Search Results (57,936)

Alcohol-associated liver cirrhosis (ALC) lacks aetiology-specific molecular diagnostic biomarkers. This study aims to quantify the association between alcohol and cirrhosis risk, and to identify transcriptomic diagnostic biomarkers and candidate therapeutics. Methods: Survey-weighted logistic regression was applied to 17,007 adults from NHANES (2017–2023) to quantify alcohol-cirrhosis associations. ALC transcriptomic data from four GEO datasets were analysed using weighted gene co-expression network analysis (WGCNA) and three parallel machine learning algorithms (LASSO, Random Forest, SVM-RFE). External validation was performed in an independent cohort of 93 samples. Candidate therapeutics were identified via drug signature database querying and validated by molecular docking. Heavy drinking conferred a 5.14-fold increased cirrhosis risk (95% CI: 2.60–10.20, p < 0.001). Transcriptomic analysis revealed global downregulation of long non-coding RNAs (with 91.7% of dysregulated lncRNAs being suppressed). A five-gene diagnostic signature (IL1B, CCL3, LUM, SPP1, ITGA6), specifically developed to distinguish ALC from histologically normal liver tissue, achieved an area under the receiver operating characteristic curve (AUC) of 0.824 in an external validation cohort. Immune infiltration analysis uncovered global contraction of macrophage-associated transcriptomic signatures across M0, M1, and M2 subtypes, inversely correlated with fibrotic hub gene upregulation. Fluvastatin and honokiol were identified as candidate therapeutic agents, with strong binding affinities to IL1B and CCL3, respectively. This study confirms a dose-dependent alcohol-cirrhosis association and establishes a five-gene diagnostic signature (distinguishing ALC from normal liver tissue) alongside candidate therapeutics, warranting prospective clinical validation. The identified tissue-derived signature and therapeutic candidates provide a foundation for future ALC-specific diagnostic and therapeutic strategies; their translation into a non-invasive (e.g., blood-based) assay will require dedicated validation in circulating samples. Full article

Gastrointestinal (GI) cancers account for a quarter of all cancer cases worldwide and are responsible for a third of cancer deaths. One of the characteristic features of GI tissue is its multilayered structure, which, in addition to multiple scattering, complicates optical spectral analysis. The use of spectroscopic diagnostics and photodynamic therapy for the detection and treatment of GI cancer is a rapidly developing field. The method proposed in this paper for layer-by-layer optical properties assessment, suitable for real-time clinical application to the walls of hollow organs, allows us to calculate the absorbed dose layer by layer. This paper proposes a method for recording spectral data in two geometries, diffuse reflectance and transmission, using light delivery from both the external and internal surfaces of the gastrointestinal tract wall. Layer-by-layer assessment of optical properties was performed using a developed algorithm based on the inverse adding–doubling method with initial optical properties values determined using the modified two-stream Kubelka–Munk model with the accuracy equal to 86 ± 13%. The method was approved in clinical conditions. Based on the results of the work, the developed method for assessing the optical properties of multilayered biological tissues exhibited sufficient speed and accuracy for in vivo application to personalize laser-induced therapy by correction of the laser dose. Full article

Background: Ultrasonography during medical procedures allows for real-time assessment of the target structure. This design feature could be more advantageous than radiological “snapshot” imaging. Technically, the image resolution of endoscopic ultrasound (EUS) is noticeably higher than that of transesophageal echocardiography (TOE). The benefits of investigating cardiovascular structures using this mode of high-resolution EUS are unknown. Materials and Methods: We present clinical applications in the diagnosis of cardiovascular structures, demonstrated during routine gastrointestinal endosonographic procedures. In some cases, these diagnoses led to changes in management strategies. Results and Discussion: The introduction of high-resolution EUS into cardiology allows for the accurate definition of variable cardiovascular anatomy and early detection of asymptomatic cardiac pathologies. It also prevents double investigations for patients and operators, reduces the risk of esophageal trauma, and highlights the benefits of interdisciplinary teamwork. In addition, the high spatial resolution of EUS facilitates detailed tissue characterization for guiding biopsies, thereby extending the applicability of elastography across various echocardiographic domains. Moreover, the precision of using EUS when targeting and inserting a needle into adjacent organs could facilitate the development of EUS indications for cardiac-based interventions. Conclusions: The use of EUS in cardiology provides high-resolution real-time assessments of cardiovascular anatomy and may facilitate the development of EUS indications for cardiac-based interventions. However, large cohort studies are required. Full article

Non-small cell lung cancer (NSCLC) exhibits genomic heterogeneity that affects tumor immunogenicity and PD-L1 expression. Patient clustering based on shared mutational profiles using social network analysis (SNA) has been narrowly explored. The study aimed to identify subgroups of NSCLC patients with similar somatic mutation profiles using network-based modularity clustering, and to compare these groups with respect to PD-L1 expression, Tumor mutation burden (TMB), and clinical variables. Data of patients with stage 4 (metastatic) NSCLC, whose tumor tissue samples were collected between 2022 and 2024, were analyzed. This retrospective study included NSCLC patients harboring actionable driver mutations in genes such as EGFR, KRAS, ALK, BRAF, MET. A social network of 129 patients was constructed. Two distinct genomic clusters were identified. Cluster 2 (n = 55) showed a higher prevalence of KRAS, TP53, BRAF, STK11 and additional mutations, while cluster 1 (n = 74) displayed a limited number of driver mutations. Cluster 2 had significantly higher PD-L1 expression (29.8% vs. 13.7%, p = 0.001) and higher TMB (7.8 vs. 5.8, p = 0.021). In multivariate logistic regression, both PD-L1 and TMB were associated with cluster assignment (p < 0.05). Mutation-based SNA clustering delineated two biologically distinct subgroups of NSCLC patients. The highly mutated cluster displayed higher PD-L1 expression and TMB, a profile consistent with a more immunogenic phenotype. This method offers a novel integrative approach that requires prospective validation before clinical implementation. Full article

Background: Virtual reality (VR) is a promising technology for the relief of physical and psychosocial burdens. We found that individualised VR videos were well tolerated and accepted and seemed to have a stronger effect on well-being and emotional connection than standardised VR in cancer inpatients under palliative care. For implementation, it is important to actively involve patients, as their input helps to ensure that the VR intervention meets their needs, thus making it more likely to be accepted and effective in practice, while balancing the needs of healthcare professionals. Aim: Exploration of patients’ and healthcare professionals’ perspectives on best practice VR intervention implementation. Design: Workshop-based 360° focus group using a strengths–weaknesses–opportunities–threats (SWOT) model and deductive/inductive qualitative analysis with a ‘framework’ approach. Setting/participants: The focus group took place at the National Centre for Tumour Therapy of a German university hospital. Participants were a local doctor (1) and nurses (3) with VR experience, the cooperating patient advisory board of the study (2), and members of a regional self-help group (3). Results: Eighteen subthemes were identified in the SWOT model. While there was agreement on the ‘strength of distraction’ and ‘opportunities of individualised VR’, concerns remained regarding data protection when using private VR content. There was an argument about gatekeeping by relatives worried about mental distress in patients immersing in home or family VR scenes. In contrast, many ideas were discussed regarding how to overcome rejectionist staff attitudes. However, the high organisational time and staff deployment were addressed as major weaknesses. Conclusions: Involving patient stakeholders and healthcare professionals in the planning of the implementation strategy revealed several issues that require attention. In particular, information needs to be provided not only to patients but also to relatives and hospital staff, alongside ensuring data protection and adequate staffing. Trial registration: Registered at German Clinical Trials Register (Deutsches Register Klinischer Studien; DRKS); registration number: DRKS00032172; registration date: 11 July 2023. Full article

Stroke remains a major cause of disability worldwide, and population-level indicators that integrate multidimensional vulnerability with physiological reserve may provide useful perspectives for characterizing cerebrovascular health in aging populations. This cross-sectional study examined the association between the Frailty-to-estimated Cardiorespiratory Fitness Ratio (FCR) and self-reported prevalent stroke among middle-aged and older adults using NHANES 2011–2014 data. FCR was calculated as a 23-item modified Frailty Index divided by estimated cardiorespiratory fitness, providing an interpretable load-to-reserve measure that linked accumulated health deficits with estimated cardiorespiratory reserve. The final analytical sample included 3511 participants aged 45 years or older. Survey-weighted logistic regression models, sensitivity analyses, exploratory subgroup analyses, and spline models were used to evaluate the association. The weighted prevalence of self-reported prevalent stroke increased across FCR quartiles, from 2.2% in Q1 to 12.9% in Q4. In the conventional clinical adjustment model, participants in the highest FCR quartile had greater odds of self-reported prevalent stroke than those in the lowest quartile (OR = 6.13, 95% CI: 2.97–12.66; $p<0.001$), with similar findings in the overlap-aware model (OR = 6.31, 95% CI: 3.07–12.97; $p<0.001$). Log-transformed FCR was also consistently associated with greater odds of self-reported prevalent stroke across adjustment models, and spline analysis suggested a generally increasing association. These findings support FCR, particularly when modeled using quartiles or log-transformed values, as an interpretable integrative load-to-reserve construct associated with self-reported prevalent stroke, and suggest its potential relevance for population-based characterization of cerebrovascular health in aging adults. Full article

Background/Objectives: Chronic lymphocytic leukemia (CLL) is characterized by a highly heterogeneous clinical course, with some patients remaining stable for years while others require early treatment. Identifying reliable and easily accessible predictors of treatment requirement at diagnosis remains an important clinical challenge. Methods: This retrospective cohort study included 226 patients diagnosed with CLL between 2015 and 2024 at a tertiary care center. Baseline demographic, clinical, and laboratory parameters were analyzed. Univariate and multivariable logistic regression analyses were performed to identify independent predictors of treatment requirement. A hierarchical mixed-effects model was constructed to account for temporal clustering across diagnostic periods. A clinical risk score was derived from independent predictors, using regression coefficient-based weighting, and its discriminative performance was evaluated using receiver operating characteristic (ROC) analysis. Results: A total of 226 patients were included (mean age: 62.4 ± 13.8 years; 56.6% male). During follow-up, 104 patients (46.0%) required treatment. Lower hemoglobin and platelet levels, higher lymphocyte counts and LDH levels, and the presence of B symptoms, splenomegaly, and advanced disease stage were independently associated with treatment requirement. These associations remained significant in hierarchical modeling. The derived risk score demonstrated acceptable discriminative ability (AUC: 0.84; 95% CI: 0.79–0.89), with a cut-off value of ≥4 yielding a sensitivity of 81.7% and specificity of 73.8%. Conclusions: Baseline clinical and laboratory parameters are associated with treatment requirement in CLL. A combination of readily available variables may support risk stratification at diagnosis. The proposed risk score may provide a practical adjunct to routine clinical assessment, particularly in settings where advanced molecular testing is not readily available; however, external validation in independent cohorts is required before clinical implementation. Full article

Aspartame is a widely used artificial sweetener, but its possible relationship with rheumatoid arthritis (RA) remains insufficiently understood. This study aimed to explore, rather than prove, potential molecular links between aspartame-related targets and RA-associated gene networks. Three public RA transcriptomic datasets (GSE55235, GSE55457, and GSE77298) from the Gene Expression Omnibus (GEO) database were integrated as discovery/training data. Because these datasets included different tissue origins, batch correction was used to reduce dataset-level technical variation, whereas tissue-origin-related biological variation was not assumed to be fully removable. After differential expression analysis, RA-associated differentially expressed genes (DEGs) were identified. The single-cell dataset GSE200815 was used for cell annotation and cellular expression visualization; because its comparator group consists of psoriatic arthritis (PsA) samples rather than healthy controls, single-cell results were interpreted as RA-vs-PsA observations and were not treated as disease-versus-healthy-control evidence. Potential targets of aspartame were retrieved from ChEMBL, SwissTargetPrediction, and the Similarity Ensemble Approach (SEA), and were intersected with RA-related DEGs to construct an aspartame-gene-RA regulatory network. Diagnostic models were developed using 113 machine-learning algorithm combinations to determine an optimal multigene model and its core genes. HLA-DRB1 was selected for exploratory scTenifoldKnk-based virtual knockout mainly because it was included in the optimal model and has a well-established role in RA immunogenetics; the single-cell analysis was used only to describe cellular distribution in the RA/PsA dataset. Molecular docking was then used to evaluate the possible interaction between aspartame and HLA-DRB1. Forty-four intersected genes linked the predicted aspartame targets with RA DEGs. The random forest plus partial least-squares generalized linear model (RF + plsRglm) identified 16 core genes. Network-level interpretation indicated that these genes were distributed across immune/antigen-processing, inflammatory-signaling, protease/extracellular-matrix-remodeling, adhesion, metabolic, and proliferation-related modules; therefore, HLA-DRB1 was treated as a prioritized immune-module candidate rather than as the sole driver of the network. Following virtual knockout of HLA-DRB1, affected genes were enriched in extracellular matrix organization, extracellular structure organization, extracellular matrix, collagen trimer, extracellular matrix structural constituent, and collagen binding. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included integrin signaling, focal adhesion, proteoglycans in cancer, cytoskeleton in muscle, and phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) signaling. Molecular docking showed a minimum binding energy of −6.7 kcal/mol, which was more negative than the preset stability criterion of −5.0 kcal/mol, and the docking pose suggested contacts around ARG-146. This integrative analysis suggests a hypothesis-generating association between aspartame-related predicted targets and RA-relevant molecular networks involving HLA-DRB1 and other core genes. The findings do not establish causality and require experimental, epidemiological, biophysical, and tissue-stratified validation before any causal or clinical inference can be made. Full article

Background/Objectives: Medication adherence is a critical determinant of therapeutic outcomes in type 2 diabetes mellitus (T2DM); however, its relationship with glycemic control remains inconsistent, particularly in real-world and socially vulnerable settings. This study aimed to evaluate medication adherence using multiple validated instruments, assess disease-related knowledge, and examine their relationship with glycemic control, with a focus on potential discordance between self-reported adherence and objective metabolic outcomes. Methods: A cross-sectional analytical study was conducted with 237 adults with T2DM receiving care in a primary health care (PHC) unit in the Brazilian Amazon. Medication adherence was assessed using the Almeida Adherence Scale, ARMS-12, and the Haynes–Sackett test, while disease-related knowledge was evaluated using the Batalla–Martínez questionnaire. Glycemic control was determined based on glycated hemoglobin (HbA1c) values obtained from clinical records within the previous three months. Descriptive and comparative analyses were performed. Results: The study population was predominantly female (64.1%) and aged 40–59 years (55.7%), with a high prevalence of socioeconomic vulnerability. Non-adherence was identified in 55.7% of participants using the Almeida Adherence Scale, whereas higher adherence rates were observed with ARMS-12 (91.1%) and the Haynes–Sackett test (72.2%). Inadequate disease-related knowledge was found in 77.2% of participants. Among individuals with available HbA1c data (n = 116), the mean HbA1c was 8.63% (SD = 1.65), and 81.9% presented inadequate glycemic control (HbA1c ≥ 7%). Notably, among participants classified as adherent by the ARMS-12 scale (91.1%), inadequate glycemic control was nonetheless present in 81.9% of those with available HbA1c data, illustrating the magnitude of the observed discordance between self-reported adherence and objective metabolic outcomes. Cross-tabulation of each adherence instrument against glycemic control showed no statistically significant associations (chi-square with Yates correction; ARMS-12: p = 0.631, φ = 0.045; Almeida Adherence Scale: p = 0.301, φ = 0.096; Haynes–Sackett: p = 0.800, φ = 0.024). Multivariable logistic regression (Nagelkerke R² = 0.321; AUC = 0.834) identified older age (aOR = 0.92; 95% CI: 0.87–0.96; p < 0.001) and higher income (aOR = 9.96; 95% CI: 2.05–48.32; p = 0.004) as independent predictors of glycemic outcome, while no adherence measure was independently associated with HbA1c ≥ 7%. A sensitivity analysis using HbA1c ≥ 8.0% revealed poor control in 59.5% of participants (n = 69/116). Conclusions: Despite varying levels of self-reported medication adherence, inadequate glycemic control was highly prevalent. The absence of statistically significant associations between self-reported adherence and HbA1c, combined with the high prevalence of poor glycemic control regardless of adherence status, is consistent with the hypothesis that adherence alone does not fully explain metabolic outcomes in T2DM. Given the cross-sectional design, no causal inferences can be drawn. These findings highlight the need for integrated care strategies in primary health care, including improved health literacy, structured pharmacotherapeutic follow-up, and the use of multiple adherence assessment tools to better inform clinical decision-making. Full article

Background/Objectives: The DPYD gene encodes the enzyme dihydropyrimidine dehydrogenase that metabolizes fluoropyrimidines. Genetic variants in DPYD have been associated with altered enzyme activity; therefore, accurate detection and interpretation is critical for individualized fluoropyrimidine therapy. The most common causal variant is c.1129-5923C>G (rs75017182) located in intron 10, which introduces a cryptic splice site. This variant is in high linkage disequilibrium (LD) in the HapB3 haplotype with a benign synonymous variant in exon 11, c.1236G>A (rs56038477). Since c.1129-5923C>G and c.1236G>A have been reported in LD, many commercial kits use c.1236G>A as a proxy for the function-altering intronic variant. Methods: A DPYD genotyping protocol was implemented following the quality regulations that apply to clinical laboratories (EN-ISO9001:2015 and EN-ISO15189:2022). NGS, MLPA and Sanger sequencing were used for validation purposes. Results: Over the last 5 years a total of 2007 patients have been analyzed at our department. The observed DPYD genotype frequencies aligned with those observed in European populations. Importantly, we have identified a patient harboring the c.1236G>A variant, but in the absence of the c.1129-5923C>G variant. This last result supports recently published findings suggesting that these two variants may not be in perfect LD, as previously assumed, and lead to suboptimal dosing for those patients carrying this allele. Finally, low frequency variants (c.496A>G, c.2194G>A, and c.1601G>A), not described in DPYD analysis guidelines recommendations, were found in two patients who required fluoropyrimidines dose adjustment. Conclusions: These findings highlight the limitations of relying on proxy variants for clinical decision-making, as incomplete linkage disequilibrium may lead to misclassification of patients’ metabolic capacity. Furthermore, in order to provide safer protocols for DPYD-based personalized treatment genetic panels should expand to include additional rare DPYD variants. Full article

Daunomycin (daunorubicin) is one of the most clinically significant anthracyclines used in chemotherapy, and its efficient production remains a major objective for biotechnological researchers. Industrial manufacturing relies on the fermentation of Streptomyces peucetius and Streptomyces coeruleorubidus, which produce daunomycin as a secondary metabolite under controlled conditions. This review will focus on the methods to enhance the total efficiency of biotechnological production, from upstream biosynthesis to downstream processing. Given the complexity of the daunomycin biosynthetic pathway in Streptomyces spp., substantial progress has been made in strain improvement to increase yield, metabolic robustness, and process stability. Advances in classical mutagenesis, pathway engineering, regulatory network modulation, and precursor supply optimization, along with rational medium design and advanced process control, have led to substantial increases in product titers and productivity. At the same time, innovations in downstream processes, such as extraction, purification and process integration, have increased recovery efficiency, product quality, and economic feasibility. With improvements in the production process, novel drug delivery modalities have been developed (e.g., drug carriers based on erythrocytes, drug nanocarriers based on hyaluronic acid) with increased efficiency and lower systemic toxicity. These developments indicate an evolution from pathway-level engineering to industrial-scale manufacturing and clinical application, underlining the evolution of daunomycin research and biotechnological production. Full article

Background: Metastasis and poor chemotherapy response have stagnated therapeutic progress in osteosarcoma (OS) for the past three decades. Defining the transition from localized to metastatic OS before overt dissemination is fundamental for improving survival. However, effective early diagnostic tools remain scarce, largely due to limited exploitation of the metastasis-associated tumor microenvironment’s own record of prior environmental and stress exposures encoded in cell-intrinsic transcriptional states. Here, we employed a supervised machine learning framework with iterative resampling and multi-stage model selection to identify molecular markers associated with metastasis in osteosarcoma and to develop a computational signature, Auto-RS. Methods: Transcriptomic and clinical data from 139 OS patients with ≥5 years of follow-up were analyzed. A LASSO–Cox framework was applied to derive a gene expression-based risk score, Auto-RS, from which a nomogram integrating age and sex was generated for individualized prognosis. Model interpretability was assessed across six independent single-cell OS patient datasets, and drug sensitivity predictions were inferred by integrating Auto-RS with the Precily algorithm to uncover actionable therapeutic vulnerabilities. Results: Auto-RS, constructed from the expression of four autophagy genes (BNIP3, MYC, PEA15, and SAR1A), served as an independent prognostic factor for overall survival (HR = 1.091; 95% CI, 1.047–1.136; p < 0.001). Time-dependent ROC analysis showed that Auto-RS was the most accurate single predictor (AUC = 0.88), exceeding metastasis (0.83), sex (0.45), and age (0.39). A basic prognostic model (BpM) incorporating metastasis status yielded a C-index of 0.741 (95% CI, 0.679–0.803). The addition of Auto-RS (CpM) improved discrimination (C-index = 0.788; 95% CI, 0.731–0.845), whereas a model without metastasis information (ApM) retained predictive ability (C-index = 0.709; 95% CI, 0.640–0.778). Single-cell analysis confirmed that Auto-RS features aligned with known metastatic trajectories, reflecting the transition from proliferative to invasive tumor states and highlighting coordinated programs among cancer-associated fibroblasts and immune cells. Drug sensitivity integration through Precily identified gemcitabine and cytarabine as FDA-approved agents predicted in silico to show greater sensitivity in the high-risk subgroup. Conclusions: We identified autophagy-mediated transcriptional ‘stress fingerprints’ that are tightly associated with OS metastasis. The Auto-RS signature, composed of BNIP3, MYC, PEA15, and SAR1A, enables early therapeutic stratification of patients independent of overt metastatic status. Moreover, Auto-RS delineates key molecular underpinnings of OS metastasis at single-cell resolution. As a practical laboratory tool, Auto-RS may represent a step toward improved risk stratification, where advances in metastasis prediction and therapeutic guidance converge to improve outcomes in OS. Full article

The regeneration of the dentin-pulp complex remains a significant challenge in regenerative endodontics. While conventional therapeutic approaches are effective in eliminating infection and preserving dental structure, they fail to restore the biological functionality of the pulp tissue. In recent years, three-dimensional (3D) printing and biopolymer-based bioprinting have opened unprecedented opportunities in dental tissue engineering, enabling the fabrication of biomimetic scaffolds with precisely controlled structural and bioactive properties. This review synthesizes current advances in bioprinting technologies, the diversity of biomaterials and bioinks employed, and the various stem cell sources utilized in pulp regeneration. It further examines how the three-dimensional microenvironment modulates cell viability, odontogenic differentiation, and the promotion of angiogenesis and neurogenesis, emphasizing the role of scaffold composition, mechanical properties, and internal architecture in influencing regenerative outcomes. Additionally, persistent challenges are discussed, including the optimization of bioink formulations, the achievement of functional vascular integration, and long-term validation of regenerated tissues, underscoring the need for multidisciplinary strategies to facilitate clinical translation. By integrating recent evidence, this review establishes a conceptual framework for the development of personalized and predictable approaches to dentin-pulp complex reconstruction. Full article

Background and Objectives: Despite recent advancement, neovascular age-related macular degeneration (nAMD) remains a leading cause of irreversible vision loss. Undertreatment, fewer anti-VEGF injections and longer intervals than in clinical trials have been associated with sub-optimal visual outcomes. Visit-based regimens (Treat-and-Extend, PRN) may permit intervals of unrecognized retinal fluid between office visits. A home OCT system with near-daily self-imaging provides frequent structural retinal information between office visits that can support early detection of persistent or recurring fluid. The objective was to evaluate the duration and magnitude of fluid exposure between standard care visits and estimate the potential to shorten that exposure. Materials andMethods: Ad hoc analysis of three cohorts of treatment naïve and experienced nAMD eyes managed by standard care while participating in observational studies of the home OCT system, with treating physicians masked to home OCT data. AI-based analysis of fluid volume, rate of change and time of fluid onset was performed. Results: Data from 209 participants, mean age 76.4 years, 53% female, who performed 10,110 scans (6.0 scans/week) were analyzed. An amount of 119 eligible eyes provided data from 185 standard care intervals. Persistent or recurring fluid was identified in 121 (65%) intervals, on average 32 days prior to the next office visit. Of these, 84 (69%) had potential visit advancement within labeled minimal treatment intervals of 19 days. Mean fluid volume at the earliest possible notification was 26 nL and recurrence rate averaged 4.4 nL/day. Conclusions: A substantial proportion of patients experience unrecognized disease activity between visits. Home OCT monitoring provides adjunctive information to support early detection of fluid and may facilitate timely clinical evaluation. In this context, such monitoring may be considered reasonable and necessary to inform management of nAMD within established standards of care, while not replacing clinician-directed diagnosis or treatment decisions. Full article

Background/Objectives: Hip fractures are associated with high short-term mortality in older adults. This study aimed to determine 30-day mortality after hip fracture surgery and evaluate factors associated with short-term mortality, with particular attention to baseline comorbidity indices and in-hospital adverse events. Methods: This retrospective cohort study included 785 patients who underwent surgery for hip fracture at University Hospital of Split, Croatia, between January 2021 and December 2022. Clinical data were extracted from medical records. The primary outcome was 30-day mortality, including in-hospital and post-discharge deaths. Associations with mortality were examined using univariable and multivariable logistic regression. Baseline-only comorbidity models were constructed using the American Society of Anesthesiologists Physical Status Classification System (ASA PS Classification), the Charlson Comorbidity Index (CCI), and the Elixhauser Comorbidity Index (ECI). Exploratory hospital-course models additionally included in-hospital adverse events, which were interpreted as time-dependent hospital-course events rather than baseline predictors. Results: 30-day mortality was 11.0% (86/785). Older age, male sex, higher comorbidity burden, and in-hospital adverse events were associated with mortality. Mortality was 5.7% without documented adverse events, 24.2% with one adverse event, and 42.4% with two or more adverse events. Baseline-only comorbidity models showed acceptable and broadly comparable discrimination, with AUCs of 0.73–0.77. Exploratory hospital-course models showed higher discrimination, with AUCs of 0.80–0.82. Conclusions: 30-day mortality after hip fracture surgery was associated with baseline patient vulnerability and in-hospital adverse events. Baseline-only models based on the ASA PS Classification, the CCI, and the ECI provided broadly comparable short-term risk stratification. In-hospital adverse events should be viewed as markers of an adverse clinical trajectory, not evidence of causality. Full article