BioMed, Volume 5, Issue 2 (June 2025) – 5 articles

Background/Objectives: Philadelphia has experienced a surge in illicit fentanyl adulterated with alpha-2 agonist sedatives. Initially, xylazine (“tranq”) was the predominant adulterant, and a novel multimodal withdrawal protocol was effective at mitigating symptoms. However, since mid-2024, medetomidine—a more potent sedative—has largely supplanted xylazine. Clinicians have reported more severe, treatment-resistant opioid withdrawal during this transition. To assess whether a previously effective withdrawal management protocol retained efficacy after the emergence of medetomidine as the primary fentanyl adulterant in a community. Methods: We conducted a retrospective cohort study of patients receiving protocol-based opioid withdrawal treatment at two emergency departments in Philadelphia between September 2022 and April 2025. Patients were divided into the xylazine era (September 2022–July 2024) and medetomidine era (August 2024–April 2025). The primary outcome was a change in Clinical Opioid Withdrawal Scale (COWS) score from pre- to post-treatment. Secondary outcomes included rates of discharge against medical advice (AMA) and ICU admission, as well as the impact of a revised treatment protocol. Results: Among 1269 encounters with full data, 616 occurred during the xylazine era and 770 during the medetomidine era. Median COWS reduction was greater in the xylazine group (−9.0 vs. −4.0 points, p < 0.001), with more patients achieving symptom relief (COWS ≤ 4: 65.6% vs. 14.2%, p < 0.001). ICU admission occurred in 8.5% of xylazine era patients and 16.8% of medetomidine era patients (p < 0.001). Rates of AMA were higher during the medetomidine era as well (6.5% vs. 3.6%) (p = 0.038). Revision of treatment protocols showed promise. Conclusions: The protocol was significantly less effective during the medetomidine era, though a protocol change may be helping. Findings highlight the need to adapt withdrawal treatment protocols in response to changes in the illicit drug supply. Full article

Background: Patients with Ehlers–Danlos Syndrome (EDS) and craniocervical instability are treated with extensive craniocervical fixation. A new argument and treatment are proposed related to filum terminale collagen dysfunction: the Neuro-Cranio-vertebral syndrome theory (NCVS). Objectives: To analyse clinical manifestation and imaging features of NCVS patients associated with EDS compared with 373 NCVS-affected controls, to propose an aetiopathogenic mechanism for NCVS in EDS patients, and to analyse and assess postoperative changes in NCVS patients with EDS after sectioning of the filum terminale. Methods: We conducted a retrospective study and selected ten patients diagnosed with EDS and NCVS. We present the images, signs, and symptoms in these cases, compared to those of 373 patients with NCVS alone. In addition, we report postsurgical findings in four EDS–NCVS patients after sectioning of the filum terminale. Results: Patients with EDS and NCVS had more cranial and vertebral symptoms. There were also significant differences in the neurological signs present in EDS–NCVS compared to those in NCVS alone. Patients who underwent sectioning of the filum terminale showed a significant improvement in signs and symptoms. Conclusions: The concept of craniocervical instability due to EDS does not explain a large number of neurological signs and symptoms, which seem to fit better in our new NCVS theory. Surgical treatment would only involve sectioning the filum terminale, while cervical fusion would never be justified in such patients. Full article

Background: Diabetic foot osteomyelitis is a complex condition to manage, with substantial risk of treatment failure, which could necessitate major amputations. Surgical debridement and prolonged systemic antibiotic therapy have been the mainstay of treatment, but recurrence rates remain high. The use of adjuvant local antibiotic therapy has been proposed as a potential adjunct to improve outcomes. Methods: This retrospective study involved 113 patients with diabetic foot osteomyelitis, who underwent debridement and application of antibiotic-loaded hydroxyapatite ceramic from the year 2018 to 2023. Clinical outcomes of interest were eradication of infection, ulcer healing, recurrence of infection, prevention of major amputation and mortality rate. Patient-associated factors were identified and analysed. Results: Eradication of infection was achieved in 96%, healing of ulcer in 93% and limb salvage in 95% of patients. The mortality rate at 1 year was 5.4%. Peripheral arterial disease, HbA1c and CRP levels were statistically significant in affecting treatment outcomes. Other factors had no impact on the treatment success. Conclusions: This is the largest single-centre study involving Cerament G and V in the management of diabetic foot osteomyelitis and the first investigating the specific factors associated with outcome goals. The use of these antibiotic-loaded carriers demonstrated excellent eradication of infection, healing of ulcer and limb salvage and prevention of recurrence of infection. Full article

Background: In functional magnetic resonance imaging (fMRI), functional network connectivity (FNC) captures temporal coupling among intrinsic connectivity networks (ICNs). Traditional FNC analyses often rely on linear models, which may overlook complex nonlinear interactions. We propose a multi-layered neural network that generates nonlinear heatmaps from FNC matrices, which we visualize at multiple layers, enabling us to better characterize multi-level interactions and improve interpretability. Methods: Our approach consists of two training stages. In the first, a deep convolutional neural network (DCNN) is trained to produce heatmaps from multiple convolution layers. In the second, a t-test-based feature selection identifies relevant heatmaps that help distinguish different groups. In addition, we introduce ‘source-based features’ which summarize the multi-layer model output using an independent component analysis-based procedure that provides valuable, interpretable insights into the specific layer outputs. We tested this approach on a large dataset of schizophrenia patients and healthy controls, split into training and validation sets. Furthermore, this method clarifies how underlying neural mechanisms differ between schizophrenia patients and healthy controls, revealing crucial patterns in the default mode and visual networks. Results: The results indicate increased default mode network connectivity with itself and cognitive control regions in patients, while controls showed stronger visual and sensorimotor connectivity. Our DCNN approach achieved 92.8% cross-validated classification accuracy, outperforming competing methods. We also separated individuals into three cognitive performance groups based on cognitive scores and showed that the model can accurately predict the cognitive level using the FNC data. Conclusion: Our novel approach demonstrates the advantage of employing more sophisticated models in characterizing complex brain connectivity patterns while enhancing the interpretability of results. These findings underscore the significance of modeling nonlinear dynamics in fMRI analysis, shedding new light on the intricate interplays underlying cognitive and psychiatric phenomena. Full article

Background: Accurate prognoses are challenging in high-risk vascular conditions, such as abdominal aortic aneurysms, and limited diagnostic standards, decision-making criteria, and data semantics often hinder clinical reliability and impede diagnostics’ digital transition. This study aims to evaluate the performance, robustness, and usability of an automatic, replicable pipeline for aortic lumen surface reconstruction for pathological vessels. The goal is to provide a solid tool for geometric reconstruction to a more complex enhanced diagnostic framework. Methods: A U-Net convolutional neural network is trained using preoperative CTA scans, with 101 for model training and 14 for model testing, covering a wide anatomical and aortoiliac pathology spectrum. Validation included segmentation metric, robustness, reliability, and usability assessments. Performances are investigated by means of the test set’s prediction metrics for several instances of the model’s input. Clinical reliability is evaluated based on manual measurements performed by a vascular surgeon on the obtained 3D aortic lumen surfaces. Results: The test set is selected to cover a wide portion of aortoiliac pathologies. The algorithm demonstrated robustness with an average F1-score of 0.850 ± 0.120 and an intersection over union score of 0.760 ± 0.150 in the test set. Clinical reliability is assessed using the mean absolute errors for diameter and length measurements, respectively, of 1.73 mm and 2.27 mm. The 3D surface reconstruction demonstrated reliability, low processing times, and clinically valid reconstructions. Conclusions: The proposed algorithm can correctly reconstruct pathological vessels. Secondary aortoiliac pathologies are detected properly for challenging anatomies. To conclude, the proposed 3D reconstruction application to a digital, patient-specific diagnostic tool is, therefore, possible. Automatic replicable pipelines ensured the usability of the model’s outputs. Full article