Abstract:

Background/Objective: The introduction of Ketoconazole (KZ, Nizoral^®) in 1977 by Janssen Pharmaceutica marked a significant milestone in medical mycology as the first broad-spectrum oral antifungal agent. However, KZ is a highly lipophilic compound, presenting significant challenges in the development of efficient topical formulations. Moreover, oral KZ has undergone labeling revisions and market withdrawal due to serious hepatic side effects. This study aimed to design, optimize, and evaluate KZ-loaded nanoemulsions (NEs; KZ-NEs) as a delivery platform that could improve skin bioavailability and antifungal activity. Methods: Optimized KZ-NEs were converted to a mucoadhesive formulation (KZ-NEC) by the addition of Carbopol^® 940 NF to enhance the adherence of the formulations to the skin surface. NEs were evaluated concerning physical appearance, globule size, polydispersity index, zeta potential, pH, viscosity, and drug content. Optimized KZ-NE and lead KZ-NEC formulations were further evaluated for in vitro release, ex vivo skin permeation and deposition, skin irritation, and in vivo studies. Results: In vitro release studies revealed that nanocarrier systems provided a sustained release of KZ over 24 hours. The ex vivo permeability coefficients of KZ from the optimized KZ-NE and lead KZ-NEC formulations were approximately 4 and 3-fold greater than that achieved with the marketed cream formulation, respectively. In addition, the C_max of the lead KZ-NEC formulation (14.4±1.1 μg/mL) was significantly higher (p<0.05) compared with the marketed cream formulation (10.5±0.5 μg/mL). Moreover, in vitro antifungal susceptibility testing showed that KZ demonstrated improved antifungal efficacy when incorporated into the NE and NEC formulations. Neither of the NE-based formulations caused any alterations in skin color or morphology during the 24-hour visual observation period. Both NE-based formulations were stable for 90 days (the last time-point tested) at three different storage conditions. Conclusions: NE-based formulation could serve as an effective topical delivery platform for KZ and could improve therapeutic outcomes for patients with topical fungal infections.

Abstract: Large language models (LLMs) are increasingly explored for clinical documentation support, yet the influence of prompting architecture on documentation quality in complex longitudinal contexts remains poorly characterized. This controlled retrospective methodological study evaluated three prompting strategies—Single Prompt (SP), Section-Based Prompt (SBP), and Section-Based Prompt with Writing Refinement (SBP+W)—for generating inpatient rehabilitation discharge reports using OpenAI large language model (GPT-5.2). Twenty anonymized rehabilitation cases involving prolonged hospital stays and multidimensional func-tional documentation were processed under standardized model conditions. AI-generated reports were compared with human-authored summaries. Two blinded board-certified rehabilitation physicians in-dependently evaluated outputs using a structured 4-point ordinal scale assessing structural integrity, clinical coherence, completeness, and readability. Inter-rater reliability was estimated with quadratic weighted Cohen’s kappa and bootstrap confidence intervals. Group differences were analyzed using non-parametric testing and exploratory multivariable modeling. All LLM prompting strategies achieved significantly higher expert-rated quality scores than hu-man-authored reports (p < 0.01). SBP demonstrated the highest median performance and strongest regression effect, although differences among LLM-based strategies were not statistically significant after correction. Prompting strategy explained more variability in expert ratings than case-level factors. Structured section-based prompting may represent a practical design lever for improving perceived quality in AI-assisted clinical documentation workflows. Keywords: artificial intelligence; clinical documentation; discharge reports; large language models; medical writing; prompt architecture; prompt engineering; rehabilitation medicine.

Abstract:

Quantitative preclinical imaging enables non-invasive characterization of physiological, molecular, and functional processes across a variety of experimental models, providing metrics that inform longitudinal studies and translational research. This review synthesizes current strategies for quantitative imaging across modalities including Positron emission tomography (PET), Single Photon Emission Computed Tomography (SPECT), Magnetic resonance imaging (MRI), Computed Tomography (CT), optical imaging, and hybrid systems. We examine methodological frameworks for parameter extraction, reproducibility, and validation against biological reference standards, evaluating each modality through a cross-cutting analytical framework that distinguishes technical, biological, and computational sources of quantitative variance and identifies the current metrological maturity of harmonization infrastructure across platforms. Key challenges, such as protocol harmonization, cross-platform comparability, and integration across species, are analyzed, alongside computational advances including parametric mapping, and artificial intelligence–assisted pipelines. Emerging approaches that combine multimodal acquisition with standardized reconstruction and calibration strategies are also discussed, emphasizing their potential to enhance precision, reduce bias, and support biologically meaningful interpretation. Collectively, this review provides a comprehensive perspective on the design, implementation, and validation of quantitative preclinical imaging studies, offering practical guidance for generating reproducible, interpretable, and translationally relevant imaging biomarkers. By integrating methodological insights with advances in technology and analytics, it underscores the role of quantitative frameworks in bridging preclinical discovery with translational applications.

Abstract: Diabetes mellitus (DM) is a rapidly growing global health burden and a major driver of infection-related morbidity and mortality. Chronic hyperglycemia disrupts both innate and adaptive immunity through impaired complement activity, dysfunctional dendritic cells and natural killer cells, altered macrophage polarization and efferocytosis, and neutrophil defects including reduced chemotaxis, impaired phagocytosis, and dysregulated NETosis. These immune abnormalities, compounded by endothelial dysfunction and microvascular disease, increase susceptibility to severe and recurrent infections such as urinary tract infections, tuberculosis, pneumonia, skin and soft tissue infections, and invasive fungal diseases. Emerging evidence also supports a bidirectional relationship in which infections may precipitate or aggravate DM via mechanisms including molecular mimicry, β-cell injury, chronic inflammation, and gut microbiota dysbiosis, contributing to insulin resistance and β-cell dysfunction. Recurrent infections and frequent exposure to broad-spectrum antibiotics, together with altered pharmacokinetics, chronic wounds with biofilm formation, and prolonged healthcare exposure, create strong selective pressure for antimicrobial resistance (AMR) in diabetic populations. Using clinical and scientific based evidence, this review explores mechanisms linking DM, infection risk, and AMR, highlighting implications for the diagnosis, therapy, stewardship, and vaccination, as well as outlines key research gaps including improved AMR surveillances stratified by diabetes status and integrated predictive models incorporating glycemic control, host factors, and microbial genomics.

Abstract: Methicillin-resistant Staphylococcus aureus (MRSA) remains a major driver of antimicrobial resistance due to expression of penicillin-binding protein 2a (PBP2a), a transpeptidase whose conformational regulation limits the efficacy of most β-lactam antibiotics. Structural studies have shown that PBP2a activity is modulated through a distal regulatory pocket that controls catalytic-site accessibility, yet exploitation of this mechanism for inhibitor design remains limited. The present study applied a pharmacophore-guided computer-aided drug design (CADD) strategy to identify β-lactam–independent scaffolds capable of engaging this regulatory region. A literature-guided workflow integrating similarity screening, pharmacophore modeling, scaffold hopping, and bioisosteric replacement was implemented. Ceftaroline was selected as a reference ligand based on clinical relevance and structural similarity analysis. Docking validation revealed limited interaction of ceftaroline with key regulatory residues within the PBP2a deep pocket, particularly Asp516, Tyr519, and Gln521, residues implicated in allosteric signal propagation and conformational control of the enzyme. Residue-level interaction analysis was therefore used to guide rational scaffold redesign. Three novel analogues were generated through scaffold hopping and targeted bioisosteric modification and evaluated using molecular docking with PyRx followed by interaction analysis in Discovery Studio. Among the designed compounds, Analogue 2 demonstrated the most favorable predicted binding affinity and interaction stability, establishing directional hydrogen bonding with Asp516 and Gln521 and improved interaction density within the regulatory pocket. These interactions were not observed for the β-lactam reference ligand. Pharmacophore validation confirmed alignment between similarity-derived candidates and the redesigned scaffolds, supporting the robustness of the computational design framework. Collectively, these findings demonstrate that rational scaffold redesign can overcome structural limitations associated with β-lactam antibiotics and identify chemically distinct scaffolds capable of engaging the PBP2a regulatory pocket. This study proposes a reproducible computational strategy for discovering non-β-lactam PBP2a modulators and highlights the role of CADD-driven medicinal chemistry in accelerating antimicrobial discovery.

Abstract: Background: Pediatric-onset neuromuscular diseases (NMDs) represent a clinically and genetically heterogeneous group of rare disorders, often posing significant diagnostic challenges due to overlapping phenotypes. Next-generation sequencing (NGS), particularly whole-exome sequencing (WES), has transformed the diagnostic landscape; however, its clinical utility varies across phenotypic subgroups. Methods: We conducted a combined retrospective–prospective cohort study including 100 pediatric patients with suspected neuromuscular disorders evaluated at a tertiary referral center between 2015 and 2025. Patients were stratified into seven clinically defined diagnostic categories prior to genetic testing. NGS-based diagnostics (primarily WES) were performed following initial clinical and targeted evaluations. Diagnostic yield and predictors of a positive genetic result were analyzed using univariate and multivariable statistical approaches. Results: A molecular diagnosis was established in 71% of patients, including 64% with pathogenic/likely pathogenic variants and 7% with clinically consistent variants of uncertain significance. Diagnostic yield varied significantly across disease categories (p < 0.001), reaching near-complete rates in well-defined phenotypes such as congenital myasthenic syndromes, neuropathies, and metabolic myopathies, while markedly lower yield was observed in unclassified cases (38.2%). Multivariable logistic regression identified disease group as the only independent predictor of diagnostic success (B = −0.436, p = 0.001). Frequently implicated genes included DMD, RYR1, and LAMA2, reflecting a predominance of structural and excitation–contraction coupling defects. Conclusions: NGS demonstrates high diagnostic utility in pediatric neuromuscular disorders, particularly when applied in a phenotype-driven framework. Diagnostic performance is strongly influenced by the degree of clinical definition prior to testing, highlighting the continued importance of expert phenotyping in the genomic era.

Abstract: Background: Central sensitization (CS) has been held responsible for both persistent pain and high disease activity score in Spondyloarthritis (SpA). Central Sensitization In-ventory (CSI) is a questionnaire used to determine CS frequency: a score of at least 40 is as-sociated with a high likelihood of CS. Objectives: To investigate the prevalence of CS in our cohort and its association with clinical characteristics of patients and their quality of life. Methods: Adult patients with a diagnosis of Psoriatic Arthritis (PsA) or Axial Spon-dyloarthritis (AxSpA) and also classifiable according to ClASsification criteria for Psoriatic Arthritis (CASPAR) and Assessment of SpondyloArthritis international Society (ASAS) criteria respectively, regularly followed at the SpA outpatients clinic of our Unit, were con-secutively enrolled from April to November 2023. Their epidemiologic, clinic and clinimet-ric data were collected, as well as patient reported outcome measures (PROMs) [CSI, Health Assessment Questionnaire (HAQ), FACIT-Fatigue (FACIT-F), SHORT-FORM 36 (SF-36), Hospital Anxiety and Depression Scale (HADS)]. Considering the definition of a “difficult to treat” rheumatoid arthritis, we defined as “multi-failure” those patients who were treated with more than 2 biologic disease modifying anti- rheumatic drugs (bDMARDs) with different mechanisms of action. Intergroups comparisons were assessed by using Chi-square, t-test and ANOVA. P values < 0.05 were considered signif-icant. Results: A total of 100 patients were enrolled, 46 male (46.0%) and 54 female (54.0%) with a mean age of 59,4±9.8 years and a mean disease duration of 14.8±10.1 years; 79 patients (79%) had a diagnosis of PsA and 21 (21%) of SA. Forty-two pa-tients (42.0%) had a CSI score ≽40. Significant correlations were found among CSI score ≽40 and female sex (p=0.004), the occurrence of enthesitis (p=0.05), DAPSA-CRP (p=0.02) and ASDAS scores (p=0.03), a multi-failure condition (p=0.01), fibromyalgia (FM) (p=0.004), thyroid disease (p=0.016) and obesity (p=0.047). Re-garding PROs, significant correlations were found between CSI and values of HADS (both anxiety and depression), FACIT-F, HAQ and all the domains of SF-36 (p value < 0.0001). Conclusions: Our data confirmed that more than 40% of SpA patients had CSI values ≥ 40 and underlined how CS could widely impair their disease burden. A routinary evaluation of CS and a multifactorial biopsychosocial perspective in the diagnosis and management of chronic pain in patients with SpA could help rheu-matologists in improving their quality of care.

Abstract: The dextran sulfate sodium (DSS) colitis model is the most widely used experimental model of inflammatory bowel disease (IBD) due to its simplicity and versatility, with over 7,000 PubMed entries in the last decade and an exponential rise in recent years. Since its initial description in 1985, DSS colitis has been extensively evaluated across species, most notably in mice and rats, and has yielded substantial insights into IBD pathogenesis. However, the model’s multifactorial nature poses a dual challenge: it offers an opportunity but complicates study design, interpretation, and translational relevance. This complexity is worsened by inconsistent reporting, which hampers reproducibility and comparability across studies. The broad use of the DSS-induced colitis model yields numerous insights about the model, which help better understand its complexity, characteristics and limitations. Although DSS colitis is induced locally, inflammation in the colon and the gut barrier destruction may also affect other organs (such as the liver and brain) and their metabolism and molecular responses, which, in turn, influence colitis development, drug response, and the interpretation of results. These intrinsic (intra-experimental) characteristics of the DSS colitis are summarised in the paper (colitis, gut-brain axis, gut-liver axis). In addition, the DSS model is heavily influenced by numerous ex-trinsic (inter-experimental) factors (environmental, microbiological, genetic), which may further complicate the colitis model, the study outcomes, and data interpretation and are also discussed in the paper. As science advances and new data accumulate, understanding the intricate interplay among internal mechanisms, external factors, and technical variables becomes increasingly essential for accurate interpretation of DSS outcomes. This review synthesizes the complexity and interdependence of factors shaping the DSS model, emphasizing the need for meticulous reporting and consideration of methodological nuances to enhance reproducibility, interpretation, and translational value in DSS colitis research. In addition, the review provides practical guidance through a “traps & tricks” subsections and a checklist table designed to provide a framework and practical recommendations to better understand, apply, and interpret DSS model results in the context of broader systemic and methodological considerations.

Abstract: Background: Human cadaveric models provide a controlled experimental setting to investigate the anatomical basis and mechanical behavior underlying musculoskeletal ultrasound findings. In recent years, both B-mode ultrasound and shear wave elastography have been applied in cadaveric studies to explore muscle architecture, aponeurotic structures, and passive mechanical properties under standardized conditions [3]. Objective: The aim of this scoping review was to map and synthesize cadaveric studies using ultrasound and shear wave elastography to investigate lower-limb muscles and their aponeurotic structures, with emphasis on methodological applications, anatomical insights, and limitations relevant to clinical interpretation. Material and Methods: A scoping review was conducted according to PRISMA-ScR principles. Studies included if ultrasound imaging (B-mode and/or shear wave elastography) was applied directly to human cadaveric lower-limb muscles or aponeurotic structures. Data were extracted and synthesized descriptively by anatomical region and ultrasound technique [8]. Results: A total of 11 studies met the inclusion criteria and were included in the final qualitative synthesis, all of which applied ultrasound imaging, with or without shear wave elastography, directly to human cadaveric muscle tissue (Table 1). Among these, seven studies specifically investigated lower-limb skeletal muscles and their aponeurotic structures using ultrasound-based techniques to describe muscle architecture, internal connective tissue anatomy, or passive mechanical behavior [5]. These studies focused on the quadriceps femoris, hamstrings, adductor longus, and the gastrocnemius–soleus complex [1]. The remaining four studies were considered relevant and therefore included in the scoping review because, although they did not focus on a specific lower-limb muscle group, they addressed key methodological factors influencing ultrasound and elastography derived measurements in cadaveric muscle tissue [2,4]. These investigations examined the effects of tissue layering, specimen-related characteristics, and measurement conditions, thereby providing essential methodological context for the interpretation of ultrasound-based outcomes across different anatomical regions. Conclusion: Cadaveric ultrasound studies provide essential anatomical context for interpreting musculoskeletal ultrasound, while cadaveric shear wave elastography supports controlled exploration of passive muscle mechanics. At the same time, these studies highlight important methodological sensitivities that should be acknowledged before translating elastography findings to clinical decision-making [2].

Abstract: Background/Objectives: Obesity is a chronic, relapsing disease with a widening gap between clinical need and the availability of specialist care. Artificial intelligence (AI) may enable earlier risk detection, more precise phenotyping, and scalable behavioural support across obesity treatment pathways. This narrative review synthesises con-temporary AI applications across the obesity care continuum and evaluates their translational readiness. Methods: A targeted search of PubMed/MEDLINE and Google Scholar (January 2024–January 2026) was conducted, complemented by citation chaining. Evidence was syn-thesised across four domains: (1) risk prediction and screening, (2) environmental and behavioural determinants, (3) multimodal phenotyping and precision stratification, and (4) AI-enabled lifestyle interventions and behavioural coaching (AIBC). Results: EHR-based models demonstrate clinically useful discrimination for early risk identification. Multimodal approaches refine stratification beyond BMI-centric classi-fication. AIBC platforms show emerging evidence of clinically meaningful weight loss, including non-inferiority to human coaching, but long-term effectiveness, generalisa-bility, and equity remain insufficiently established. Conclusions: AI is positioned to become a core enabler of personalised obesity path-ways. Safe translation requires external validation, bias auditing, transparent report-ing, human oversight, and post-deployment surveillance aligned with clinical guide-lines and regulatory expectations.

Abstract: Hypertension is an important target for primordial prevention of complex, noncommunicable diseases and its prevalence remains high across populations. Urban population in India is at a high risk of hypertension but the genetic basis of hypertension in this population remains poorly understood. We conducted a pooled whole-blood genome-wide association study of 28 pools representing 1,402 participants of the Diabetes In Sindhi Families In Nagpur (DISFIN) study which enrolled families of probands with type 2 diabetes (T2D). Genotyping was done using Illumina’s Global Screening Array. From a total of 608,550 single nucleotide variants, 191 were found to be significantly associated with hypertension even after adjusting for metabolic comorbidities, batch effects, pooling error, kinship status and pooling variation. These variants mapped to 180 well-characterized genes that comprised 55 (31%) genes encoding long noncoding RNA (lncRNA). Many of the genes significantly associated with hypertension (including 35% of the lncRNAs) have also been reported by other studies. However, we identified novel genes (SBF2, ARHGAP12, EPAS1, CLEC16A and LRPPRC) to be associated with hypertension. The most significantly associated lncRNA gene was FLYWCH-AS1. Bioinformatic analyses indicated that these novel genes are likely to have functional importance in hypertension. Our study thus points to the potential candidate genes associated with hypertension in endogamous Sindhi families with T2D patients. The replicable and functional role of these candidate genes should be investigated in future studies.

Abstract:

Background: Preliminary reports have suggested a batch-dependent safety signal for COVID-19 vaccines. It is important to establish if these findings can be replicated. Methods: We used publicly available nationwide data from Germany spanning the first 3.5 years of the vaccination campaign to calculate weekly rates of spontaneously reported suspected adverse events (SAEs) per 1 000 administered vaccine doses. Results: SAE rates ranged between 2.2 and 22.8 per 1 000 doses and women accounted for 72% of all SAEs. Crucially, SAE rates for Comirnaty (Pfizer-BioNTech), Spikevax (Moderna), and Vaxzevria (AstraZeneca) were very high in the initial phase of vaccination rollout and hereafter declined precipitously. For example, SAE rates in weeks 1-4 of 2021 were 8.2, 50.8, and 620.9 per 1 000 doses of Comirnaty, Spikevax, and Vaxzevria, respectively, but fell to 4.4, 11.6, and 7.4 per 1 000 doses in weeks 12-16 of 2021. Conclusions: SAE rates in Germany were highly elevated in the initial phase of COVID-19 vaccination rollout and then fell precipitously, a pattern compatible with a batch-dependent safety signal. Furthermore, there was a considerable overrepresentation of women with SAEs. These preliminary results call for more definitive studies of batch-dependent COVID-19 vaccine safety.

Abstract: The block-and-lock strategy aims to achieve a functional cure for human immunodeficiency virus type 1 (HIV-1) infection by enforcing durable, drug-independent silencing of proviral transcription. Several latency-promoting agents have been described that effectively limit viral reactivation in vitro or in animal models. However, most approaches induce only partial or reversible transcriptional repression and have not yet been translated into safe and effective clinical interventions. This review summarizes the molecular mechanisms underlying block-and-lock strategies and critically evaluates the limitations of current candidate compounds. We highlight recent advances in understanding HIV-1 integration site selection, focusing on the roles of lens epithelium-derived growth factor p75 (LEDGF/p75) and cleavage and polyadenylation specificity factor subunit 6 (CPSF6) in directing proviral integration toward gene-dense, transcriptionally active chromatin. Pharmacological disruption of the LEDGF/p75–integrase interaction by LEDGF/p75 inhibitors (LEDGINs) redirects proviral integration toward less transcriptionally active genomic regions that are more resistant to reactivation. Recent tandem knockout studies, however, demonstrate that CPSF6 plays a dominant role in guiding HIV-1 integration toward gene-dense, transcriptionally active chromatin. LEDGIN treatment has been linked to the preferential targeting of proviruses to heterochromatin-rich regions within the nuclear interior. By contrast, CPSF6 knockout redirects integration toward peripheral heterochromatin, especially lamina-associated domains (LADs), genomic regions typically exhibiting stronger and more stable transcriptional repression than interior heterochromatin. These findings suggest that therapeutic modulation of CPSF6 may exert a more profound and durable effect on proviral silencing within a block-and-lock framework. Nevertheless, complete CPSF6 ablation is associated with severe cellular toxicity. The challenges associated with CPSF6-related adverse effects and potential strategies to overcome these limitations are discussed.

Abstract: Background: Response to neoadjuvant therapy in locally advanced rectal cancer (LARC) is heterogeneous and early identification of non-responders may help optimize treatment strategies and reduce unnecessary toxicity. This study aimed to develop and internally validate a machine learning model based on radiomic features extracted from baseline magnetic resonance imaging (MRI) to predict treatment response assessed at restaging MRI. Methods: In this retrospective single-center study 86 patients with histologically confirmed LARC who underwent baseline and restaging MRI, neoadjuvant therapy, and surgery, were included. Primary tumors were manually segmented on oblique axial T2-weighted images. A total of 107 radiomic features were extracted using PyRadiomics, with and without N4 bias field correction. Feature selection was performed using LASSO, followed by elasticnet–regularized logistic regression. Model performance was assessed using repeated stratified 5-fold cross-validation. Response was defined according to MRI tumor regression grade (mrTRG) at restaging, dichotomized into responders (mrTRG ≤ 2) and non-responders (mrTRG ≥ 3). Results: The model achieved a mean area under the receiver operating characteristic curve (AUC-ROC) of 0.73, accuracy of 72.5%, sensitivity of 79.2%, and specificity of 50%. Conclusions: Baseline MRI-based radiomics demonstrated to potentially identify patients at higher risk of non-response to neoadjuvant therapy in LARC.

Abstract: Bertolotti's Syndrome is a congenital condition characterized by chronic low back pain stemming from a lumbosacral transitional vertebra (LSTV). While LSTV anatomy is present in approximately 20% of the population, the syndrome is clinically defined only when the LSTV is confirmed as the pain generator. This review synthesizes the latest understanding of the condition, ranging from its genetic origins in HOX gene patterning defects to modern clinical management. We examine the biomechanical alterations caused by LSTV and discuss the advantages of Jenkins classification over Castellvi classification. The diagnostic protocol is presented as a multi-step process, emphasizing the necessity of fluoroscopy-guided diagnostic injections to distinguish incidental findings from true pathology. Finally, we present the consensus treatment protocol based on latest findings and identify critical gaps in the literature regarding standardized care protocols and long-term surgical outcomes.

Abstract: Objective: To characterize real-world distributions of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) across major rheumatic diagnoses, and to quantify concordance/discordance patterns and combined CRP-ESR inflammatory phenotypes. Methods: We retrospectively extracted all CRP and ESR tests performed in a tertiary university rheumatology hospital (January 2018-December 2023), including ICD-10-coded diagnoses. Analyses were conducted at measurement level and patient level (medians across repeated tests). CRP and ESR were expressed as raw values and multiples of ULN, and categorized into severity strata. CRP and ESR datasets were merged by patient identifier and calendar date to define same-day pairs; paired analyses used Spearman correlations and ULN-based phenotype classes. Sensitivity analyses tested alternative pairing windows, first-pair-only analyses, phenotype persistence rules, and tertile/quartile discordance definitions. Results: Among 16921 patients with ≥1 CRP and 17126 with ≥1 ESR, CRP was more disease-discriminative and only negligibly age-related, whereas ESR increased modestly with age and showed marked sex shifts across severity categories. Inflammatory burden was highest in gout and rheumatoid arthritis, intermediate in psoriatic arthritis and ankylosing spondylitis, and lower in connective tissue diseases and osteoarthritis; CRP distributions were more strongly right-tailed than ESR. Merging yielded 44427 same-day CRP-ESR pairs from 16824 patients (99.1% match). CRP and ESR were moderately correlated at measurement and patient levels, yet discordance was common: 27.3% of pairs showed isolated elevation of a single marker. Conclusions: In routine rheumatology care, CRP and ESR provide complementary information. Clinically relevant CRP-ESR dissociation is frequent, persists at the patient level, and follows diagnosis-dependent phenotype patterns.

Abstract: Background: Care for knee osteoarthritis (KOA) is frequently fragmented, and pathway-level decisions within Physical Medicine and Rehabilitation (PM&R) are influenced by local organizations. We sought expert consensus on an ideal, function-oriented KOA care itinerary deliverable in PM&R services. Methods: A two-round Real-Time Delphi study was conducted using the SmartDelphi web platform. A steering committee of five PM&R physicians developed a 37-item questionnaire covering referral/access, functional and outcome assessment, conservative management, escalation/referral thresholds, and follow-up/discharge. Round 1 was online (SERMEF osteoarthritis working group; 46 invited, 40 completed; 87.0%) with responses collected until 30 April 2025. Round 2 was an in-person, facilitated validation round on 30 May 2025 at the SERMEF Congress (A Coruña; 85 invited, 70 completed; 82.4%). Items were rated on a 6-point Likert scale; consensus strength was defined by interquartile range (IQR): strong (0–1) vs weak (≥2). No patient-level data were collected; participant characteristics were comparable across rounds, suggesting consensus refinement reflected deliberation rather than panel shifts over time. Results: Consensus supported a longitudinal, function-first pathway structured into five phases: entry/referral to PM&R; comprehensive functional assessment using a minimum outcomes dataset (pain VAS/NRS, WOMAC function, quality-of-life scale); multimodal conservative rehabilitation combining exercise/physiotherapy, education/self-management support, and indicated oral/topical therapies; reassessment-guided escalation in non-responders, reserving interventional PM&R techniques, multidisciplinary musculoskeletal pain-unit management, or orthopaedic evaluation for persistent pain and/or functional limitation; and longitudinal monitoring with defined discharge criteria. Conclusions: SERMEF PM&R experts converged on an implementation-oriented, outcomes-driven KOA itinerary centred on functioning, conservative multimodal care, structured reassessment, and explicit discharge planning.

Abstract: Background/Objectives: When a patient dies and a complex pathology is identified, it can be difficult to ascertain the cause of death and the contribution of each ailment to the dying process. Patients with traumatic brain injuries often represent the best hosts for healthcare-associated infections, especially pneumonia or bronchopneumonia. Additionally, some of these ones also have pre-existing chronic diseases, which can influence their medical evolution. Therefore, many factors need to be carefully analyzed by the forensic doctor when establishing the cause of death. The purpose of this study is to demonstrate the crucial role a forensic autopsy plays in accurately identifying the cause of death and diagnosing all of a patient's ailments, particularly in cases involving nosocomial infections, while emphasizing the significant number of cases where healthcare-associated infections can lead to mortality. Methods: We conducted a retrospective analysis that included cases with cranial-cerebral traumas, pre-existing chronic diseases, and hospital-acquired infections that were autopsied at the Forensic Medicine Institute in Timișoara between January 1, 2018, and December 31, 2021. We studied the autopsy reports and the medical documentation of the patients. Results: Our research indicates that patients with traumatic brain injuries and non-traumatic conditions face a considerable risk of acquiring healthcare-associated infections, especially pneumonia or bronchopneumonia, even with adequate medical treatment. A significant part of these infectious issues remains unrecognized while hospitalization; however, autopsy can elucidate them. Conclusions: This study demonstrates a high prevalence of healthcare-associated infections in patients with brain injuries, as well as how these infections can alter the progression of traumatic disorders and their mortality rate.

Abstract: Chronic wounds represent a significant global healthcare challenge, affecting millions of patients and imposing substantial economic burdens on healthcare systems. Traditional wound management approaches often fail to address the complex pathophysiology underlying chronic wounds, including persistent inflammation, impaired angiogenesis, and disrupted extracellular matrix remodeling. Three-dimensional (3D) bioprinting has emerged as a transformative technology that enables the fabrication of patient-specific, biomimetic tissue constructs capable of addressing these intricate challenges. This comprehensive review synthesizes recent advances in 3D bioprinting for chronic wound treatment, examining bioprinting technologies, biomaterial innovations, mechanisms of wound healing, and clinical applications. Recent studies demonstrate that bioprinted constructs incorporating living cells, growth factors, and bioactive molecules can significantly accelerate wound closure, enhance vascularization, and restore functional skin architecture. Notable innovations include in situ bioprinting systems, photosynthetic scaffolds for oxygen delivery, and immunomodulatory bioinks. While significant technical challenges remain—including vascularization, scalability, and regulatory approval—the integration of advanced bioprinting techniques with regenerative medicine principles offers unprecedented opportunities for personalized chronic wound care and improved patient outcomes.

Abstract: Background: The long-term cardiovascular safety of high-dose intravenous mesenchymal stem cell (MSC) therapy remains insufficiently characterized in real-world clinical settings. Methods: We conducted a single-center retrospective observational study of patients who received high-dose intravenous MSC therapy. Cardiovascular events were identified through follow-up records. Observed event incidence was compared descriptively with age-adjusted population reference data. Statistical analyses were performed using two-sided Poisson methods. Results: Among treated patients, a total of four cardiovascular events were recorded during follow-up. The observed incidence did not demonstrate an excess signal compared with reference population data. No clustering of events was observed in the early post-infusion period. Sensitivity analyses yielded consistent findings. Conclusions: In this real-world cohort, high-dose intravenous MSC therapy was not associated with an apparent increase in cardiovascular event incidence. Given the observational design and limited event number, larger prospective studies are warranted to further characterize long-term cardiovascular safety.