets, building new database resources, and optimizing extant core resources. Notably, the launch of BIG Search enables cross-database search services for large-scale biological data platforms, including NGDC, National Center for Biotechnology Information (NCBI), and European Bioinformatics Institute (EBI). Additionally, several new resources have been developed, covering genome and variation (Hiland Resource, TOAnnoPriDB), expression (TEDD), single-cell omics (PreDigs, scMultiModalMap, TE-SCALE), radiomics (TonguExpert), health and disease (CAVDdb, IDP, MTB-KB, ResMicroDb), biodiversity and biosynthesis (SugarcaneOmics), as well as research tools (Dingent, miMatch, OmniExtract, RDBSB, xMarkerFinder). All these resources and services are freely accessible athttps://ngdc.cncb.ac.cn.

Introduction: Human Immunodeficiency Virus (HIV) infection remains a major public health problem, particularly in sub-Saharan Africa. Despite expanded access to antiretroviral therapy (ART), many patients are still admitted at an advanced stage of the disease, characterized by severe immunosuppression and high morbidity. In this context, the evaluation of biological parameters is an essential tool for the monitoring and management of patients with advanced HIV disease (AHD). Methodology: A retrospective and analytical study was conducted at the Care, Training, and Research Unit (CTRU) of Donka National Hospital over a four-year period (2016-2020). All patients aged 15 years or older hospitalized for advanced HIV infection (CD4 &lt; 200 cells/mm³ or clinical stage III/IV according to the WHO) were included. Sociodemographic, clinical, and biological data were collected and analyzed using Epi Info 7.2.2.6 software, with a significance threshold set at p &lt; 0.05. Proportions were compared using the chi-square (χ²) test, while means were compared using analysis of variance (ANOVA). Results: A total of 2,236 HIV-infected patients were hospitalized during the study period. Of these, 1,861 (83.22%) were over 15 years of age, in accordance with the inclusion criteria, and were included in the analysis. The study population was predominantly female, with 1,144 women (61.47%) compared to 717 men (38.53%), representing a male-to-female ratio of 0.6. The most represented age group was 35 to 44 years old (590 patients, or 31.70%). The average age of participants was 38.69 ± 12.07 years, ranging from 15 to 79 years. HIV-1 accounted for 99.2% of cases. Nearly half of patients (48.1%) had a CD4 count &lt; 50 cells/mm³ and 89.6% had a detectable viral load. Severe anemia (&lt; 8 g/dL) was observed in 47.6% of patients, leukopenia in 45.1%, and neutropenia in 50%. Renal failure (creatinine &gt; 150 µmol/L) was observed in 44.4% of cases. Mortality was significantly associated with WHO stage IV (p = 0.00002) and a CD4 count &lt; 50 cells/mm³ (p = 0.00003). Conclusion: This study highlights the high frequency of biological abnormalities in patients admitted to the USFR Donka in the advanced stages of HIV infection, characterized by profound immunodeficiency, severe cytopenias, and largely uncontrolled viral load. The major prognostic factors for mortality identified clinical stage IV and CD4 count &lt; 50 cells/mm³ confirm the severity of these advanced forms.

BackgroundGiant cell arteritis (GCA) is the leading vasculitis threatening vision in adults aged ≥ 50 years; permanent vision loss may occur within the first few days after symptom onset. We assessed the impact of a fast-track pathway (FTP) for early diagnosis and treatment of giant cell arteritis in terms of hospitalization patterns and cost-effectiveness.MethodsWe conducted a retrospective, single-center medico-economic study of consecutive patients referred to a neuro-ophthalmology tertiary center between Nov 1, 2016, and Dec 31, 2022. GCA was defined by ≥ 3 American College of Rheumatology criteria plus a positive temporal-artery biopsy or vascular imaging. An FTP—24/7 access to internal medicine specialists, priority magnetic-resonance imaging, and protocol-driven corticosteroid initiation—was launched on Nov 1, 2018. Demographic, clinical, biological, care-pathway, and cost data were compared before (pre-FTP) and after (post-FTP) implementation. Continuous variables were analyzed with two-sample t tests or Wilcoxon rank–sum tests; categorical variables with χ² or Fisher’s exact test.FindingsWe included 135 patients (mean age 76 ± 8 years, 61% women): 23 pre-FTP and 112 post-FTP. Baseline characteristics were similar between groups. Compared with the pre-FTP period, the FTP reduced full hospitalizations (62% [69/112] vs 96% [22/23]; p < 0.01) and increased day-hospital or outpatient management (39% vs 4%; p < 0.01). More patients received treatment within one month of symptom onset (54% vs 22%; p < 0.01). Final visual acuity improved (median 2.0 vs 2.6 logMAR; p < 0.01), while cumulative intravenous corticosteroid exposure was significantly reduced (1679 ± 760 mg vs 2295 ± 1055 mg; p = 0.02). Reliance on temporal-artery biopsy fell (17% vs 91%; p < 0.01), owing to a four-fold rise in diagnostic MRI use. Mean total medical costs decreased by €814 per patient (€3672 ± 2861 vs €4486 ± 3193), although this difference did not reach statistical significance (p = 0.23).InterpretationA dedicated fast-track pathway for suspected GCA enables prompt, largely ambulatory care, halves unnecessary full hospitalizations, speeds treatment initiation, improves visual prognosis, and lowers overall expenditure. These findings support wider adoption of imaging-driven FTPs to mitigate the growing clinical and economic burden of GCA.

This paper presents an improved method for selecting the best features for data, based on the combination of the mutual information (MI) method and the chaotic binary bat algorithm (CBBA). The proposed method, named MI-CBBA, is based on three stages: (1) MI is used to rank the most relevant features in order of importance from the highest to the lowest importance, (2) a chaotic sine map is used to generate the initial population parameters for the binary bat algorithm, and (3) the binary bat algorithm is applied as an additional stage to reduce the dimensionality of the data and obtain the best features. The results obtained through application to biological data show that the proposed MI-CBBA algorithm has higher classification accuracy with a smaller number of selected features compared to the standard bat algorithm.