Johnson & Johnson (Germany)

Cerebrospinal fluid (CSF) biomarkers are central to Alzheimer's disease (AD) diagnosis and research. However, CSF composition is shaped not only by neurodegeneration, but also by underlying physiological and pathological processes that remain poorly characterized. By integrating multi-omics data from the deeply characterized memory-clinic ACE CSF cohort (N=1,372), the Global Neurodegeneration Proteomics Consortium (N=1,863), and publicly available quantitative trait loci data, we reveal that 73.2-85.9% of the molecular variance in CSF omics data is driven by two main factors: one reflecting CSF turnover rate, and another representing blood-brain barrier (BBB) integrity. CSF turnover mainly determines brain-derived molecules, while BBB damage leads to increased blood-derived protein abundance. CSF turnover/clearance severely impacted core AD biomarker levels, affecting the classification of subjects in the A/T framework. Adjusting biomarker levels for OPCML, a novel reference marker, improved biomarker-based prediction of AD progression and removed confounded associations, revealing a proteomic signature of sporadic AD pathology that closely resembles that of autosomal dominant AD. Finally, using the ACE CSF cohort as discovery (N=1,221) and Knight ADRC as replication (N=1,073), we report a curated AD signature comprising 446 unique proteins. Our findings identify CSF dynamics as a major source of molecular variation, reshaping the interpretation of CSF biomarkers.

PurposeSegmentations of retinal layers in spectral-domain optical coherence tomography (SD-OCT) images serve as a crucial tool for identifying and analyzing the progression of various retinal diseases, encompassing a broad spectrum of abnormalities associated with age-related macular degeneration (AMD). The training of deep-learning algorithms necessitates well-defined ground-truth labels, validated by experts, to delineate boundaries accurately. However, this resource-intensive process has constrained the widespread application of such algorithms across diverse OCT devices. This work validates deep learning image segmentation models across multiple OCT devices by testing robustness in generating clinically relevant metrics. DesignProspective, comparative study. ParticipantsAdults over 50 years of age with no AMD to advanced AMD, as defined in the Age-Related Eye Disease Study (AREDS)), in at least one eye, were enrolled. 402 SD-OCT scans were used in this study. MethodsWe evaluate two separate state-of-the-art segmentation algorithms through a training process using images obtained from one OCT device (Heidelberg-Spectralis) and subsequent testing using images acquired from two OCT devices (Heidelberg-Spectralis and Zeiss-Cirrus). This assessment is performed on a dataset that encompasses a range of retinal pathologies, spanning from disease-free conditions to severe forms of AMD, with a focus on evaluating the device independence of the algorithms. Main Outcome MeasuresPerformance metrics (mean-squared-error, mean-absolute-error, dice-coefficients) for the segmentations of the internal limiting membrane (ILM), retinal-pigment-epithelium (RPE), and RPE to Bruch’s-membrane (BM) region, along with en face thickness maps, volumetric estimations (in mm3). Violin plots and Bland-Altman plots comparing predictions against ground-truth are also presented. ResultsThe UNet and DeepLabv3, trained on Spectralis B-scans, demonstrate clinically useful outcomes when applied to Cirrus test B-scans. Review of the Cirrus test-data by two independent annotators revealed that the aggregated-mean-absolute-error in pixels for ILM was 1.82±0.24 (equivalent to 7.0±0.9 μm) and for RPE was 2.46±0.66 (9.5±2.6 μm). Additionally, the dice-similarity-coefficient for the RPE-drusen complex (RPE-DC) region, comparing predictions to ground truth, reached 0.87±0.01. ConclusionIn the pursuit of task-specific goals such as retinal layer segmentation, a segmentation network has the capacity to acquire domain-independent features from a large training dataset. This enables the utilization of the network to execute tasks in domains where ground truth is hard to generate.

In the real-world, retrospective, EXPAND study of consecutive adults with glaucoma, ab-externo gel stent implantation effectively lowered intraocular pressure (34%) and the medication burden (61%), with transient/self-resolving hypotony as the most frequent adverse event (28%). To assess effectiveness and safety of ab-externo gel stent (GS) implantation in glaucoma. Multicenter, real-world, retrospective study. Consecutive adults with glaucoma and ab-externo GS implantation (with/without phacoemulsification or open/closed conjunctiva) ≥12 months before study entry. Data were extracted between the baseline/preoperative and last follow-up visit or date of secondary surgical intervention (SSI). Primary effectiveness endpoint: proportion of primary eyes (first eye in bilaterally implanted patients) at month 12 (M12) achieving ≥20% intraocular pressure (IOP) reduction from baseline without medication increase, clinical hypotony, vision loss to counting fingers, or SSI. Secondary effectiveness endpoints included complete success (IOP ≤18mmHg and ≥20% IOP reduction from medicated baseline without medication, clinical hypotony, or SSI); qualified success (same but without medication increase); and needling rate. Key safety endpoints (all eyes): intraoperative complications, postoperative adverse events (AEs), and SSIs. The safety population included 466 eyes; 80.7% received the GS alone and 85.0% were implanted with closed conjunctiva. At M12, 39.1% of all primary eyes (N=413) and 54.9% of primary eyes with IOP and medication data at baseline and M12 (N=213) achieved the primary endpoint. At M12 among all primary eyes, the complete success, qualified success, and needling rates were 33.5%, 56.5%, and 28.6%. Most eyes (97.9%) had no intraoperative complications. The most frequent postoperative AE was transient/self-resolving hypotony (IOP <6mmHg; 28.1%). Sixty-nine (14.8%) eyes required an SSI. Ab-externo GS placement effectively lowered IOP and the medication count without unexpected complications/AEs, expanding implantation options based on patients' needs and surgeons' preferences.

7504 Background: Functional high-risk (FHR) multiple myeloma (MM) is associated with poor prognosis. In CARTITUDE-4, a single ciltacabtagene autoleucel (cilta-cel) infusion significantly improved progression-free survival (PFS) vs established standard of care (SC; hazard ratio [HR], 0.26 [95% CI, 0.18–0.38]; P&lt;.0001) in patients (pts) with lenalidomide (len)-refractory MM after 1–3 prior lines of treatment (tx; LOT). This post hoc subgroup analysis of CARTITUDE-4 reports outcomes for pts who received cilta-cel vs SC as second-line tx, including pts with FHR MM. Methods: Pts randomized to cilta-cel underwent apheresis, received PVd or DPd bridging tx, and then cilta-cel infusion (target dose, 0.75×106 CAR+ viable T cells/kg) 5–7 d after the start of lymphodepletion. Pts randomized to SC received PVd or DPd until progressive disease (PD). FHR was defined as PD within 18 mo after receiving autologous SCT or the start of initial frontline tx. Efficacy was assessed in randomized pts (intent to treat) and safety in pts who received any part of study tx. Results: 136 pts received cilta-cel (n=68) or SC (n=68) as second-line tx. Of these, 79 had FHR MM (cilta-cel, n=40; SC, n=39). Median PFS was longer among pts who received cilta-cel vs SC as second-line tx including the subset who had FHR MM (Table). Overall survival was immature at the time of this analysis. A greater proportion of pts who received cilta-cel vs SC as second-line tx had an overall response, complete response (CR) or better, minimal residual disease (MRD) negativity, and longer median duration of response (DOR), with similar observations among the FHR subset. Proportion of pts with grade ≥3 TEAEs who received cilta-cel vs SC as second-line tx was comparable (96% vs 96%) including the subset with FHR MM (100% vs 97%). Among pts who received second-line tx, 22 died (cilta-cel, n=11; SC, n=11), 16 of whom had FHR MM (n=7; n=9). Conclusions: In pts with len-refractory FHR MM after 1 prior LOT, cilta-cel improved outcomes vs SC and had a safety profile consistent with the known mechanism of action of CAR-T tx, suggesting cilta-cel may overcome the poor prognosis associated with FHR MM. Clinical trial information: NCT04181827 . [Table: see text]

BackgroundSurgical site infections (SSIs) represent ~ 20% of all hospital-acquired infections in surgical patients and are associated with prolonged hospital stay, admission to intensive care, and mortality. We conducted a systematic review with economic and environmental models to assess whether triclosan-coated sutures (Plus Sutures) provide benefits over non-coated sutures in the reduction of SSI risk.MethodsSearches were conducted in fifteen databases. A total of 1,991 records were retrieved. Following deduplication and screening by two independent reviewers, 31 randomized controlled trials in adults and children were included in the review.Similarity of the studies was assessed by narrative review and confirmed by quantitative assessment. A fixed effects meta-analysis of SSI incidence model including all groups of patients estimated a risk ratio of 0.71 (95% confidence interval: 0.64 to 0.79) indicating those in the Plus Sutures group had a 29% reduction in the risk of developing an SSI compared with those in the control group (p < 0.001). Safety outcomes were analysed qualitatively.ResultsThe economic model estimated the use of Plus Sutures to result in average cost savings of £13.63 per patient. Plus Sutures remained cost-saving in all subgroup analyses with cost-savings ranging between £11 (clean wounds) and £140 (non-clean wounds).The environmental impact of SSI is substantial, and the model suggests that the introduction of Plus Sutures could result in potential environmental benefits.ConclusionsThe evidence suggests that Plus Sutures are associated with a reduced incidence of SSI across all surgery types alongside cost savings when compared with standard sutures.

Automatic surgical workflow recognition enabled by computer vision algorithms plays a key role in enhancing the learning experience of surgeons. It also supports building context-aware systems that allow better surgical planning and decision making which may in turn improve outcomes. Utilizing temporal information is crucial for recognizing context; hence, various recent approaches use recurrent neural networks or transformers to recognize actions. We design and implement a two-stage method for surgical workflow recognition. We utilize R(2+1)D for video clip modeling in the first stage. We propose Action Segmentation Temporal Convolutional Transformer (ASTCFormer) network for full video modeling in the second stage. ASTCFormer utilizes action segmentation transformers (ASFormers) and temporal convolutional networks (TCNs) to build a temporally aware surgical workflow recognition system. We compare the proposed ASTCFormer with recurrent neural networks, multi-stage TCN, and ASFormer approaches. The comparison is done on a dataset comprised of 207 robotic and laparoscopic cholecystectomy surgical videos annotated for 7 surgical phases. The proposed method outperforms the compared methods achieving a [Formula: see text] relative improvement in the average segmental F1-score over the state-of-the-art ASFormer method. Moreover, our proposed method achieves state-of-the-art results on the publicly available Cholec80 dataset. The improvement in the results when using the proposed method suggests that temporal context could be better captured when adding information from TCN to the ASFormer paradigm. This addition leads to better surgical workflow recognition.