Search Results (8,509)

Despite advances in precision medicine, therapeutic options for pancreatic adenocarcinomas are limited, alongside a significant increase in incidence and mortality in recent years. We present the case of an exceptional response to immunotherapy in a heavily pre-treated pancreatic adenocarcinoma. The patient is a 73-year-old man that was diagnosed in 2017 with locally advanced pancreatic adenocarcinoma. He underwent different lines of chemotherapy and after exhausting standard treatment options, he practiced the FoundationOne^® CDx analysis (Foundation Medicine, Inc., Cambridge, MA, USA), that pointed out a High Tumor mutational burden that permitted our Oncology Center to request Pembrolizumab 200mg flat dose q 21 as an off-label therapy. The patient started the treatment in July 2021 and is still ongoing, having achieved a complete radiological response of hepatic metastases. Although immunotherapy is not part of the standard treatment paradigm for advanced pancreatic cancer, our case suggests that it may provide substantial and durable clinical benefit in a small molecularly selected subgroup of patients who have exhausted conventional therapeutic options, highlighting the critical role of comprehensive molecular profiling in identifying actionable treatment opportunities. Full article

Background/Aims: While pathogenic germline variants play a critical role in pediatric cancer susceptibility, traditional clinical genetics primarily focuses on single-gene interpretations. Transitioning to a systems-level analysis of inherited variation can uncover shared biological vulnerabilities, informing genetic counseling, surveillance, and targeted therapeutics. This study aims to implement an unsupervised machine learning framework to identify and characterize Germline Mutation Signatures (GMS) across diverse pediatric malignancies, elucidating latent genomic patterns that reveal shared oncogenic mechanisms. Methods: We analyzed germline whole-exome and whole-genome sequencing (WES/WGS) data from a retrospective cohort of 420 pediatric cancer patients and matched non-cancer controls. Variants were deeply annotated to capture multi-dimensional features, including predicted pathogenicity, splice-site disruption, regulatory impact, population frequency, and sequence context. To enable robust modeling, we integrated an augmented feature set encompassing evolutionary constraint, loss-of-function intolerance, and compositionally normalized substitution spectra. These high-dimensional annotations were processed using a deep autoencoder for non-linear representation learning, followed by Gaussian Mixture Modeling (GMM) of the latent space. Results: The framework delineated 13 signatures (GMS1–GMS13), yielding an optimal Davies–Bouldin index of 1.051. These signatures map to fundamental biological processes, including DNA repair deficiencies, transcription-coupled damage, replication stress, and aberrant RNA regulation. Crucially, these GMSs transcend traditional tissue-of-origin classifications, manifesting across multiple distinct cancer types. This observation indicates convergent germline etiologies and suggests potential shared susceptibilities to pathway-directed therapies. Conclusions: The discovery of these cross-cancer signatures provides a scalable, biologically interpretable framework for decoding inherited pediatric cancer risk. While the therapeutic mapping networks identified are currently exploratory and serve as a hypothesis-generating foundation, this deep learning-driven paradigm establishes a robust basis for stratified precision medicine. Pending prospective clinical validation, this approach holds significant translational potential to move beyond single-gene paradigms toward unified, systems-level precision oncology strategies. Full article

Background: Artificial intelligence in medicine has largely relied on supervised training of disease-specific models, limiting scalability in conditions where labeled data are scarce. Large language models (LLMs), which encode broad medical knowledge through large-scale pretraining, offer an alternative paradigm in which structured physiological measurements can be interpreted directly without task-specific model training. Objective: To evaluate whether smartphone-derived ocular motor biomarkers can be translated into clinically meaningful probabilistic assessments of Huntington’s disease (HD) using general-purpose LLMs operating as inference engines. Methods: In this prospective proof-of-concept study, 26 participants (13 with genetically confirmed HD and 13 age-matched controls) completed a standardized ocular motor assessment using a custom smartphone application. Quantitative eye-movement metrics were validated against expert neurologist ratings. Structured physiological features were then provided to four general-purpose LLMs without task-specific training or diagnostic labels, and the models generated an AI-Assigned HD Probability Score (HAIPS). Discriminative performance and associations with clinical severity measures were evaluated. Results: Smartphone-derived ocular motor metrics showed strong agreement with clinician assessments (Spearman ρ = 0.76–0.95; all p < 0.001), confirming preservation of clinically meaningful physiological signals. LLM-derived HAIPS distinguished HD from controls with high accuracy (AUC 0.879–0.944), with no significant differences across models. Discrimination was statistically equivalent to a supervised logistic regression model trained on the same features. HAIPS correlated strongly with established measures of disease severity, including cognitive (MoCA, ρ = −0.86), functional (TFC, ρ = −0.74), and motor impairment (UHDRS, ρ = 0.85) (all p ≤ 0.003). Conclusions: Structured ocular motor biomarkers acquired using a consumer smartphone can be translated into clinically meaningful probabilistic assessments of HD by general-purpose LLMs without disease-specific model training. These findings support a framework in which physiologically grounded digital biomarkers are coupled with general-purpose inference models, potentially enabling scalable assessment in rare neurological diseases where labeled data are limited. Full article

Aconitum soongaricum, a medicinal plant endemic to the Tianshan Mountains in Xinjiang, China, produces numerous natural compounds with potential medicinal value. Mitochondria function as energy hubs and play critical roles in plant development and stress adaptation; thus, their genomic composition underpins biological functions. Here, we assembled the complete mitochondrial genome of A. soongaricum using next- and third-generation sequencing data and performed comparative analyses with related species. The mitochondrial genome exhibited a typical circular structure of 487,849 bp with a GC content of 46.80%. A total of 77 genes were annotated, including 41 protein-coding genes (PCGs), three rRNAs, 31 tRNAs, and two pseudogenes. The genome showed a strong A/U bias at the third codon position and displayed C-to-U RNA editing transitions, whereas no U-to-C transitions were estimated. Maximum-likelihood phylogenetic analysis supported a close relationship among A. soongaricum, A. carmichaelii, and A. kusnezoffii, confirming the utility of mitochondrial genomes for genetic relationship inference in genus Aconitum. Divergence time estimation placed the differentiation of A. soongaricum from the other two species at approximately 4.19 million years ago (Mya). Additionally, we evaluated the expression levels of NADH dehydrogenase (nad) genes across different tissues and under drought stress using real-time PCR, revealing diverse expression patterns. Collectively, this study provides a foundation for future investigations into the genetic mechanisms underlying evolution, energy metabolism, and environmental adaptation in A. soongaricum. Full article

Background: Patients with type 2 diabetes mellitus (T2DM) who present with ST-segment elevation myocardial infarction (STEMI) remain at high risk of adverse remodeling after reperfusion. This observational study examined whether pre-admission glucose-lowering therapy class was associated with six-month left ventricular (LV) reverse remodeling and myocardial work recovery. Methods: We analyzed 253 patients with STEMI, baseline LV ejection fraction ≤ 50%, successful primary PCI, and complete baseline and six-month echocardiography. The primary inferential analyses focused on 75 patients with T2DM, grouped according to pre-admission therapy with SGLT-2 inhibitors, GLP-1 receptor agonists, DPP-4 inhibitors, or conventional therapy; non-diabetic patients were retained as a descriptive reference group. Clinical outcome, propensity-score, subgroup, and mediation analyses were considered exploratory because of small subgroup and event counts. Results: SGLT-2 inhibitor and GLP-1 receptor agonist exposure was associated with larger improvements in LVEF, LV volumes, and global work efficiency than DPP-4 inhibitors or conventional therapy. Crude MACE rates were highest in the conventional-therapy group, but event estimates were imprecise and confounded by baseline risk, revascularization status, and discharge therapy. Conclusions: In patients with T2DM recovering from STEMI, pre-admission exposure to SGLT-2 inhibitors and, to a lesser extent, GLP-1 receptor agonists was associated with more favorable structural and myocardial work recovery. These hypothesis-generating findings should be interpreted as associations and require confirmation in adequately powered prospective studies. Full article

Estimation of population proportion is a significant problem in survey sampling and has wide application in social sciences, economics, agriculture, medicine, and public health. The accuracy of estimators can be significantly improved by effectively using auxiliary information. This study proposes an improved generalized class of estimators for estimating the population proportion using two auxiliary attributes. First-order approximation of the mathematical property is obtained for the proposed class, including the expressions for the bias and mean square error (MSE). Theoretical comparisons are made with the traditional sample proportion estimator and some existing estimators that are available in the literature. Analytical conditions under which the proposed generalized class performs better than the other estimators are also determined. In order to analyze the practical performance of the proposed methodology, numerical and simulation studies are carried out on the real and artificially generated population. The results of the experiments confirm that the proposed generalized class consistently yields lower MSE and higher PRE than the traditional estimators. It is concluded that the proposed generalized class is a reliable and efficient alternative to the population proportion estimation for practical survey sampling applications having appropriate auxiliary attributes. Full article

Theranostics is a novel approach that integrates diagnostic and therapeutic efficacy on a single platform, holding great promise for precision medicine by enabling real-time monitoring of disease progression and therapeutic response. Despite significant advances, the successful development and delivery of theranostic systems are critically limited by multiple biological barriers present at systemic, tissue, cellular, anatomical, and immunological levels. These barriers restrict bioavailability, target accessibility, and therapeutic efficacy, while often increasing off-target accumulation and adverse effects. This review provides a comprehensive overview of the major biological barriers encountered in theranostic development, including physiological barriers such as plasma protein binding, renal clearance, and hepatic metabolism; anatomical barriers like endothelial linings, the blood–brain barrier (BBB), and the tumor microenvironment; cellular barriers involving membrane permeability, intracellular trafficking, and endo-lysosomal entrapment; and immunological barriers such as immune recognition, inflammatory responses, and complement activation. Special emphasis is placed on the BBB, highlighting its structural complexity, transport mechanisms, and strategies such as molecular Trojan-horse technology, receptor-mediated and adsorptive-mediated transcytosis, and nanocarrier-based approaches to enhance central nervous system delivery. The review further discusses targeted delivery challenges, including receptor heterogeneity and multidrug resistance, and critically evaluates current strategies to overcome these barriers through surface functionalization, stimuli-responsive systems, biomimetic carriers, and controlled-release mechanisms. Finally, recent advances, clinical challenges, and future perspectives—including personalized theranostics, artificial intelligence—assisted design, and next-generation barrier-penetrating systems—are explored. Overall, this review aims to provide a structured understanding of biological barriers in theranostics and highlight innovative approaches to improve their translational potential. Full article

Stilbene-type compounds are vital plant secondary metabolites that are classified under polyphenols and generally exhibit significant biological activities, as well as potential health benefits. These compounds, prevalent in food sources and medicinal plants, are recognized for their complex structures and their roles in plant defense mechanisms against environmental stressors. Despite their beneficial properties, the natural stilbenes face limitations related to their bioavailability and solubility, highlighting the need for chemical modifications to enhance their therapeutic efficacy. Studies have focused on structural modifications of the stilbene scaffold, including the introduction of carbon-based fragments, aiming to improve the compounds’ stability, selectivity, and overall biological activities. The development of stilbene analogues through chemical modifications not only expands the library of valuable stilbene-type compounds but also holds promise for new therapeutic applications in combating chronic diseases. This review summarizes current knowledge on the sources, biological activities, and chemical modifications of stilbene compounds, emphasizing their potential in healthcare and nutrition. Full article

Relict yew plants (Taxus L.) are not only ornamental plants with valuable wood but also have the ability to synthesize the unique compound taxol, which is successfully used in the treatment of cancer due to its powerful cytotoxic effect. Due to the presence of taxol, all parts of yew plants are extremely poisonous, but there have been cases where animals have eaten yew cones without fatal consequences. The biosynthesis of taxol is carried out due to the interaction of the isoprenoid and phenolic pathways of the secondary metabolism of plants. Despite the close attention of researchers to the peculiarities of taxol metabolism, there is very little data on the tissue and intracellular localization of both taxols and phenolic compounds in yew plants. Polyphenols are known to be physiologically active mediators involved in respiration, photosynthesis, plant growth and development, as well as in the process of in vitro dedifferentiation. Since Taxus is a relict species and has a limited and hard-to-reach range in nature, technologies that allow yew plants to be restored without removing plant material from the natural environment are of great practical importance: overcoming deep physiological dormancy of seeds, microclonal reproduction and initiation of plant growth. In vitro cultures are possible sources of biologically active and medicinal products. The aims and objectives of this study are to determine the characteristics of the formation and localization of phenolic compounds with high biological activity in various organs of plants of the genus Taxus and to determine the biological activity of ethanolic extracts from this plant. The objects of this study were the generative organs of Taxus canadensis, collected during the entire growing season (April–October) from plants growing in the Moscow region. The localization of various classes of polyphenols was determined by histochemical methods using light microscopy. Histochemical studies have shown the abundant presence of polyphenols in yew megastrobiles, microstrobiles, cones, seeds and aril. Ethanolic plant extracts were used to determine the biological activity. Flavans were dominant in the aril at various stages of vegetation, which was confirmed by our biochemical and histochemical studies. Extractive substances of T. canadensis show high antibacterial activity, especially in its shoot extracts. Ethanolic extracts from plant shoots showed greater biological activity than seed extracts. Aril extracts had the lowest cytotoxicity. Full article

Background/Objectives: The “small-n-large-p” dilemma in mammography artificial intelligence (AI)—where the number of candidate imaging features far exceeds the number of labeled cases—commonly results in model overfitting, unstable feature selection, and poor generalization across clinical settings. This study aims to reassess the internal performance of the HiTopology-GOA-CSA (Grasshopper Optimization Algorithm–Crow Search Algorithm) feature-selection framework for mammography using a larger real Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) cohort and a stricter leakage-aware evaluation strategy. Methods: In this retrospective computational study using public anonymized datasets, an expanded internal cohort of 98 CBIS-DDSM mass cases (49 benign, 49 malignant) was assembled from digital imaging and communications in medicine (DICOM) region of interest (ROI) series. A total of 1074 features were extracted per case, including 88 handcrafted radiomic descriptors and 986 EfficientNet-B5 deep features. HiTopology-GOA-CSA selected 102 features, corresponding to 91% feature reduction. Two internal evaluation modes were compared: Mode A, which matched the original pilot methodology by performing feature selection once on the full cohort before cross-validation, and Mode B, which used strict nested feature selection within training folds. Performance was assessed with 5-fold stratified cross-validation using a multilayer perceptron (MLP) classifier. Results: On the expanded cohort, Mode A achieved an area under the receiver operating characteristic curve (AUC) of 0.726 (95% CI: 0.594–0.858), sensitivity of 0.658, specificity of 0.651, and F1-score of 0.644. Under the stricter nested evaluation, Mode B achieved AUC of 0.683 (95% CI: 0.549–0.817), sensitivity of 0.598, specificity of 0.631, and F1-score of 0.595. Mean pairwise Jaccard similarity across nested folds was 0.604, indicating moderate feature stability. Benchmark comparisons showed that the proposed method was competitive but did not outperform standard baselines; LASSO logistic regression achieved the highest AUC of 0.739, while the proposed HiTopology-GOA-CSA + MLP achieved an AUC of 0.683. Real external validation on the locked VinDr-Mammo subset (n = 25) remained near-random (AUC of 0.500 [95% CI: 0.304–0.696]), with complete prediction collapse (sensitivity of 1.000, specificity of 0.000). Conclusions: The framework demonstrated feasibility for structured feature selection and stress testing in a small-cohort mammography AI setting; however, external validation revealed near-random discrimination and prediction collapse, indicating limited generalizability. These findings emphasize the need for benchmark comparisons, transparent uncertainty reporting, patient-level validation, and larger multicenter datasets before clinical translation. Full article

Human papillomavirus (HPV) infection remains the principal etiological factor in cervical cancer development worldwide, with Eastern Europe continuing to demonstrate disproportionately high cervical cancer incidence and mortality rates. Regional disparities in screening implementation, vaccination coverage, and HPV genotype distribution contribute substantially to the persistent burden of HPV-related disease. In recent years, increasing attention has focused on molecular biomarkers and the vaginal microbiome as complementary approaches for improving cervical cancer prevention strategies. This systematic review aimed to evaluate recent evidence regarding HPV genotype distribution, molecular biomarkers, vaginal microbiome composition, and their implications for cervical cancer prevention in Eastern Europe. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, and the Cochrane Library for studies published between January 2020 and May 2026. This systematic review was conducted in accordance with the PRISMA 2020 guidelines and prospectively registered in PROSPERO (CRD420261391136). Studies from Eastern European populations reporting data on HPV genotype distribution, screening strategies, vaccination, molecular biomarkers, or vaginal microbiome composition were included. HPV prevalence in screening populations ranged from approximately 12% to over 20%, with HPV16 consistently identified as the predominant genotype across all included studies. However, non-16/18 high-risk genotypes, particularly HPV31, HPV51, HPV52, HPV66, and HPV68, represented a substantial proportion of infections in several Eastern European cohorts. Studies evaluating CINtec PLUS cytology and HPV E6/E7 mRNA testing demonstrated improved specificity for identifying clinically significant cervical lesions compared with HPV DNA testing alone. Emerging evidence also suggested associations between vaginal dysbiosis, increased microbial diversity, persistent high-risk HPV infection, and progression to cervical intraepithelial neoplasia. Although the 9-valent HPV vaccine provides coverage for most circulating high-risk genotypes identified in the region, vaccination uptake remains inconsistent throughout Eastern Europe. The findings of this systematic review support the growing importance of extended HPV genotyping, molecular biomarkers, and microbiome-related approaches in cervical cancer prevention strategies in Eastern Europe. Strengthening organized screening programs, expanding vaccination coverage, and improving access to molecular diagnostic technologies remain essential priorities for reducing the regional burden of HPV-related disease. Full article

Generative artificial intelligence (AI) is transforming biological and medical research and data analysis. Beyond analyzing existing information, these models can learn complex patterns and generate new data such as realistic protein sequences, genetic variants, or clinical notes. In molecular biology, language-like sequence models can read and generate DNA, RNA, and amino acid sequences to predict genetic variant effects, design new proteins, and explore molecular functions. In medicine, large language models (LLMs) trained on biomedical literature and electronic health records (EHRs) can summarize clinical findings, identify patterns, and provide decision support for clinicians and healthcare providers. Additionally, synthetic data generation can help protect patient privacy and augment existing disease datasets. While these advances make tasks that were previously impractical possible at scale, they also carry major risks, including producing convincing but incorrect results, reflecting hidden biases in the training data, and underperforming when real-world conditions change. Full article

Background: Medication adherence is a major determinant of treatment effectiveness in psychiatric care and is influenced by patients’ attitudes toward medication and beliefs about treatment. Objective: This study aimed to evaluate medication adherence, drug attitudes, and beliefs about medicines, and to examine their relationships in the study population. Methods: A total of 300 participants were assessed using the Medication Adherence Rating Scale (MARS), Drug Attitude Inventory-10 (DAI-10), and Beliefs about Medicines Questionnaire (BMQ-General and BMQ-Specific). Descriptive statistics, independent-samples t-tests, Pearson correlation analyses, and multiple linear regression were performed. Results: The mean DAI-10 score was 3.57 ± 3.44, indicating an overall positive attitude toward medication, although 27.33% of participants had neutral or negative attitudes. The mean MARS score was 6.27 ± 2.24, suggesting moderate adherence. Mean BMQ-General and BMQ-Specific scores were 21.70 ± 5.81 and 31.64 ± 6.13, respectively. Significant gender differences were found across all scales. DAI-10 was positively correlated with MARS, while BMQ-General was negatively correlated with MARS. Multiple regression showed that DAI-10, BMQ-General, and BMQ-Specific significantly predicted MARS scores, explained 30.8% of variance after adjustment. Conclusions: Medication adherence was moderate and was significantly associated with treatment attitudes and beliefs about medicines. The findings support multidimensional assessment and targeted interventions addressing both positive attitudes and negative medication beliefs. Full article

Background/Objectives: With the increasing incidence of drug counterfeiting and the emergence of personalized medicine, the need for unique marking of solid dosage forms, e.g., tablets, has attracted considerable interest in the current research and development landscape. Besides traditional printing methods, laser marking offers several advantages, as it eliminates the need for organic solvents and enables the generation of precise patterns. However, laser exposure may raise safety concerns regarding the stability of photosensitive drugs in the irradiated dosage forms. Therefore, the aim of the present study was to test the photoprotective effect of titanium dioxide (TiO₂) and its various alternatives, e.g., talc, calcium carbonate (CaCO₃), zinc oxide (ZnO), and black iron oxide (Fe₃O₄), alongside a ready-to-use reference formulation, Opadry^® Brown, which contains TiO₂ (titanium-containing, TC) on nifedipine, a light-sensitive model drug. Methods: Laser marking or short-term laser ablation at different wavelengths (193 nm, 248 nm, 532 nm, and 781 nm) was applied to different coating formulations. As a positive control, prolonged exposure to daylight was applied. The properties and photostability of these formulations were evaluated using several analytical methods (i.e., surface profilometry, Raman spectroscopy, and high-performance liquid chromatography (HPLC)). Results: The TiO₂, ZnO, Fe₃O₄, and Opadry^® TC Brown coatings maintained their color during the long-term study under all conditions. Furthermore, the prepared formulations exhibited different ablation depths and morphological changes depending on the coating and laser type. HPLC measurements confirmed significant differences in the protective ability of various pigments against sunlight and different types of lasers. Nevertheless, the obtained Raman spectra were not in complete agreement with HPLC results, which can be attributed to spectral overlap between key nifedipine degradation markers and excipient signals in the tablet core. Conclusions: Overall, laser treatment of tablets containing photosensitive drugs may induce API decomposition; however, this effect can be minimized or avoided by careful selection of the appropriate combination of laser type and photoprotective pigment. Under the applied experimental conditions, Ti:Sa laser treatment was associated with the lowest degree of nifedipine degradation among all formulations, while ZnO-containing coatings demonstrated the most consistent photoprotective performance against the majority of the tested laser types, while Fe₃O₄-containing coatings provided superior protection during prolonged sunlight exposure and Nd:YAG laser irradiation. Full article

Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 M^pro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. Full article