Thermo Fisher Scientific (Israel)

Sulfatases are critical enzymes regulating the dynamic sulfation states of biomolecules and have profound implications in human physiology and pathology. Abnormal sulfatase activity is strongly connected to various diseases such as hormone-dependent cancers, infectious diseases like tuberculosis, lysosomal storage disorders, and bacterial virulence, making these enzymes emerge as crucial biomarkers and therapeutic targets. This review highlights the advances in small-molecule probes for selectively detecting sulfatase activity through various imaging modalities, such as fluorescence, photoacoustic, in-gel fluorogenic assays, and bioluminescence imaging. Additionally, the enzyme-activatable probes that exploit mechanisms via self-immolative linkers, intramolecular charge transfer (ICT), photoinduced electron transfer (PET), ratiometric sensing, followed by aggregation-induced emission (AIE), to achieve high specificity and sensitivity. Along similar lines, we explored substrate-based and affinity-based designs, covering turn-on, ratiometric, and dual-modal near-infrared fluorescence, chemiluminescence, and photoacoustic (NIRF/PA) probes that enable real-time, noninvasive, and deep-tissue imaging. A special focus is given to activity-based sensing of steroid sulfatases (STS) in hormone-related cancers and on profiling bacterial sulfatases for rapid mycobacterial strain differentiation. Furthermore, emerging strategies using nanoprobes and AIEgens demonstrate potential for image-guided surgery, inhalable diagnostics, and high-throughput screening of enzyme inhibitors. Together, these innovations establish a strong framework for sulfatase activity profiling and offer powerful capabilities for disease diagnostics, inhibitor screening, and therapeutic monitoring by enabling spatiotemporal visualization of sulfatase activity both in vitro and in vivo. This review may inspire the development of novel activatable sensors suited for practical biomedical applications by consolidating recent advances in sulfatase-targeted probe design and imaging strategies.

Biotherapies, and especially cell therapy products, are required to be tested for sterility and mycoplasma. Developing an analytical strategy to test for sterility and mycoplasma can be daunting. There are many variables to consider, including complex matrices with high mammalian cell density and detection of non-viable microbes. Which type of analytical testing can work in an early stage of therapeutic development and then be scaled to meet the challenges later in the production process?Leveraging rapid sterility as well as mycoplasma qPCR-based detection techniques can help provide confidence in the final product by helping to detect potential contamination earlier in the production process.

Abstract Background: In patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer (MBC) with ≥ 1 PIK3CA, AKT1, or PTEN tumor alterations, capivasertib plus fulvestrant is approved in the US for use after progression on at least one endocrine-based treatment. This study assessed real-world treatment patterns of capivasertib in the US. Methods: This retrospective cohort study used Flatiron Health electronic health record-derived database. Eligible patients were ≥18 years, diagnosed with MBC, initiated capivasertib between November 16, 2023 and July 31, 2024, and had at least 6 months (mos) of potential follow-up time from capivasertib initiation (index date). Real-world time to treatment discontinuation (rwTTD) and time to next line of treatment (rwTTNT) were assessed from the index date. Summary statistics were used to describe patient characteristics at baseline and treatment patterns. rwTTD and rwTTNT were estimated using Kaplan-Meier methods. The line of therapy (LOT) was defined using an algorithm that incorporated prior treatment use. Results: This study identified 412 patients with MBC who received capivasertib. The median age was 67 years (IQR 60, 75). The majority were female (98.8%), white (71.6%), and from community practice settings (83.0%). Prior to capivasertib initiation, sites of disease included: bone (91.0%), liver (44.2%), lung (28.2%), and brain (7.0%). PIK3CA, AKT1, or PTEN alterations were documented in 90.3% of patients: PIK3CA (75.0%), PTEN (14.3%), and AKT1 (10.0%) alterations. Co-mutations with ESR1 mutations were observed in 25.5% of patients. Prior therapies for MBC included: CDK4/6 inhibitors (87.1%), fulvestrant (66.0%), chemotherapy (31.8%), alpelisib (20.6%), and antibody-drug conjugates (14.1%) in any prior LOT. Most patients received capivasertib with fulvestrant (n=387, 93.9%); in 25 patients (6.1%), use with another endocrine therapy/combination or alone was recorded in the chart, but could not be verified due to data limitations. Among patients recorded as having received capivasertib with fulvestrant (n=387/412, 93.9%), more than half of patients received capivasertib in second-line (2L) (n=115, 29.7%) or 3L (n=102, 26.4%), though many were treated in 4L+ (n=146, 37.7%). 2L and 3L median rwTTNT was 7.1 mos (IQR 5.8, NE) and 6.9 mos (IQR 6.1, 7.8), respectively. 2L and 3L median rwTTD was 6.9 mos (IQR 5.3, NE) and 6.6 mos (IQR 5.2, 7.1), respectively (Table 1). Conclusions: Findings from this large database analysis of US patients demonstrate the effectiveness of capivasertib + fulvestrant in real-world practice. Clinical outcomes in 2L and 3L closely match those observed in the CAPItello-291 Phase 3 randomized controlled trial, which supported FDA approval of the capivasertib + fulvestrant regimen. Numerically improved outcomes were observed in patients who used capivasertib in earlier vs later line settings. Citation Format: K. H. Natsuhara, L. Park, S. Udayachalerm, R. Mireia, B. Murphy, B. Nordstrom, L. A. Huppert. Real-world Treatment Patterns of Capivasertib in Metastatic Breast Cancer in the US [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS5-05-23.

Electronic waste (e-waste) is a growing solid waste stream with largely undisclosed and poorly characterized fluorinated constituents. We evaluated per- and polyfluoroalkyl substances (PFAS) leachability from four e-waste components (phone screens, phone plastics, capacitors, and Lithium-ion batteries) using a 30-day deionized water leaching test. PFAS were extracted by solid-phase extraction using weak anion exchange (WAX) cartridges and analyzed with a liquid chromatography triple-quadrupole mass spectrometer. In addition, the PFAS chemical profiles of e-waste components were characterized by non-targeted analysis. Leachable sums of detected PFAS (∑PFAS) were highest in phone screens (1739-1932 ng·kg-1) and phone plastics (1575-2197 ng·kg-1) and an order of magnitude lower in Lithium-ion batteries (148-158 ng·kg-1) and capacitors (147-243 ng·kg-1). Short-chain perfluoroalkyl acids (PFAAs) (e.g., PFBA, PFHxA) and legacy acids (e.g., PFOA, PFNA) were more prevalent in phone screens/plastics, whereas capacitors and batteries showed mixed sulfonate/carboxylate patterns (PFOS, PFHxS, and 6:2 FTS). Although capacitors and Lithium-ion batteries contained essential PFAS with high hazard potential at trace levels, phone screens and phone plastics pose a greater risk per mass due to higher ∑PFAS levels and larger volumes. Non-targeted analysis using Orbitrap Astral revealed CF2/CF2O homologous trends (confidence levels 2-3) with corroborating targeted findings. These findings highlight the need for PFAS-free alternatives, the disclosure of fluorinated additives, and stronger end-of-life management strategies to prevent PFAS releases from e-waste.

N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations are influenced by age, which may influence the diagnostic performance of this peptide. Machine learning approaches incorporating NT-proBNP and age as continuous measures may have improved diagnostic performance. We pooled individual patient-level data for 10 369 patients [median age 73 years (25th-75th percentile: 59-82)] with suspected acute heart failure across fourteen studies. The diagnostic performance of guideline-recommended NT-proBNP thresholds (uniform rule-out threshold of 300 pg/mL and age-stratified rule-in thresholds of 450, 900, and 1800 pg/mL for patients <50, 50-75, and >75 years, respectively) and the Collaboration for the Diagnosis and Evaluation of Heart Failure (CoDE-HF) machine learning model were evaluated using random effects meta-analysis across age groups. Overall, 43.9% (4549/10 369) of patients had an adjudicated diagnosis of acute heart failure. The negative predictive value (NPV) of the rule-out threshold of 300 pg/mL was lower in older patients [NPV 88.7% (confidence interval (CI) 84.2-92.1%) in patients ≥80 years vs 98.9% (97.6-99.5%) <50 years]. Conversely, the positive predictive value (PPV) of age-stratified rule-in thresholds was lower in younger patients [PPV 62.0% (56.2-67.5%) in those <50 years vs 79.6% (70.7-86.3%) ≥80 years]. CoDE-HF was more accurate than guideline-recommended thresholds across all age groups, with NPV and PPV ranging from 96.4% to 99.5% (93.8-99.8% CIs) and 81.1% to 84.2% (74.7-90.4% CIs), respectively. The diagnostic performance of guideline-recommended thresholds of NT-proBNP varies significantly with age. A decision-support tool incorporating NT-proBNP with age as a continuous variable provides a more consistent and accurate approach.

Abstract Electric vehicles (EVs) continue to increase their share of the automotive market. To spur this growth, original equipment manufacturers (OEMs) and battery cell manufacturers have invested in atomistic modeling approaches based on fundamental science. This effort has been successful in improving vehicle performance through modifications to battery cell chemistry. However, it is our view that atomistic modeling can go a step further to affect vehicle battery design. In this perspective, we demonstrate a multi-scale modeling approach to link atomic-scale phenomena with full cell predictions relevant for battery design engineers. Recent multi-scale modeling approaches undertaken at General Motors are discussed. We show that variation in the predicted diffusivity of lithium ions in the electrolyte leads to variation in final cell temperatures of 3°C in small format cells, and that reversible volume change for common cathode materials can be as large as 10% for the full state of lithiation window. Additionally, machine learning (ML) will be presented as another growing area in the literature to drive cell-level battery design. Linking ML approaches with datasets from atomistic modeling represents a key direction of growth for vehicle design.