Citations

Abstract Background Presacral tumours are rare, heterogeneous lesions. Historical classification systems for presacral tumours have provided a valuable framework, but were based on limited available data. This study aimed to develop a contemporary classification aligned with current tumour entities and clinical practice. Methods A prospectively registered systematic review of surgically managed presacral tumours published from 1975 to 2025 was combined with a retrospective multisite institutional surgical pathology review from 1986 to 2025. Pathological descriptors were extracted, cross-referenced, harmonized to current terminology, and organized through multidisciplinary expert review to obtain a new classification. Results The systematic review identified 5997 records, of which 203 studies met the inclusion criteria and contributed 181 pathological terms after generic and unclassifiable diagnoses were removed. Review of 3606 pathology reports identified 19 additional terms, bringing the total to 200 pathological terms. After harmonization to current terminology and exclusion of non-neoplastic processes, a contemporary classification was developed using biological descriptors: developmental versus acquired origin; tissue lineage (neurogenic, osseous/haematopoietic, soft tissue, or miscellaneous); and tumour behaviour (benign, locally recurrent without metastatic potential, locally recurrent with low metastatic potential, malignant non-sarcoma, or malignant sarcoma). Conclusion The Mayo Clinic classification of presacral tumours updates historical systems by reflecting the broader, more modern spectrum of neoplasms and distinguishing lesions by lineage and biological behaviour. It may improve diagnostic clarity, communication, and selection of appropriate observation, multidisciplinary assessment, and treatment pathways.

Abstract Motor speech disorders (MSDs) are early markers of neurological disease, but expert perceptual analysis is rarely available outside specialized centers. Automated speech analysis offers a scalable alternative, yet prior studies have not systematically compared modeling approaches or assessed clinically relevant metrics in independent datasets. This study compared static acoustic features, articulatory informed Phonet features, and self-supervised pretrained models for binary and multi label MSD classification. We trained and evaluated models on 583 speech samples using speaker level splits. Baseline models included logistic regression and Gated Recurrent Units (GRUs) trained on eGeMAPS and MFCCs. We extracted three types of Phonet derived features and evaluated pretrained HuBERT and SSAST models in frozen, partially fine-tuned, and fully fine-tuned configurations. Binary classification distinguished MSDs from controls, while multi label classification identified six MSD subtypes. Models were assessed using validation AUC, and cut points were tested on two independent datasets. Pretrained and Phonet based models substantially outperformed static acoustic features. In binary classification, HuBERT achieved the highest AUC (0.95), while compact Phonet derived GRUs achieved comparable performance (up to 0.94). These models generalized well to independent datasets, maintaining high sensitivity (0.94) and specificity (0.97). In multi label classification, Phonet models achieved the highest macro average AUC (0.86), but threshold-based subtype performance declined on unseen data. Automated MSD detection is feasible and clinically promising. Binary classification generalized well, whereas multi label classification showed limited threshold stability across datasets.

Abstract Background Breast cancer is etiologically heterogeneous, but which risk factors differ in their associations across tumor subtypes remains unclear. We conducted a large, pooled analysis to evaluate independent, dose-response associations between breast cancer risk factors and quantitative tumor features. Methods Analyses of 15,731 invasive breast cancers from 24 studies evaluated associations (p-trend) between reproductive and hormonal factors, body mass index (BMI), alcohol, smoking, and family history in relation to quantitative immunohistochemistry measures on tissue microarrays (ER, PR, HER2, KI67, TP53) and tumor grade. Analyses in a subset of 10 population-based studies estimated subtype-specific odds ratios (ORs) comparing cases to controls. A Bayesian False Discovery Probability (BFDP) <0.2 was used to identify associations with strong statistical evidence. Results Nulliparity and later age at menopause were associated with higher ER-positivity (p-trend=0.021 and 0.001, respectively), with corresponding OR[ER+] (95% CI) = 1.49 (1.16-1.90) for nulliparous vs. parous and 1.07 (1.03-1.11) per 5 years. Current combined menopausal hormone therapy (MHT) use was associated with lower grade (p-trend<0.001), with OR [grade1] = 3.37 (2.69-4.21) for current vs. never users. Higher BMI was associated with lower ER-positivity and higher grade in premenopausal women (p-trend<0.001 and <0.001), with OR[ER+] = 0.80 (0.74-0.87) and OR[grade1] = 0.75 (0.63-0.88) per 5 units, and with higher PR-positivity and higher grade in postmenopausal women (p-trend<0.001 and <0.001), with OR[PR+] = 1.08 (1.03-1.14) and OR[grade 3] = 1.10 (1.04-1.17) per 5 units. Conclusion This pooled analysis of 15,731 cases showed that nulliparity, age at menopause, MHT, and BMI have independent, dose-response associations with ER, PR, and grade, clarifying patterns of etiologic heterogeneity. Associations with HER2, KI67 and TP53, or other risk factors did not meet our threshold for strong evidence.

Abstract Background: Endocrine therapies remain central to estrogen receptor alpha-positive (ERα+) breast cancer treatment, yet their predominantly cytostatic effects highlight the need for combinatorial approaches that promote tumor cell death. We therefore tested BH3 mimetics as single agents and in combination with (Z)-endoxifen (ENDX) across endocrine sensitive and endocrine resistant ERα+ breast cancer models. Methods: Six BH3 mimetics were tested in MCF7 and T47D cells and matched ENDX-resistant and fulvestrant-resistant derivatives. ENDX combinations were assessed using 2D proliferation and synergy assays, 3D RASTRUM® bioprinted cultures, migration assays, and in vivo MCF7 xenograft studies. Results: BH3 mimetics showed broad anti-proliferative activity as single agents in endocrine sensitive and resistant models. ENDX combinations were predominantly additive in 2D assays, with limited synergy confined to specific dose windows. In 3D culture, selected combinations of an MCL1 inhibitor (MCL1i) or navitoclax with ENDX produced greater suppression than single agents and, in several conditions, reduced growth below baseline, consistent with loss of viability. Combination treatment also enhanced suppression of migration under select concentrations. In MCF7 xenograft studies, ENDX robustly suppressed tumor growth; however, adding MCL1i or navitoclax did not provide additional antitumor benefit at the doses, schedules, and timeframes tested. Conclusion: BH3 mimetics show activity in ERα+ breast cancer models in vitro , including endocrine resistant models. Although ENDX combination benefit did not translate in vivo under the conditions tested, these data support further study of optimized BH3 mimetic combinations, including evaluation of dose, ratio, schedule, tumor target engagement, and other clinically advanced agents.

Background The current classification of pulmonary hypertension (PH), based largely on expert opinion, has limitations in prognostication and guiding therapies. We hypothesize that novel PH clusters that predict survival will reveal mechanistic phenotypes associated with biomarkers of vascular health across all PH groups. Methods We first identify novel PH clinical clusters by performing unsupervised clustering analysis on the CC‐PH (Cleveland Clinic PH) registry (N=1529). We develop classification models to predict the new PH clusters and then apply them to the multicenter PVDOMICS (Pulmonary Vascular Diseases Phenomics) cohort (N=853) for validation. We compare transplantation‐free survival across the new PH clusters. We quantify metabolites of the arginine‐nitric oxide pathway and D‐dimer levels and calculate global arginine bioavailability (arginine/[ornithine+citrulline]) to assess endothelial function and activation in the new clusters and link these biomarkers to clinical outcomes. Results Clustering analysis identify 3 clear clusters in CC ‐ PH that are validated in PVDOMICS and outperform conventional classifications in predicting transplantation‐free survival. The phenotype associated with the worst survival is characterized by reduced lung diffusion capacity, decreased arginine bioavailability and nitrate levels, and elevated D‐dimer levels, consistent with loss of pulmonary microcirculation and endothelial dysfunction. Conclusions We identify new informative PH phenotypes associated with mortality and defined by biomarkers of endothelial function and activation. Loss of endothelial health and pronounced pulmonary vascular rarefication contribute more substantially to mortality across the spectrum of PH than right heart function. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02980887.

Background: We had a large-scale measles exposure event affecting 626 patients. Management of exposed patients was urgent, involving quarantine, post-exposure prophylaxis (PEP), and symptom monitoring on a case-by-case basis. Cases were initially tracked and managed using a combination of multiple spreadsheets amongst stakeholders as well as lists within the electronic health record (EHR). Using shared spreadsheets and lists created significant challenges with maintaining and editing multiple copies and updating workbooks simultaneously. Methods: Infection Prevention and Control (IPAC) worked with primary care personnel and EHR developers to create a shared registry that would prioritize communication and follow-up for exposed patients based on age and immunity. Patients are initially added to the shared registry within the EHR by reviewing non-registry reports and tagging patients, identified using exposure timeframes that overlapped with the index measles cases, in a manner that transferred them to the shared registry. From this registry, measles, mumps, and rubella vaccine status can be confirmed, immune status can be identified, and criteria for PEP assigned. High-risk patients are then contacted with recommendations from IPAC and primary care. Communication and follow-up are tracked directly within the shared registry to ensure appropriate patient management. The exposure management registry is further leveraged throughout the measles outbreak timeframe by allowing real-time monitoring of patients that require additional precautions or guidance related to their exposure. Result: The shared registry enhanced documentation accuracy and transparency between stakeholders. It led to availability of real-time status of exposed patients for the duration of the measles outbreak timeframe. Overall, the registry enabled time-sensitive patient communication to all exposed patients and rapid identification of those who needed PEP. Communication could happen rapidly and seamlessly between care teams within the organization, and the necessary information could be shared quickly with public health authorities as required. Conclusion: Large scale exposures are a challenge to manage and ensure appropriate action and communication occurs. Errors can be prevented by having a single shared exposure management registry. A registry enables care teams to quickly identify and prioritize patients for measles follow-up, communication, PEP, and ongoing management across outbreak timeframes. This process reduces both time and operational burdens of exposure follow-up tracking outside of the EHR.

Background: Measles is a highly transmissible virus. A single case can result in significant exposures within a healthcare facility and prompt action is needed to prevent secondary cases. In October 2025, Infection Prevention and Control (IPAC) was notified of a patient being admitted to our facility with recent international travel and symptoms consistent with measles. The patient was unvaccinated and had five healthcare facility visits prior to measles being suspected and subsequently confirmed. Objective: To describe our facility’s effective identification, prioritization, and prophylaxis of individuals exposed to measles. Methods: IPAC leveraged the electronic medical record to identify potentially exposed patients. Patients were quickly assessed for age and immune status, then prioritized and assigned to primary care teams. These teams promptly contacted patients and coordinated PEP administration. Results: Across five encounters, the index patient exposed 539 other patients. Due to delayed notification, our teams had less than four days to administer PEP for those exposed and still within PEP window. PEP administration began within hours of the index case’s positive test and patients received PEP within three days (Table 1). No secondary measles cases occurred within our facility. Conclusion: Timely and coordinated multidisciplinary effort enabled us to rapidly respond to our first measles case and control secondary transmission. Although the index case resulted in a large exposure, patients that were exposed and eligible for PEP were quickly prioritized to prevent secondary transmission.

Background: Advanced planning and adaptable response strategies are essential for preventing measles transmission in healthcare facilities. Objective: To describe a measles management plan and subsequent modifications in response to a cluster of measles cases. Methods: Drawing on previously published work (doi:10.1017/ice.2025.49), the Infection Prevention and Control (IPAC) team initiated a multi-disciplinary team and developed a measles preparedness plan with the following domains: Guidance and education; early detection and testing; isolation and patient movement; contact tracing and exposure management (Table 1). In October 2025, a patient was admitted during their fifth healthcare encounter and was diagnosed with measles. Two family members were later hospitalized with measles. Delayed recognition of the first case prompted enhancements to our preparedness framework (Table 1). Results: The first case resulted in 539 exposed patients. The second resulted in 87 exposures, and the third resulted in no exposure. Despite the extensive exposure, zero secondary measles were reported. Conclusion: Proactive planning enabled rapid response to our first case and controlled secondary transmission. Despite extensive planning, delayed identification of the first case caused significant exposures. As the cluster unfolded, we refined our plan, resulting in zero exposures from the last patient and zero secondary cases, despite over 850 exposures. These efforts may guide other healthcare facilities in improving their measles preparedness and response.