Citations

Abstract Background Upper-airway and respiratory conditions impose a large and growing global burden, yet there is no standardized way to assess whether a nasal microbiome is “healthy”. Currently, monitoring remains reactive, beginning only after symptoms manifest. The nasal cavity is well suited to proactive monitoring: it shapes respiratory health and pathogen colonization resistance, and can be sampled non-invasively and repeatedly. To address this gap, we introduce the Nasal Microbiome Wellness Index (NMWI), a disease-agnostic, continuous score of nasal microbiome health derived from LASSO-penalized logistic regression. Rather than counting taxa, it learns which taxa (and in what balance) characterize a healthy nose and returns the predicted log-odds that a profile resembles a healthy state. The index was trained on 1654 nasal 16S rRNA gene amplicon sequencing samples (589 healthy, 1065 non-healthy) pooled from 27 publicly available studies, uniformly reprocessed through a single computational pipeline. Results The NMWI comprises an interpretable signature of 24 taxa whose combined relative abundances determine the health-associated log-odds. Health-associated genera such as Corynebacterium and Cutibacterium raised the score, while dysbiosis-associated genera such as Pseudomonas and Escherichia – Shigella lowered it. The NMWI substantially outperformed the Shannon, Simpson, and Chao1 diversity indices, which showed negligible, directionally inconsistent separation between healthy and non-healthy samples (|Cliff’s δ | = 0.01–0.20), whereas the NMWI produced large, consistent separation ( δ = 0.65). It achieved a balanced accuracy of 74.37% on the training data (resubstitution estimate), and 73.43% under repeated 10-times 10-fold cross-validation. Performance remained stable at mean balanced accuracy of 73.82% under a leave-one-study-out framework, reflecting cross-study generalizability. In independent external cohorts, the balanced accuracy was 71.49%, and leave-one-disease-out analysis (in which each disease condition was withheld from training) showed a mean balanced accuracy of 63.92% across unseen conditions, consistent with a disease-agnostic design. The index also generalized across heterogeneous datasets spanning multiple 16S rRNA gene hypervariable regions—to our knowledge the first demonstration of such cross-study, cross-region transferability for the nasal cavity. Conclusions The NMWI distills a complex nasal microbial profile into a single interpretable score computed directly from the 16S rRNA gene data that dominate existing nasal research, making it immediately applicable to published and future datasets without re-sequencing. By replacing descriptive, diversity-based comparison with a quantitative standard, it offers a reproducible, open-source foundation for cross-study benchmarking, individual-level phenotyping, and longitudinal respiratory wellness monitoring.

Background Lewy body disease (LBD) is a progressive neurodegenerative a-synucleinopathy, whereas isolated REM sleep behavior disorder (iRBD) is recognized as a prodromal stage of LBD. Although growing evidence implicates the gut–brain axis in neurodegeneration, the taxonomic and functional roles of the gut microbiome across the prodromal-to-symptomatic LBD continuum remain poorly defined. Methods Here, we performed shotgun metagenomic sequencing on stool samples from 25 patients with LBD (10 mild cognitive impairment due to LBD [MCI-LB] and 15 dementia with Lewy bodies [DLB]), 10 individuals with iRBD, and their household matched cohabitant controls to characterize disease-associated microbial alterations while minimizing environmental confounding. Results Despite no significant differences in global microbial diversity, we identified convergent shifts in microbial taxa, metabolic pathways, and gene families across disease stages. Both LBD and iRBD showed increased abundance of microbial taxa potentially associated with gut barrier disruption, as well as higher abundance of functional pathways related to lipopolysaccharide biosynthesis. LBD showed lower abundance of pathways related to complex carbohydrate fermentation, and both groups showed lower abundance of pathways associated with neurotransmitter-related metabolism. In particular, pathways and gene families associated with starch degradation were reduced in LBD, and those associated with histidine-to-glutamate/ GABA metabolism were reduced in both groups. Discussion These exploratory findings represent the first high-resolution, shotgun metagenomic characterization of gut microbiome alterations across the LBD continuum, highlighting functional patterns that may serve as candidate markers of disease progression in future longitudinal and mechanistic studies.

Abstract Objective. Modern stereotactic neurosurgery increasingly relies on bilateral and multi-trajectory workflows (e.g. deep brain stimulation (DBS), stereoelectroencephalography (sEEG)), which can exceed the reach and flexibility of conventional frame-based systems. Building on our skull-mounted Navinetics D1 platform, we present a stereotactic frame (Navinetics E1) intended to expand the reachable work envelope, enable independent bilateral targeting, and preserve mechanical accuracy and patient comfort. Approach. We designed the Navinetics E1 stereotactic frame using a center-of-arc architecture with five degrees of freedom per hemisphere ( X, Y, Z , Arc, Collar) and two operating modes: Double Arm (independent left/right arms for simultaneous bilateral targeting) and Single Arm (mechanically coupled arms). The system was designed in CAD and fabricated via carbon fiber-reinforced polylactic acid (PLA-CF) additive manufacturing. Collision-aware MATLAB simulations quantified work volume and admissible approach directions. Mechanical targeting accuracy was evaluated on a ground truth fixture (GTF) platform, with mixed-effects modeling to assess target- and trajectory-dependent effects. Operative feasibility was assessed through an intraoperative entry-point replication test relative to the D1 system. Main results. Compared to the D1, the E1 frame significantly expanded key geometric ranges, including, most notably, 0°–360° Collar rotation and posterior Y -axis reach, thereby increasing the work volume and trajectory range by factors of 3.39 and 8.42, respectively. Mean GTF targeting deviation was 0.78 ± 0.38 mm in Single Arm mode and 1.23 ± 0.36 mm (right) and 1.65 ± 0.47 mm (left) in Double Arm mode. Entry-point replication across 17 clinical cases achieved 76.5% success in overlapping the reference points, providing preliminary evidence of feasibility under real operative conditions. Significance. The E1 frame extends a compact skull-mounted platform to support broader stereotactic reach and trajectory while enabling independent bilateral workflows. These features may reduce intraoperative reconfiguration for complex DBS and sEEG cases. Future work will focus on increasing structural stiffness, improving angular readout/setting, and developing MRI-compatible implementations.

Abstract INTRODUCTION: Colorectal cancer (CRC) is the 2nd most fatal cancer in the U.S.; most benefits of CRC screening are from detection of advanced pre-cancerous lesions (APLs). With a goal to improve non-invasive stool test APL sensitivity, we sought novel APL signatures in stool metaproteomes (host- and gut microbiome-derived proteins) among cancer-free controls (CTRL), APL, and CRC patients. Since dysbiosis in gut microbiota is associated with CRC initiation, we also examined the microbial source of differentially expressed proteins (DEPs). METHODS: Mass spectrometry was performed on archival stool supernatants (n=63 patients; 15 CTRL, 32 APL [16 adenomas & 16 sessile serrated lesions], 16 CRC). A sectioning and database-enrichment strategy was applied for peptide-spectrum matching. Microbial and human-derived proteins were searched against the Integrated Gene Catalogue (IGC) and UniProt human reference proteome. DEPs were identified using thresholds of P < 0.05 and fold-change ≥ 1.5. Pathway annotation of human DEPs was performed with the REVIGO webserver, while gut microbial DEPs were functionally categorized using the KEGG Automatic Annotation Server (KAAS). To assess the taxonomic distribution of microbial DEPs, sequences were aligned against the NCBI non-redundant database using BLASTP. RESULTS: Mass spectrometry detected 879 human and 32,514 microbiome-derived proteins. When limiting detection to proteins present in ≥ 75% of patients, only 117 human and 204 microbial proteins remained. There were 17 (4,875), 288 (9,638), and 63 (3,053) unique human (microbial) proteins detected in CTRL, APL, and CRC groups, respectively. When the protein presence cutoff was set at >30% of patients, there were 52 (13 human [11 upregulated, 2 downregulated], 39 microbial [28 upregulated, 11 downregulated]) DEPs detected for the CRC + APL vs CTRL comparison. Some pathways associated with upregulated human DEPs included: inflammatory response, cellular response to iron, and endothelial cell migration. The top upregulated microbial DEPs were derived from the Phocaeicola, Lachnospiraceae, and Oscillospiraceae genera. The top downregulated microbial DEPs belonged to the Bacteroides, Eubacterium, Agathobacter, Coprococcus, and Blautia genera. CONCLUSION: This work highlights the biological diversity and molecular heterogeneity of the stool proteome. We identified several host and microbial protein markers that differentiate APLs and early CRC from controls. These results provide a foundation for future validation studies, which are necessary to determine the clinical utility of these protein biomarkers prior to their integration into a noninvasive stool test. Citation Format: Emmalee J. Northrop-Albrecht, Vinod K. Gupta, William R. Taylor, Jason P. Sinnwell, Doug W. Mahoney, Patrick H. Foote, Kelli N. Burger, Jaeyun Sung, John B. Kisiel, . Comprehensive investigation of stool metaproteomes among patients with precancerous lesions and colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7656.

BACKGROUND AND OBJECTIVES: External ventricular drain (EVD) placement is the most commonly performed bedside emergent neurosurgical procedure. However, catheter misplacement occurs in approximately 25% of cases, potentially resulting in neurological injury or the need for repositioning, highlighting the need for safer and more accurate placement techniques. This review aims to (1) summarize technological innovations developed to enhance the accuracy and safety of bedside EVD placement, (2) evaluate their reported benefits and limitations, and (3) identify key barriers to their clinical implementation in critical care settings. METHODS: We conducted a systematic review of PubMed, Embase, and Scopus combining terms related to EVD placement and assistive technologies. All the studies evaluated EVD placement using simulations, phantoms, cadavers, and patient procedures conducted at the bedside and in the operating room. RESULTS: We identified 3898 records, and 76 studies met the inclusion criteria. Of these, 30 studies compared stereotactic systems, ultrasound guidance, frameless neuronavigation, and augmented or mixed reality technologies to the freehand technique. These studies consistently reported improved accuracy with advanced systems although complication rates and procedural times were variably reported. An additional 35 and 12 studies evaluated nonimmersive and immersive technologies, respectively, in noncomparative settings. Most of these demonstrated high first-pass success and mean tip deviations under 3 mm. Variability in the definitions of accuracy and procedural metrics limited data synthesis, but the overall findings suggest a promising trajectory for technology-assisted EVD placement. CONCLUSION: Assistive technologies for EVD placement show potential to improve accuracy and safety over the freehand technique. These early successes signal potential to improve bedside neurosurgical care. Realizing this promise will require future studies to standardize accuracy metrics, report time, and safety outcomes consistently and validate performance in real-world critical care settings.

OBJECTIVE Achieving submillimetric accuracy in stereotactic neurosurgery remains critical for safely targeting deep brain structures. Current workflows rely on intraoperative CT to register stereotactic frames to preoperative imaging, but this introduces additional radiation exposure, cost, and workflow complexity. Three-dimensional surface scanning (3DSS) potentially offers a fast radiation-free alternative capable of capturing detailed craniofacial anatomy and frame geometry. Therefore, the aim of this cadaveric study was to evaluate the feasibility, accuracy, and navigational compatibility of integrating 3DSS into stereotactic workflows as a potential replacement for intraoperative CT. METHODS A human cadaveric head was imaged with both thin-slice CT and structured-light 3DSS after mounting stereotactic frame components. CT, 3DSS, and computer-aided design (CAD) models were registered using a multistep point cloud alignment pipeline involving the fast point feature histogram, the random sample consensus method, and the iterative closest point algorithm. Accuracy was assessed using surface deviation error, root mean square error (RMSE), and fiducial registration error within a commercial neuronavigation platform. RESULTS Fusion of the 3D facial scan with CT provided a mean surface deviation error of approximately 0.4 mm. Registration of the 3DSS-derived N-bar localizer to its CAD reference model produced a frame alignment RMSE of 1.3 mm. Within the navigation system, the 3DSS-based workflow achieved a mean fiducial registration error of 0.14 mm (range 0.02–0.20 mm), outperforming the conventional CT-based method (mean error 0.20 mm, range 0.09–0.40 mm). CONCLUSIONS The 3DSS-based method enabled precise submillimetric stereotactic registration without the need for intraoperative CT, reducing radiation exposure and operative complexity. This workflow is fully compatible with existing navigation systems and could serve as a practical radiation-free alternative in stereotactic neurosurgery. Future work will focus on automating frame detection, incorporating artificial intelligence–driven fusion methods, and validating this approach in live surgical settings.

Abstract Introduction: Opioid use disorder constitutes a significant health crisis in the United States, contributing to high rates of opioid overdose-related deaths. A major driver of these fatalities is fentanyl, a potent synthetic opioid with both sedative and analgesic properties. These properties that make fentanyl clinically effective also increase its addictive potential. Early in opioid addiction, abnormal increases in extracellular dopamine in the nucleus accumbens (NAc) reinforce excessive drug-seeking behaviors which can lead to fatal respiratory depression. Given this mechanism, we investigated whether high frequency stimulation (HFS), mimicking human deep brain stimulation (DBS) of the ventral tegmental area (VTA) could block NAc dopamine increase following an acute lethal dose of fentanyl. We hypothesized that VTA DBS would mitigate these dopaminergic responses and prevent fentanyl-induced respiratory failure. Methods: Multiple cyclic square wave voltammetry (M-CSWV), was applied via a carbon fiber microelectrode in the NAc of urethane-anesthetized male Sprague-Dawley rats. Dopamine levels were recorded at baseline and following acute fentanyl (30 µg/kg, i.v.). HFS (130 Hz frequency, 200 µsec pulse width, and 0.2 mA amplitude) was administered to the VTA before and during fentanyl exposure. Results: Acute fentanyl administration resulted in a 178.2% increase in NAc dopamine levels from baseline, accompanied by a decline in respiratory rates to critically low levels (45 breaths per minute vs. 102 bpm), eventually resulting in 100% mortality. HFS of the VTA did not significantly alter baseline tonic dopamine levels or prevent fentanyl-induced dopamine increase in the NAc but was able to fully rescue fentanyl-induced respiratory failure.

Abstract Predicting host phenotypes from shotgun metagenomic data is essential for translating microbiome research into clinical practice)Despite the development of numerous computational tools for this task, researchers often default to traditional machine learning methods such as Random Forest)This hesitancy to adopt newer methods stems from their complexity as well as the lack of standardized evaluations, as most tools are assessed on different datasets and compared against a limited set of methods)Here, we introduce LAMPP, a standardized benchmark for evaluating methods for predicting host phenotypes from gut metagenomic data)LAMPP features a diverse range of prediction tasks and enables consistent, comparative assessments across prediction tools)Our systematic evaluation of existing tools shows that classic machine learning methods (e.g., Random Forest) perform competitively, offering both ease of use and state-of-the-art results)At the same time, it demonstrates that microbiome-based phenotype prediction remains a challenging problem)By providing a consistent platform for ongoing evaluation and access to raw sequencing data, LAMPP motivates the development of novel prediction pipelines from raw sequencing data to phenotype prediction, including novel sample representation and data augmentation strategies)LAMPP is publicly available for ongoing benchmarking at https://lampp.yassourlab.com/ .

BACKGROUND: Pharmacological treatments for fibrocalcific aortic valve stenosis (FCAVS) have been elusive for >50 years. Here, we tested the hypothesis that reactivation of oxidized sGC (soluble guanylate cyclase), the primary receptor for nitric oxide, with ataciguat is a safe and efficacious strategy to slow progression of FCAVS. METHODS: We used quantitative real-time reverse transcription polymerase chain reaction, Western blotting, and immunohistochemistry to characterize sGC signaling and the biological effects of ataciguat on signaling cascades related to nitric oxide, calcification, and fibrosis in excised human aortic valve tissue, aortic valve interstitial cells, and mouse aortic valves. We then conducted randomized, placebo-controlled phase I (14-day safety/tolerance) and phase II (6-month efficacy) trials in patients with moderate aortic valve stenosis. RESULTS: In excised human tissue, we found robust losses in sGC signaling despite upregulation of sGC subunits. In vitro, ataciguat increased sGC signaling and reduced BMP2 (bone morphogenetic protein 2) signaling in aortic valve interstitial cells. In mice with established FCAVS, treatment with ataciguat attenuated BMP signaling and slowed progression of valve calcification and dysfunction. In a phase I, randomized, placebo-controlled trial, treatment with ataciguat for 2 weeks was safe and well tolerated in patients with moderate FCAVS ( https://www.clinicaltrials.gov ; Unique identifier: NCT02049203). In a separate phase II, randomized, placebo-controlled trial, treatment with ataciguat for 6 months slowed the progression of aortic valve calcification and tended to slow the progression of valvular and ventricular dysfunction in patients with moderate FCAVS ( https://www.clinicaltrials.gov ; Unique identifier: NCT02481258). CONCLUSIONS: Collectively, this study highlights the therapeutic potential of the targeted restoration of the diseased/inactive form of sGC for treatment of FCAVS. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02049203. URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02481258.