Karol Jędrasiak

Background/Objectives: Continuous injection-pressure monitoring may provide quantitative insight into fascial plane hydrodissection, but comparative data remain limited. This study compared baseline-corrected mean pressure and linear pressure trends during PECS I block (interpectoral plane block) versus superficial parasternal intercostal plane (SPIP) block and rectus sheath block (RSB) versus transversus abdominis plane block (TAPB). Methods: Each ultrasound-guided technique was performed once in 10 fresh, unfrozen human cadavers. Saline was infused at 10 mL/min, and pressure was recorded continuously. A deterministic signal-processing algorithm estimated the onset of the post-puncture pressure plateau; the subsequent 40 s segment was analysed after subtraction of the pre-insertion baseline. Paired t-tests were used, with exact sign-flip permutation tests as sensitivity analyses and with Holm adjustment for multiplicity. Results: Mean pressure was 4.99 psi for PECS I and 5.09 psi for SPIP, with no significant difference between techniques (mean paired difference, 0.09 psi; 95% confidence interval [CI], −0.67 to 0.85; p = 0.790). TAPB showed a higher mean pressure than RSB (5.50 versus 4.47 psi, respectively; mean paired difference, 1.03 psi; 95% CI, 0.36 to 1.69; p = 0.0134; paired effect size, d = 1.11). Permutation analyses were concordant. Linear pressure trends did not differ for SPIP versus PECS I (p = 0.484) or TAPB versus RSB (p = 0.742). Conclusions: Continuous pressure monitoring was used to characterize the hydrodissection pressure of four fascial plane block techniques. PECS I and SPIP yielded closely aligned mean values, whereas TAPB was associated with higher pressure than RSB. These findings provide quantitative reference data and support further clinical evaluation of continuous injection-pressure monitoring.

This narrative review synthesizes evidence linking periodontal dysbiosis with Alzheimer’s disease, all-cause dementia, and dementia-relevant mechanisms, focusing on the red complex pathogens P. gingivalis, T. denticola, and T. forsythia and on the translational meaning of periodontal therapy. A PubMed-centered literature search up to May 2026 informed this synthesis (a narrative review, not a registered systematic review or meta-analysis) of 46 periodontal-scope sources, supplemented by five contextual references identified outside the periodontal search (51 references in total). P. gingivalis shows the strongest mechanistic support, including gingipains, lipopolysaccharide, outer membrane vesicles, endothelial stress, and neuroinflammatory signaling; T. denticola shows moderate biological plausibility; and T. forsythia remains mainly hypothesis-generating. Human studies associate periodontitis, tooth loss, oral-hygiene indicators, and periodontal-care exposure with dementia-relevant outcomes, but residual confounding, reverse causation, dental-care access, and heterogeneous endpoints preclude causal inference. Notably, direct targeting of a single periodontal pathogen has not shown clinical benefit, as the gingipain inhibitor atuzaginstat failed in the GAIN trial, contrasting with the modest success of amyloid-targeting therapies. Current evidence supports graded plausibility rather than causal certainty, and a registered multi-database systematic review with neurological endpoints is needed before meta-analytic clinical claims can be made.

Teleconsultations are exposed to digital impersonation and synthetic media attacks that can alter identity, articulation, or consent evidence, introducing emerging AI-enabled biosecurity risks to digitally mediated healthcare workflows. We evaluate an explainable integrity control based on motion dynamics and audiovisual temporal coherence, designed for conservative operation at extremely low false alarm rates with auditable evidence reporting to support governance and risk-based escalation. Our contribution is a reproducibility-oriented evaluation protocol and an evidence atlas of temporally grounded cues, together with a staged fusion that supports scalable prescreening and targeted verification under platform-style degradations characteristic of real-world dissemination chains. Building on prior biomarker-oriented analyses of physiological and structural cues for synthetic media detection, this work advances toward a calibrated, deployment-oriented integrity control architecture optimized for operational screening in telemedicine workflows. We use the DeepFake RealWorld (DFRW) dataset with 46,371 clips (229.28 h), combining 4,186 Open-Source Intelligence (OSINT) samples and 42,185 controlled, systematically degraded variants emulating recompression, resizing, filtering, and recapture. On the held-out binary-labeled test split, descriptor fusion reaches an AUC of 0.91 and achieves a true positive rate (TPR) of 18.5% (95% CI 16.2–20.8) at a false positive rate (FPR) of 0.1%, compared with 6.2% (95% CI 4.8–7.6) for an Xception baseline fine-tuned on the DFRW dataset. Microbenchmarked latencies motivate a two-stage deployment with explicit abstention and a structured integrity report aligned with healthcare governance and post-incident auditing requirements. This study evaluates telemedicine-oriented integrity controls using a benchmark dataset and does not claim demographic or clinical subgroup generalizability. Before deployment in clinical workflows, the proposed approach requires broader validation across age groups, ethnic backgrounds, speech characteristics, capture conditions, and medical conditions that may affect facial motion, articulation, or voice production. Prospective subgroup validation and governance-oriented assessment should therefore be treated as necessary directions for future work.

Background: Perineural catheter migration is a clinically relevant cause of continuous block failure, but the present study was not designed to model true clinical displacement. Instead, we investigated whether low-rate infusion pressure differs between two predefined catheter–tissue environments, interfascial and intramuscular, under controlled ex vivo conditions. Methods: Sixty porcine thigh specimens were studied. Under ultrasound guidance, a catheter-over-needle system with a multi-orifice catheter was placed either in the interfascial plane or intramuscularly, with one measurement obtained from each specimen. After baseline recording outside the tissue, saline was infused at 5 mL/h for 10 min. Pressure recordings were normalized to baseline. For each trace, a representative value was obtained using a predefined automated stable-segment algorithm, and between-group differences were assessed using Welch’s t-test. Results: Mean normalized pressure was higher during intramuscular than interfascial infusion (0.3346 ± 0.0635 PSI [17.3 ± 3.3 mmHg] vs. 0.1917 ± 0.0285 PSI [9.9 ± 1.5 mmHg]). The between-group difference was significant (mean difference: 0.1430 PSI [7.4 mmHg], 95% CI: 0.1181 to 0.1679 PSI; p = 7.22 × 10⁻¹⁵), with a very large standardized effect size (Hedges’ g = 2.87), reflecting strong statistical separation between the two predefined groups under controlled ex vivo conditions rather than clinical discriminative ability. However, the absolute pressure difference remained small. Conclusions: Under controlled ex vivo conditions, mean normalized infusion pressure differed between predefined interfascial and intramuscular catheter positions. However, the absolute difference was small. This binary model does not represent real catheter displacement, and the findings do not support current clinical applicability, individual-level interpretation, or the definition of a clinically usable threshold. The results should be considered exploratory and hypothesis-generating.

Introduction: Reliable identification regarding interfascial spaces proves essential to achieve successful nerve block analgesia; however, ultrasound guided approaches are recognized as challenging, particularly in obese or pediatric patients. In prior cadaveric and clinical investigations, multiple approaches were evaluated to identify methods for measuring injection pressures as a function of needle position relative to fascia. Our previous study proposed simpler method of finding interfascial spaces with the needle tip. In this study, it was examined whether needle tip design influences injection pressures during regional anesthesia procedures, via an ex vivo pig specimen setup. Methods: A bespoke apparatus for tracking injection pressure was deployed to enable continuous measurement of intraluminal pressure generated while delivering saline throughout ultrasound guided peripheral nerve block needles conducted within pig thigh specimens. Delivery was performed using an infusion pump. Three types of needles of the same manufacturer (Pajunk) and same diameter (22G) but with different tips (Facet, Facet S and Sprotte) were used to measure injection pressures during penetration through tissues until interfascial plane hydrodissection was created. Statistical analyses were performed to compare pressure levels, variability, and temporal pressure trends. Results: Ninety ultrasound guided injections in porcine thigh tissue were analyzed, with thirty procedures per needle type. Injection pressure differed significantly between intramuscular, fascial puncture, and interfascial phases, showing a distinct puncture peak (p ≤ 3.44 × 10⁻¹⁴). Needle geometry significantly affected pressures across all phases (Kruskal–Wallis intramuscular p = 2.0 × 10⁻⁶, puncture p = 7.52 × 10⁻⁸, interfascial p = 9.2 × 10⁻⁵), with large pairwise effects (Hedges g up to 1.51). The classical tip produced the highest intramuscular and higher interfascial pressures, the sharp tip required the lowest puncture pressure, and the lateral tip yielded the lowest intramuscular and interfascial pressures. Conclusions: Needle tip geometry substantially influences pressure dynamics throughout the injection process, with the classical design associated with the highest fascia-penetration injection pressures and the sharp needle exhibiting the lowest, while the lateral design associated with lowest intramuscular-penetration and interfascial pressures.

Purpose: The purpose of the study was to empirically confirm that acoustic anomalies and speech artefacts may constitute interpretable and robust descriptors used to detect audio deepfakes. The study focused on identifying typical deviations in voice, prosody and acoustic spectrum parameters, which result from synthetic speech generation by TTS models and voice conversion, in particular in the sound source and articulation track mirroring layers. The following stage was to assess the stability of these characteristics in realistic distribution of the content (in the wild), including recompression, variable band and typical noise. Project and methods: A full framework was developed for unification, extraction and selection of acoustic characteristics, independent of classifiers. The analysis included the impact of the signal to noise ratio (SNR), which determines the quality of audio recording, where a low SNR value indicates strong impact of the background noise and significantly decreases the effectiveness of phase, cepstral and modulation characteristics. 46,371 clips from the DeepFake RealWorld (DFRW) set were analysed, which includes authentic and synthetic recordings, generated using various technologies (GAN, diffusion models, TTS, voice conversion). Five descriptor families were defined: tonal-glottal, cepstral-spectral, phase, energy-dynamic and prosodic-modulational. The selection was completed without using neural networks, using differential factors Δp = p_df − p_real and PR = p_df / p_real with thresholds Δp ≥ 0.15 or PR ≥ 1.5 and validity control FDR (q < 5%). Results: The analysis revealed significant differences between authentic and synthetic speech. The highest differentiation effectiveness was obtained for characteristics LFCC, CQCC and MFCC (Δp to 0.25; PR ≈ 1.6–1.8), which maintained stability after degradations typical of social medial. The jitter/ shimmer, HNR/CPP and modulation characteristics showed smoothing of prosody and excessive voice regularity (Δp ≈ 0.17–0.23). Phase characteristics were useful in detecting harmonic discontinuities, however their effectiveness dropped at low SNR. The combination of acoustic analysis with audio-video synchronisation metrics (LSE-C/LSE-D) increased the resistance to single modality disturbance attacks. Conclusions: The identified speech anomalies and artefacts are a credible and interpretable foundation of audio deepfake detection. The results have a direct application value for public safety and civil protection, because they enable building an auditable audio content layer for voice impersonation and message manipulations. In operating scenarios, such as crisis communication of public institutions, verification of authenticity of recordings disseminated in social media and analysis of sociotechnical incidents, interpretable descriptors may shorten the triage time, support the early warning and reduce the escalation risk of voice misinformation. They can be used as basis for hybrid forensic systems that combine classic acoustic descriptors with deep learning models to ensure interpretability and resistance to technological drift. The DFRW set and the applied selection method enable a comparable and repeatable evaluation of the effectiveness of characteristics in various distribution conditions. The continuation of the project (DFRWv2) will include database extension to ≥ 500,000 clips and multi-modal audio-video analyses, which will enable standardisation of the reporting of indicators Δp, PR, p_real, p_df and 95% CI in forensic studies and security engineering.

Purpose. To develop and empirically validate a ten-phase framework for the implementation of artificial intelligence (AI) in data-driven enterprises, conceptualizing AI adoption as a socio-technical transformation integrating strategic, technological, and human dimensions. Method. Multiple case study design combined with Design Science Research. Data were collected from twelve enterprises across manufacturing, logistics, and healthcare sectors through interviews, workshops, process datasets, and surveys. Cross-case and within-case analyses were used to validate and refine the framework. Findings. Organizations that completed all ten phases recorded measurable gains: +14% operational efficiency (OEE); –32% decision latency; +11% first-pass yield (FPY); >80% user adoption of AI-supported systems. Success depended less on algorithms and more on data integrity, strategic alignment, human participation, and embedded governance. Theoretical implications: Integrates fragmented models (CRISP-DM, AI Maturity Models, TOE) into a unified, process-oriented framework; redefines AI maturity as a dynamic capability-building process; extends socio-technical systems theory by proving that human participation accelerates AI adoption. Practical implications. Provides a replicable roadmap for scaling trustworthy and regulation-compliant AI aligned with the EU AI Act and ISO/IEC 42001. Demonstrates that data governance, competence development, and early compliance reduce failure risk and long-term cost. Value. The first empirically validated end-to-end managerial framework linking strategy, data governance, human competence, and regulatory accountability into a single model for AI transformation. Bridges the gap between AI research and enterprise-level implementation.

Background/Objectives: The accurate localization of interfascial planes is critical for effective regional anesthesia, yet current techniques relying on ultrasound guidance can be challenging, especially in obese or pediatric patients. Previous cadaveric and clinical studies have suggested that injection pressure varies depending on needle placement relative to fascial and neural structures. This pilot study aimed to evaluate whether the continuous monitoring of injection pressure can reliably differentiate interfascial spaces from surrounding anatomical structures in a porcine tissue model. Methods: A custom-built pressure monitoring system was used to continuously measure saline injection pressure during regional block procedures performed on porcine thighs. Injections were guided by ultrasound and conducted using an infusion pump. Needle positions were classified as intramuscular, resting on fascia, or interfascial. Statistical comparisons of pressure levels, variability, and temporal trends were conducted using Wilcoxon signed-rank tests and regression analysis. Results: Mean intramuscular pressure was significantly higher than the mean interfascial pressure (p < 1 × 10⁻¹³). Interfascial injections demonstrated lower pressure variability (p = 2.1 × 10⁻⁴) and an increasing trend in pressure over time (p = 2.1 × 10⁻⁴), whereas intramuscular injections exhibited a decreasing pressure trend (p = 3.15 × 10⁻³). Conclusions: Continuous pressure monitoring effectively distinguishes interfascial from intramuscular and fascial penetration phases during regional anesthesia. The method demonstrates potential as a real-time, objective tool for enhancing needle guidance and improving the safety and accuracy of interfascial plane blocks. Further cadaveric and clinical studies are warranted to validate these findings.

Aim: The purpose of the article was to empirically verify the hypothesis that image quality descriptors and processing artifacts can provide a stable and interpretable foundation for deepfake detection in real-world distribution conditions (in-the-wild). The study aimed to identify measurable visual characteristics, rooted in the physics of signal acquisition and processing, that allow synthetic content to be distinguished from authentic content with high resistance to platform degradation and recoding manipulation. Project and methods: The DeepFake RealWorld (DFRW) dataset comprising of 46,371 clips (4,186 authentic and 42,185 synthetic) was developed and utilized, reflecting real-world processing chains and generative models (GAN, diffusion, reenactment, face swap). For each recording, a set of 20 quality descriptors and artifacts were calculated, including BRISQUE, NIQE, PIQE, BLIINDS II, V-BLIINDS, CPBD, Wang–Bovik, PRNU, CFA, and double compression markers. Feature selection was performed without classifiers, by thresholding anomalies defined on the actual class and calculating the p_df, p_real, Δp, and PR indices with FDR control for stability and resistance to platform degradation. Results: Significant differences were found between synthetic and authentic content: on average, p_df = 41.92%, p_real = 26.54%, Δp = 0.15, PR = 1.56. BRISQUE, PIQE, Wang–Bovik, and Laplacian variance, which remained resistant to recoding and mobile filters. PRNU, CFA, and double compression features increased the evidentiary value in high-quality materials. The set of quality characteristics and processing artifacts remained stable under conditions typical for Internet distribution and can be used to calibrate uncertainty and validate forensic systems. Conclusions: The identified quality descriptors and processing artifacts provide an interpretable and robust foundation for deepfake detection, combining perceptual and technical features with acquisition physics. The DFRW dataset enables the construction of hybrid, explainable detectors that combine IQA feature analysis with deep learning models. Future research (DFRWv2) will focus on expanding the dataset to ≥ 500,000 clips with full diffusion model involvement and audio-video multimodality to standardize the reporting of θ, p_df, p_real, Δp, PR, and 95% CI parameters in forensic analyses.