Lubomir Traikov

The expanding scope and sensitivity of genomic testing are reshaping hereditary cancer assessment, shifting attention from the detection of genomic variants alone to the more complex task of determining their significance, biological origin, and relevance to clinical management. This review examines the analytical, biological, and clinical factors that shape this process and integrates them within a five-stage translational framework encompassing variant detection, variant interpretation, biological attribution, clinical validity, and utility. Reliable interpretation requires appropriate test and gene selection, recognition of assay limitations and technically challenging variant classes, and evidence-based variant classification. Computational prediction can support variant interpretation when appropriately validated. Polygenic risk models may further refine risk estimation, although their clinical implementation remains limited by validation, calibration, and population-specific performance. Ancestry-related limitations in genomic resources remain relevant to both variant interpretation and risk estimation. Particular attention is given to biological attribution, illustrated by liquid biopsy, where clonal hematopoiesis can confound the origin and clinical interpretation of plasma variants. Genomic findings must ultimately be considered together with phenotype, family history, gene–disease validity, and disease-specific evidence when informing risk assessment, surveillance, prevention, counseling, or treatment. As hereditary cancer testing continues to expand, its value will depend less on the number of findings generated than on how reliably those findings can be translated into meaningful benefit for patients and families.

Cardiovascular diseases (CVDs) are the foremost contributor to global mortality, with a significant inherited factor that has long been recognized but only recently become decipherable. Next-generation sequencing (NGS) has transformed the study of cardiovascular genetics, allowing researchers to move beyond single-gene analyses toward comprehensive assessments of both rare and common genetic variations. This review summarizes how NGS informs clinical practice, from the molecular diagnosis of inherited cardiac disorders and risk prediction using polygenic models to emerging applications in precision therapeutics. It also discusses analytical and ethical challenges and highlights new technologies, such as long-read and single-cell sequencing, that are likely to further advance precision cardiology.

This Data Descriptor presents an anonymized, shuffled dataset of creatinine-normalized urinary metabolite measurements from 73 Bulgarian children with autism spectrum disorder (ASD), released to support reuse in secondary analyses and cross-cohort comparisons. The public release represents a pathway-oriented 24-marker subset from a broader urinary diagnostic panel, assembled as a self-contained resource for investigators working in these metabolic domains. Spot urine results are provided as individual-level values after creatinine normalization; for trimethylamine, values below the limit of quantification (LOQ) were replaced with LOQ/2. The deposit contains measurements for 24 urinary markers grouped into three functional classes (neurotransmitters and aromatic amino acid precursors; one-carbon/methylation and vitamin-related metabolites; and energy metabolism/organic acids with microbiome-related amines). The underlying cohort comprised children aged 3–13 years, and no contemporaneous neurotypical control group was enrolled. Second-morning, midstream, acid-stabilized spot urine samples were collected within the provider’s workflow; metabolites were measured by LC–MS/MS, and spot urinary creatinine was measured enzymatically for normalization. The release includes the results table in both XLSX and CSV formats, a reference limits and units file for contextual interpretation, a data dictionary, a README, a changelog, and SHA-256 checksums for integrity verification. The public files contain de-identified analytical variables only and omit individual-level demographics, dates, standalone urinary creatinine, and richer clinical metadata to preserve anonymity.

This Data Descriptor presents an anonymized, shuffled dataset of creatinine-normalized urinary metabolite measurements from 73 Bulgarian children with autism spectrum disorder (ASD), released to support reuse in secondary analyses and cross-cohort comparisons. Spot urine results are provided as individual-level values after creatinine normalization; for trimethylamine, values below the limit of quantification (LOQ) were replaced with LOQ/2. The deposit contains measurements for 24 urinary markers grouped into three functional classes (neurotransmitters and aromatic amino acid precursors; one-carbon/methylation and vitamin-related metabolites; and energy metabolism/organic acids with microbiome-related amines). The release includes the results table in both XLSX and CSV formats, a reference limits and units file for contextual interpretation, a data dictionary, a README, a changelog, and SHA-256 checksums for integrity verification. Cohort-level demographics and additional sampling details are described in the companion publication cited in the main text. Dataset: https://doi.org/10.5281/zenodo.18614881 Dataset License: Creative Commons Attribution 4.0 International.

Background/Objectives: Autism spectrum disorder (ASD) is biologically heterogeneous, and immune-linked variation may be associated with differences in tryptophan–kynurenine pathway (KP) metabolism. Here, we report a targeted urinary profile of KP metabolites, NAD (nicotinamide adenine dinucleotide), and neopterin in a Bulgarian pediatric ASD cohort to describe within-cohort patterns and associations. Methods: Second-morning, acid-stabilized spot urine was collected from 73 children with ASD in Bulgaria (3–13 years; 57 males; 16 females). No contemporaneous neurotypical control group was enrolled; therefore, laboratory-provided reference limits are reported only as contextual benchmarks and are not interpreted as ASD-specific abnormalities. Tryptophan (TRP), kynurenine (KYN), kynurenic acid (KYNA), 3-hydroxykynurenine (3-HK), quinolinic acid (QUIN), NAD, and neopterin were quantified and derived indices were computed (KYN/TRP × 1000; QUIN/KYNA). Non-parametric statistics, Benjamini–Hochberg false discovery rate (FDR) correction, and Spearman correlation analyses were applied. Results: Neopterin was strongly associated with QUIN and KYN in creatinine-normalized data (QUIN: ρ = 0.59, q36 = 2.64 × 10⁻⁷; KYN: ρ = 0.54, q36 = 3.69 × 10⁻⁶); these associations persisted when reconstructed as absolute concentrations (e.g., QUIN_abs: ρ = 0.68, q36 = 2.69 × 10⁻¹⁰) and after partial Spearman correlation controlling for spot creatinine (partial ρ = 0.46, q = 2.52 × 10⁻⁴). One NAD value was <LOQ and was imputed as ½LOQ; sensitivity analyses did not materially change inference. Conclusions: In this ASD-only cross-sectional dataset, urinary neopterin levels co-varied with urinary KYN and QUIN and with KP indices. Clinical interpretation and causal inference require controlled and longitudinal studies with richer covariate capture.

Background/Objectives: Autism spectrum disorder (ASD) is biologically heterogeneous, and immune-linked variation may coincide with differences in tryptophan–kynurenine pathway (KP) metabolism. Here, we report a targeted urinary profile of KP metabolites, NAD (nicotinamide adenine dinucleotide), and neopterin in a Bulgarian pediatric ASD cohort to describe within-cohort patterns and associations. Methods: Second-morning, acid-stabilized spot urine was collected from 73 children with ASD in Bulgaria (3–13 years; 57 males; 16 females). No contemporaneous neurotypical control group was enrolled; therefore, laboratory-provided reference limits are reported only as contextual benchmarks and are not interpreted as ASD-specific abnormalities. Tryptophan (TRP), kynurenine (KYN), kynurenic acid (KYNA), 3-hydroxykynurenine (3-HK), quinolinic acid (QUIN), NAD, and neopterin were quantified and derived indices were computed (KYN/TRP × 1000; QUIN/KYNA). Non-parametric statistics, Benjamini–Hochberg false discovery rate (FDR) correction, and Spearman correlation analyses were applied. Results: Neopterin was strongly associated with QUIN and KYN in creatinine-normalized data (QUIN: ρ = 0.59, q36 = 2.64 × 10-7; KYN: ρ = 0.54, q36 = 3.69 × 10-6); these associations persisted when reconstructed as absolute concentrations (e.g., QUIN_abs: ρ = 0.68, q36 = 2.69 × 10-10) and after partial Spearman correlation controlling for spot creatinine (partial ρ = 0.46, q = 2.52 × 10-4). One NAD value was <LOQ and was imputed as ½LOQ; sensitivity analyses did not materially change inference. Conclusions: In this ASD-only cross-sectional dataset, urinary neopterin levels co-varied with urinary KYN and QUIN and with KP indices. Clinical interpretation and causal inference require controlled and longitudinal studies with richer covariate capture.

Alzheimer’s disease (AD), one of the most common forms of dementia, where loss of memory is the first and most characteristic symptom, is emerging as one of the biggest public health burdens in the aging Western society. Although the diagnosis is frequently made with cognitive tests and imaging, the use of biochemical markers in clinical practice is emerging for early diagnosis and prevention. This research aimed to study the effects of the experimental model of AD on the protein profile of the hippocampus and cerebrospinal fluid (CSF). We used an experimental model of sporadic AD induced by intracerebroventricular (ICV) injection of streptozotocin (STZ) in adult male Wistar rats. We performed SDS-PAGE and compared the results to known alterations in the protein profiles of AD patients. This model of AD caused the occurrence of impaired spatial memory and some unexpected protein changes demonstrated by unusual spikes in the densitogram, suggesting posttranslational changes of albumin and other transporter proteins in the hippocampus and CSF. These data suggest that such posttranslational changes may partly contribute to the pathogenesis of AD and can serve as a starting point for further evaluation of the STZ model’s strengths and limitations in studying this disorder.

Our study explores the potential of a novel microwave plasma source for enhancing wound healing in BALB-C mouse models. Chronic wounds, particularly in diabetic individuals, present significant challenges due to impaired regenerative capacity. Cold Atmospheric Plasma (CAP) has emerged as a promising approach, offering diverse therapeutic benefits. However, its specific efficacy in the context of diabetic wounds remains underexplored. We developed and characterized a microwave plasma source optimized for wound treatment, inducing acute wounds and treating them with CAP in a controlled experimental setup. The treated group exhibited accelerated wound closure compared to controls, suggesting CAP’s potential to enhance the healing process. Our findings underscore CAP’s multifaceted impact on the wound healing cascade, highlighting its ability to promote angiogenesis, modulate inflammatory responses, and exhibit antimicrobial properties. These results position CAP as a promising intervention in acute wound management, paving the way for further exploration of its therapeutic potential in clinical settings.

Impaired wound healing in diabetic individuals presents a significant clinical challenge, and this study explores the impact of low-temperature microwave plasma in an argon atmosphere, a type of cold atmospheric plasma (CAP), on wound regeneration in diabetic rats. The findings reveal that this CAP treatment accelerates wound regeneration in diabetic rats, promoting faster wound closure, reducing inflammation, and enhancing critical regenerative processes such as angiogenesis, collagen synthesis, and extracellular matrix remodeling. Additionally, CAP exhibits anti-inflammatory effects by modulating the immune response towards a pro-regenerative state. These results underscore the potential of CAP in diabetic wound care, offering a promising approach to address delayed wound healing in diabetic patients and potentially improving the quality of life for those with chronic diabetic wounds.

Cold atmospheric plasma (CAP) applications in various fields, such as biology, medicine and agriculture, have significantly grown during recent years. Many new types of plasma sources operating at atmospheric pressure in open air were developed. In order to use such plasmas for the treatment of biological systems, plasma properties should fulfil strong requirements. One of the most important is the prevention from heating damage. That is why in many cases, the post-discharge region is used for treatment, but the short living particles in the active discharge zone and reactions with them are missed in that case. We use the active region of surface-wave-sustained argon plasma for biological systems treatment. The previous investigations showed good bactericidal, virucidal, seeds germination and decontamination effects at a short treatment time, but the discharge conditions for bio-medical applications need specific adjustment. A detailed theoretical and experimental investigation of the plasma characteristics and their possible optimization in order to meet the requirements for bio-medical applications are presented in this paper. The length of the plasma torch, the temperature at the treatment sample position and the microwave radiation there are estimated and optimized by the appropriate choice of discharge tube size, argon flow rate and microwave power.