Medicine and Pharmacology

Abstract: Background/Objectives: The objective of the present study was to determine the association between blood cadmium (Cd) and lead (Pb) levels and survival of the patients with kidney cancer. In this prospective study, we analyzed 272 consecutive, unselected kidney cancer patients and assessed their 8-year survival in relation to Cd and Pb levels. Methods: Cd and Pb concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). Patients were categorized into four groups according to the quartile distribution of Cd and Pb levels, ranked in ascending order. Multivariable models were adjusted for covariates including age at diagnosis, sex, smoking status, type of surgery, histopathological classification and blood levels of selenium, zinc, copper, iodine, cadmium and lead. Results: We observed no association between blood Cd and Pb levels and all-cause mortality in patients with kidney cancer. Conclusions: To our knowledge, this study is the first to investigate the relationship between blood levels of cadmium and lead and kidney cancer survival.

Abstract: Background: Although Diagnostic Reference Levels (DRLs) based on anatomical regions are widely used in Computed Tomography (CT) imaging, a clinical-indication-based approach provides a more accurate representation of daily practice and protocol variation. This study aimed to establish typical radiation doses for common CT clinical indications among adult patients at King Abdulaziz University Hospital (KAUH) in Saudi Arabia. Methods: This retrospective cross-sectional study included 298 adult patients who underwent CT examinations between 2020 and 2025 using two dual-source scanners operating in single- and dual-source modes. Demographic data, acquisition parameters, and radiation dose metrics, including volume CT dose index (CTDIvol) and the dose–length product (DLP) were extracted from scanner consoles. Six clinical indications were analyzed: brain trauma, sinusitis, chest metastases (chest Mets), interstitial lung disease (ILD), abdominopelvic metastases (AbdPel Mets), and hernia. Results: Typical median CTDIvol values in mGy were 36.4 for brain trauma, 3.4 for sinusitis, 4.9 for chest Mets, 5.6 for ILD, 7.2 for AbdPel Mets and hernia. Corresponding DLP values in mGy·cm were 654, 50, 173, 188, 344, and 369, respectively. Brain trauma demonstrated the highest radiation exposure, whereas sinusitis CT showed the lowest. Most values were comparable to or lower than international DRLs. Conclusions: This study provides the first comprehensive clinical-indication-based DRL data in Saudi Arabia beyond anatomical benchmarks, supporting ongoing dose optimization and future national DRL development.

Abstract: The Universal Declaration on the Human Genome and Human Rights gave genomics an enduring human-rights grammar built around dignity, equality, privacy, and the symbolic idea that the human genome is the heritage of humanity [1]. That grammar remains indispensable, but it is no longer sufficient. Contemporary genomic practices are not confined to laboratory science or bedside counseling: they unfold within data-intensive, computational, and commercially mediated infrastructures that classify persons, govern access to care, and redistribute risk across families, communities, and generations. This article asks a sharper question than the usual privacy-versus-innovation framing: what is the normative object of genomic rights under conditions of predictive biology? The article argues that genomic rights should be interpreted not merely as personality rights protecting individuals from misuse, but as governance rights aimed at shaping how genomic prediction, circulation, ownership, and benefit-sharing are organized. The argument proceeds in four steps. First, it reconstructs the normative architecture of the UNESCO framework and its connections with broader human-rights law, including privacy, equality, and the right to enjoy the benefits of scientific progress [1-6]. Second, it shows why mainstream approaches centered on consent, confidentiality, and anti-discrimination are necessary but analytically insufficient in the face of algorithmic profiling, cross-sector data drift, and unequal access to genomic benefit [7-10]. Third, it proposes four analytic concepts—algorithmic genomic biopower, conditional genomic sovereignty, anticipatory dignity, and multilevel genomic justice—as a vocabulary for contemporary governance. Fourth, it tests that framework against six boundary cases that reveal where conventional bioethics becomes descriptively weak or normatively thin [11-24]. The article concludes that the most important contemporary question is no longer whether genomics can be reconciled with human rights in principle, but who governs predictive biological futures, through which institutions, and for whose benefit. A rights-based response adequate to that problem must move from downstream protection toward upstream governance, from exclusively individual consent toward relational and collective accountability, and from formal access to innovation toward justice in the distribution of genomic risk and benefit.

Abstract: Objectives: Sarcopenia impairs physical function and increases healthcare burden among older adults with chronic musculoskeletal disorders. This study estimated the prevalence of sarcopenia and explored factors associated with sarcopenia among Vietnamese elderly outpatients. Methods: A hospital-based cross-sectional study was conducted among 88 outpatients aged 60 years or older with knee osteoarthritis and/or chronic spinal pain at a tertiary hospital in Northern Vietnam from May 2024 to October 2025. Sarcopenia and severe sarcopenia were defined according to the Asian Working Group for Sarcopenia 2019 criteria. Muscle mass, muscle strength, and physical performance were assessed using bioelectrical impedance analysis, handgrip dynamometry, and usual gait speed, respectively. Multivariable logistic regression and an exploratory chi-square automatic interaction detection decision tree were applied. Results: The prevalence of sarcopenia was 40.9%, including 23.9% with sarcopenia and 17.0% with severe sarcopenia. Age >70 years (adjusted odds ratio [AOR] 9.00, 95% confidence interval [CI] 2.40-33.60), history of falls (AOR 6.33, 95% CI 2.77-14.45), low educational attainment (AOR 2.86, 95% CI 1.46-5.61), and poor sleep quality (AOR 1.16, 95% CI 1.02-1.32) were independently associated with sarcopenia. Conclusions: Sarcopenia was common in this outpatient population. Routine case-finding may be particularly relevant in older patients with falls, lower educational attainment, and poor sleep quality. The decision-tree findings should be interpreted as exploratory because of the cross-sectional, single-center design and modest sample size.

Abstract: Companion dogs are increasingly recognized as translational models for studying human physiology and disease. Unlike conventional or genetically engineered laboratory models, dogs are outbred, immunocompetent animals that spontaneously develop complex diseases whose pathogenesis and environmental exposures commonly overlap with those of humans. These distinctive features create opportunities to study mechanisms of disease, progression, and therapeutic responses under conditions that more closely resemble clinical reality. This review highlights evidence for the translational relevance of canine models across multiple therapeutic areas. We further discuss how advances in genomics, transcriptomics, spatial biology, in vitro, and in silico model systems are expanding the translational utility of canine models for applications in human medicine. Although important species differences must be carefully weighed, dogs represent a uniquely valuable comparative model for elucidating disease mechanisms, informing drug development, and accelerating the translation of scientific discoveries to human medicine.

Abstract: Artificial intelligence (AI) is rapidly transforming healthcare, yet its integration into medical education remains inconsistent and lacks standardization across training levels. This article synthesizes current research on AI applications within medical education, highlighting significant gaps between the growing clinical role of AI and its representation in undergraduate and postgraduate curricula. The article identifies disparities in AI awareness and utilization, with postgraduate trainees demonstrating greater familiarity than undergraduates, and notes that most existing educational efforts are concentrated in specialty training and continuing education, particularly in fields such as radiology, pathology, surgery, cardiology, and dentistry. While medical trainees generally express positive attitudes toward acquiring AI competencies, barriers such as the absence of standardized frameworks and AI taxonomy, limited faculty expertise, curricular constraints, and ethical considerations impede broader adoption. Drawing on examples of pioneering programs and a systematic analysis of curricular approaches, we propose a novel, tiered framework for comprehensive AI integration across the medical education continuum. This framework emphasizes universal AI literacy, critical evaluation skills, ethical awareness, and experiential learning at the undergraduate level, with extensions for specialty-specific training and advanced technical or leadership tracks. Recommendations include phased implementation strategies, faculty development initiatives, including “teach the teacher”, and competency-based assessment methods. The article concludes that adequate preparation of future physicians requires a shift from isolated AI initiatives to coordinated, longitudinal integration efforts supported by collaboration among educational institutions, professional societies, and technology experts. Future research should focus on evaluating educational outcomes, developing robust assessment tools for AI competencies, and examining the long-term clinical impact of AI training.

Abstract: Objective: To compare rehabilitation outcomes in nonagenarian stroke patients (≥90 years) with those of younger elderly patients (65–89 years). Design: Retrospective parallel-group study. Setting: Geriatric rehabilitation department in an urban academic hospital. Participants: Medical records of 906 consecutive elderly patients admitted for post-stroke neurorehabilitation between 2015 and 2019 were reviewed. After exclusions, 876 patients were included. Main Outcome Measure: Discharge destination (home vs. long-term care facility). Results: Of the 876 patients, 803 were aged 65–89 years and 73 were aged ≥90 years. Median admission FIM scores were 54 and 46, respectively. Median FIM improvement (ΔFIM) was 16 in the younger group and 11 in the ≥90 group (p< 0.01). Median length of stay was similar (41 vs. 40 days; p=0.93). Discharge home rates were comparable (78.2% vs. 76.7%; p=0.776). Conclusions: Nonagenarian stroke patients benefit meaningfully from inpatient rehabilitation, achieving substantial functional gains and community discharge rates comparable to younger elderly patients.

Abstract: N-Methyl-D-aspartate (NMDA) receptors are ligand- and voltage-gated ion channels essential for synaptic plasticity, learning, and memory. The GluN2B subunit, highly expressed in the forebrain and spinal cord, is implicated in multiple neurological and psychiatric disorders, making it an attractive target for positron emission tomography (PET) imaging. However, the development of selective GluN2B PET radioligands remains challenging. Here, we describe the design, synthesis, and evaluation of eighteen 3-alkylaryl derivatives of 7-methoxy-2,3,4,5-tetrahydro-1H-benzo[d]azepin-1-ol, including enantiomerically resolved compounds, as candidate PET radioligands. Structure–activity relationship studies show that binding affinity is largely insensitive to electronic and steric variation at the terminal aryl group but strongly dependent on alkyl linker length, with a four-carbon chain providing optimal affinity. Binding affinity does not correlate with calculated lipophilicity, suggesting hydrophobicity is not the primary determinant of receptor interaction. Absolute configuration was established using vibrational circular dichroism and infrared spectroscopy, and docking studies provided insight into enantiomer-specific binding modes. Two ligands, L3 and L6, and their enantiomers exhibited high GluN2B affinity, favorable physicochemical properties, and suitability for carbon-11 labeling. PET imaging confirmed strong and specific brain binding of the radiolabeled compounds. These findings establish this scaffold as a promising platform for GluN2B PET ligand development.

Abstract: Tuberculosis remains a major cause of mortality globally. Treatment of tuberculosis requires a long duration with multiple drug regimen. Unfortunately, tuberculosis drug resistance is emerging, resulting in a treatment failure rate of 14% in new cases. Bedaquiline, a poorly soluble second-line drug is used to treat multidrug-resistant tuberculosis in combination with first-line anti-tuberculosis drugs. Bedaquiline is often ad-ministered with rifampicin, as this combination has demonstrated additive intracellular bactericidal and faster onset of action compared to bedaquiline monotherapy. However, co-administration with rifampicin has been reported to increase bedaquiline clearance, reducing concentration of bedaquiline in the blood by up to 25%. There is a need for alternative pharmaceutical formulations to enhance bedaquiline bioavailability and treatment success of tuberculosis. Co-amorphous drug delivery systems have the potential to improve the water solubility and bioavailability of poorly soluble drugs. In an aim to enhance the solubility of bedaquiline when co-administered with rifampicin, we have prepared co-amorphous systems of rifampicin and bedaquiline fumarate. First, miscibility of the components was assessed using Hansen solubility parameters. Then, the solid co-amorphous drug was prepared by fast solvent evaporation, and characterized using PXRD, TGA-DSC, FTIR, dissolution rate, and accelerated stability study. Results show that the co-amorphous form exhibited better dissolution for bedaquiline without compromising rifampicin dissolution. Furthermore, the co-amorphous product remained stable under stress conditions for 30 days. These findings suggest that co-amorphous systems of rifampicin and bedaquiline fumarate may represent a viable strategy to improve treatment outcomes for patients with multidrug-resistant tuberculosis treated with these drugs and decrease pill burden.

Abstract: Background/Objectives: Artificial intelligence (AI) and its subfield deep learning (DL) have rapidly evolved into a central tool in modern medicine. The purpose of this work was to examine if DL neural networks can discriminate efficiently the microvessel network of post-COVID-19 patients from healthy individuals from. Methods: A non-contact, digital slit-lamp video capillaroscopy system was used to record high magnification images form the bulbar conjunctival microcirculation of 12 COVID-19 survivors (named “COVID-19 Group”) and 12 healthy volunteers (named “Control Group”). Four pretrained convolutional neural networks (CNNs) were fine-tuned by transfer learning and their performance was assessed by standard binary classification evaluation criteria. Results: A scene-centric CNN named GoogLeNet-Places365 excelled on all evaluation criteria with an average testing accuracy, sensitivity, specificity and AUC (area under the curve) of 92%, 92%, 91%, and 0.971, respectively. Conclusions: Post-COVID effects on the eye microcirculation can be detected by deep CNNs, and there is now evidence for the first time, that AI could provide a risk-free, painless, contactless, fast, and accurate detection method of viral effects that does not depend on the optical clarity of the eye.

Abstract: Antimicrobial resistance poses a critical global health challenge, necessitating the accelerated discovery of novel antibacterial agents. This study presents a quantitative structure–activity relationship (QSAR)-based multiclass classification framework for predicting the antimicrobial activity of β-lactam, azetidinone, and thiazolidinone derivatives. A chemically diverse library of over 220 compounds was constructed through combinatorial scaffold expansion guided by structure–activity relationship principles, with activity classified as Inactive, Moderate, or Active based on minimum inhibitory concentration (MIC) values. Molecular features were encoded using a fused descriptor set comprising Morgan fingerprints (radius 2 and 3), MACCS structural keys, and eight physicochemical descriptors, yielding a 1,199-dimensional feature vector. Class imbalance was addressed via SMOTE applied exclusively to the training set. Multiple machine learning models were developed and compared, including Random Forest, Gradient Boosting, XGBoost, a stacking ensemble, and a deep neural network with residual connections, batch normalization, and dropout regularization. Hyperparameter optimization was performed using randomized search with stratified 5-fold cross-validation. Model performance was evaluated using accuracy, weighted F1-score, balanced accuracy, and multiclass ROC-AUC. Visualization strategies including t-SNE, PCA, and feature importance analysis confirmed meaningful chemical space organization and robust structure–activity discrimination across all classifiers.

Abstract: Background/Objectives: Vaccination is a critical public health intervention, yet its global implementation is hindered by high production costs and cold-chain requirements. This review aims to evaluate plant-based systems as sustainable, cost-efficient alternatives for vaccine production. Methods: A comprehensive literature search was conducted across major databases (PubMed, Scopus, Web of Science). The peer-reviewed references were critically assessed, focusing on molecular expression strategies, phytochemical immunomodulators, and plant-mediated oral delivery. Results: Plant and microalgae systems effectively support nuclear, chloroplast, and transient expression of diverse antigens. Furthermore, specific plant-derived compounds were found to act as potent adjuvants and immunostimulants, enhancing the immunogenicity of vaccine formulations. Edible plant tissues also provide a viable platform for oral delivery, reducing the need for extensive purification and refrigerated logistics. Conclusions: Integrating recombinant expression technologies with bioactive plant metabolites offers a flexible and scalable foundation for next-generation vaccines. These biological platforms are uniquely positioned to address global immunization challenges, particularly in low-resource settings.

Abstract: Background: Differentiating adrenal adenomas from non-adenomatous lesions remains a critical challenge in the management of adrenal incidentalomas. Conventional unenhanced CT relies on attenuation thresholds of 10 HU and 20 HU, which present trade-offs between sensitivity and specificity. Objectives: To evaluate the diagnostic performance of unenhanced Spectral CT using the attenuation difference between 40 keV and 140 keV virtual monoenergetic images for differentiating adrenal adenomas from non-adenomatous lesions. Methods: In this retrospective single-center study, 60 patients with adrenal lesions who underwent unenhanced dual-energy CT were included. Mean attenuation values were measured on conventional images and on virtual monoenergetic images at 40 keV and 140 keV. The spectral attenuation difference (Δ40–140 keV) was calculated. ROC analysis was performed to determine optimal thresholds and diagnostic performance. Results: Forty-nine lesions were adenomas and eleven were non-adenomatous. The optimal threshold for Δ40–140 keV was −17 HU. Diagnostic performance was as follows: HU ≤10 (AUC 0.816, diagnostic accuracy 0.70), HU ≤20 (AUC 0.883, diagnostic accuracy 0.87), and Δ40–140 keV ≤ −17 HU (AUC 0.940, diagnostic accuracy 0.90). The spectral attenuation difference demonstrated the highest overall diagnostic accuracy. Conclusions: Unenhanced Spectral CT using Δ40–140 keV improves discrimination between adrenal adenomas and non-adenomatous lesions compared with conventional attenuation thresholds. This technique may reduce indeterminate findings and limit the need for additional imaging.

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.