Citations

Abstract Background All Hitachi proton pencil beam scanning facilities currently use discrete spot scanning (DSS). Mayo Clinic Florida (MCF) is installing a Hitachi particle therapy system with advanced technologies, including fast scan speeds, high beam intensity, rapid beam off control (RBOC), a skip spot function, and proton pencil beam scanning using dose driven continuous scanning (DDCS). A potential concern of RBOC is the generation of a shoulder at the end of the normal spot delivery due to a flap spot (FS) with a flap dose (FD), which has been investigated for carbon synchrotron but not for proton delivery. While investigated, for instance, for Hitachi's installation at MCF, this methodology could be applicable for all future high intensity proton deliveries. Purpose No Hitachi proton facility currently uses the proposed RBOC. This study aimed to understand the dosimetric impact of proton FD at MCF by simulating the FS with a Hitachi proton machine in research mode, reflecting the higher proton intensities expected with RBOC at MCF. Method Experiments were conducted to simulate MCF RBOC at Kyoto Prefecture University of Medicine (KPUM) in research mode, reducing delay time (Td) from 1.5 ms to 0.1 ms. 5,000 contiguous spots were delivered on the central axis for proton energies of 70.2, 142.5, and 220.0 MeV; at normal, high dose rate (HDR), and ultra‐high dose rate (uHDR) intensities; and at vertical and horizontal gantry angles for different Td. Measurements were taken using a fast oscilloscope and the nozzle's spot position monitor (SPM) and dose monitor (DM). A model was developed to predict FD dependence on beam intensity and assess the dosimetric impact for prostate and brain treatment plans. Two simulation types were planned: a flap DSS plan with FS at every spot and a flap DDCS plan with FS only at the end of each layer. Result FD was observed for RBOC with Td = 0.1 ms, showing no gantry angle dependence. FD increased with higher delayed dose rate (DDR), that is, beam intensity. The planning study showed dose volume histogram deterioration with increased FD compared to the clinical plan, but it was only significant for uHDR intensities. Deterioration was marginal in flap DSS plans for the HDR intensities planned at MCF, and flap DDCS plans were even less sensitive than flap DSS plans. Conclusion MCF is installing proton DDCS with higher beam intensities, a skip spot function, and fast beam‐off control. The resulting FD had an insignificant impact on dose distribution for two patient plans with both DSS and DDCS at the anticipated MCF intensities. However, significant dependence was observed in the case of uHDR. A method to measure the position and dose of the FS during commissioning is described in addition to recommendations for regular QA and log‐based proton patient‐specific quality assurance.

ABSTRACT In this secondary analysis of 386 subjects with Gram-negative bacteremia enrolled in the RAPIDS GN trial, rapid antibiotic susceptibility testing had a greater benefit on the management of bacteremia caused by antibiotic-resistant compared to antibiotic-susceptible isolates, especially for Escherichia coli, Klebsiella species , and Proteus species. IMPORTANCE Rapid blood culture diagnostics are costly, and their use has not demonstrated clear clinical benefit for patients with Gram-negative sepsis, possibly because prior trials did not enroll sufficient numbers of patients with antibiotic-resistant infections. This analysis of patients with sepsis previously enrolled in a randomized controlled clinical trial demonstrates that rapid susceptibility testing of bacteria from blood cultures had the greatest impact for patients with antibiotic-resistant infections. Specifically, after rapid testing results were reported, patients infected with resistant bacteria received appropriate antibiotic therapy more quickly than patients with susceptible bacteria. These findings highlight the need to evaluate blood culture diagnostics in areas with high antibiotic resistance where these diagnostics are likely to have the greatest impact.

The NCCN Guidelines for Survivorship include recommendations for screening, evaluation, and treatment of psychosocial and physical problems resulting from adult-onset cancer and its treatment. They also include recommendations to promote healthy behaviors and immunizations in survivors and provide a framework for care coordination. These NCCN Guidelines Insights summarize the panel’s current recommendations regarding sexual health and fertility.

Multigene panel testing has allowed for the detection of a growing number of inherited pathogenic/likely pathogenic variants in people at high risk of cancer, including endometrial cancer (EC). Hereditary syndromes associated with EC include Lynch syndrome, PTEN hamartoma tumor syndrome, and Peutz-Jeghers syndrome. This manuscript provides the latest recommendations from the NCCN Guidelines for Genetic/Familial High-Risk Assessment: Colorectal, Endometrial, and Gastric on the screening and management of EC in patients at high risk for these syndromes, as well as the advantages and limitations of multigene panel testing. This manuscript also describes recent updates to these guidelines regarding de-implementation of colon cancer screening in individuals with CHEK2 pathogenic/likely pathogenic variants, based on the most up-to-date evidence regarding the association between CHEK2 pathogenic/likely pathogenic variants and colon cancer risk.

Abstract The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particularly single-cell transcriptomic data containing rich cell-to-cell variations, it is highly desirable to infer TF–gene interactions (TGIs) using deep learning technologies. Numerous models or software including deep learning–based algorithms have been designed to identify transcriptional regulatory relationships between TFs and the downstream genes. However, these methods do not significantly improve predictions of TGIs due to some limitations regarding constructing underlying interactive structures linking regulatory components. In this study, we introduce a deep learning framework, DeepTGI, that encodes gene expression profiles from single-cell and/or bulk transcriptomic data and predicts TGIs with high accuracy. Our approach could fuse the features extracted from Auto-encoder with self-attention mechanism and other networks and could transform multihead attention modules to define representative features. By comparing it with other models or methods, DeepTGI exhibits its superiority to identify more potential TGIs and to reconstruct the GRNs and, therefore, could provide broader perspectives for discovery of more biological meaningful TGIs and for understanding transcriptional gene regulatory mechanisms.

ObjectiveVagus nerve stimulation (VNS) paired with rehabilitation therapy improved motor status compared to rehabilitation alone in the phase III VNS‐REHAB stroke trial, but treatment response was variable and not associated with any clinical measures acquired at baseline, such as age or side of paresis. We hypothesized that neuroimaging measures would be associated with treatment‐related gains, examining performance of regional injury measures versus global brain health measures in parallel with clinical measures.MethodsBaseline magnetic resonance imaging (MRI) scans in the VNS‐REHAB trial were used to derive regional injury measures (extent of injury to corticospinal tract, the primary regional measure; plus extent of injury to precentral gyrus and postcentral gyrus; lesion volume; and lesion topography) and global brain health measures (degree of white matter hyperintensities, the primary global brain measure; plus volumes of cerebrospinal fluid, cortical gray matter, white matter, each thalamus, and total brain). Eight clinical measures assessed at baseline were also evaluated (treatment group, age, race, gender, paretic side, pre‐stroke dominant hand, time since stroke, and baseline Fugl‐Meyer upper extremity score). Bivariate analyses compared each measure with the primary trial end point (change in Fugl‐Meyer upper extremity score from baseline to end of 6 weeks of treatment) across all subjects, with secondary analyses examining trial groups separately.ResultsMRIs were available from 80 patients (age = 59.8 ± 9.5 years, 29 women). Across all patients, less white matter hyperintensities (r = −0.25, p = 0.028) at baseline was associated with larger Fugl‐Meyer score change. In the VNS group, less white matter hyperintensities (r = −0.37, p = 0.018) and larger ipsilesional thalamus volume (r = 0.33, p = 0.046) were each associated with larger Fugl‐Meyer score change. Analysis of covariance (ANCOVA) analyses tested the interaction that each baseline measure had with treatment group and found that the model examining white matter hyperintensities had a significant interaction term, indicating 2.3 less change in Fugl‐Meyer Upper Extremity (FM‐UE) points in the VNS group relative to the control group for each point increase in modified Fazekas scale.InterpretationNeuroimaging measures are associated with extent of gains on the primary endpoint of a phase III stroke recovery trial. Among the neuroimaging measures examined, a measure of global brain health (extent of white matter hyperintensities) was better at explaining the change in arm impairment as compared with measures of regional injury; this was true when examining all study subjects as well as only those in the VNS group and is consistent with the global mechanism of action that VNS has throughout the cerebrum. Future studies can evaluate additional measures that further predict response to VNS therapy. The current findings suggest that individual patient neuroimaging results may be useful for a personalized medicine approach to stroke recovery therapeutics. ANN NEUROL 2025;97:709–719

Background: There are limited evidence-based guidelines to predict which osteochondritis dissecans (OCD) lesions will heal with nonoperative treatment. Purpose: To train a set of classification algorithms to predict nonoperative OCD healing while identifying new clinically meaningful predictors. Study Design: Case-control study; Level of evidence, 3. Methods: Patients with OCD of the knee with open physes undergoing nonoperative management were prospectively queried from the Research on OCD of the Knee (ROCK) cohort ( https://kneeocd.org ) in April 2022. Patients were included if they met the study criteria for nonoperative treatment success or failure. Nonoperative treatment success was defined as complete healing on magnetic resonance imaging (MRI) and total return to sports participation. Failure was defined as the crossover from nonoperative management to surgery at any point at or beyond the 3-month follow-up. If a patient did not meet one of these criteria, they were not included. Normalized lesion size, lesion location, patient characteristics, and symptoms were used as clinically relevant predictors. Results: A total of 64 patients were included, of whom 24 (37.5%) patients successfully healed with nonoperative management. Multivariate logistic regression revealed that a 1% increase in normalized lesion width was associated with an increase in the likelihood of nonoperative failure (odds ratio [OR], 1.41 [95% CI, 1.17-1.81]; P < .01). By contrast, lesions in the posterior sagittal zone (OR, 0.08 [95% CI, 0.009-0.43]; P < .01) or the medial-most coronal zone (for lesions of the medial femoral) and lateral-most coronal zone (for lesions of the lateral femoral condyle) on MRI (OR, 0.05 [95% CI, 0.004-0.44]; P < .01) were associated with a decrease in the likelihood of nonoperative treatment failure. Support vector machines had a cross-validated area under the receiver operating characteristic curve of 0.89 and a classification accuracy of 83.3%. Conclusion: Lesion location in the posterior aspect of the condyle on sagittal MRI and lesion location in the medial-most or lateral-most locations on coronal MRI were identified as statistically significant predictors of increased nonoperative treatment success on multivariate analysis. Machine learning models can predict which OCD lesions will heal with nonoperative management with superior accuracy compared with previously published models.

Background: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are commonly used to treat obesity and diabetes but are linked to a variety of gastrointestinal (GI) adverse events (AEs). Real-world data on GLP-1 RA-related GI AEs and outcomes are limited. This study assessed GI AEs and adverse outcomes using the US FDA Adverse Event Reporting System (FAERS). Methods: This retrospective pharmacovigilance study used the US FDA FAERS database (2007–2023). We searched GLP-1 RA medications, AEs, and adverse outcomes. Demographic, treatment indication, and AE data were collected. Descriptive analysis involved frequencies and percentages, while reporting odds ratio (ROR), proportional reporting ratio, Bayesian confidence propagation neural network, and multivariate logistic regression were used to analyze GLP-1 RA-related GI AEs and outcomes. Results: From 2007 to 2023, a total of 187,757 AEs were reported with GLP-1 RAs, and 16,568 were GLP-1 RA-associated GI AEs in the US. Semaglutide was linked to higher odds of nausea (IC025: 0.151, βCoeff: 0.314), vomiting (IC025: 0.334, βCoeff: 0.495), and delayed gastric emptying (IC025: 0.342, βCoeff: 0.453). Exenatide was associated with pancreatitis (IC025: 0.601, βCoeff: 0.851) and death (ROR: 4.50, IC025: 1.101). Overall, semaglutide had a broader range of notable adverse effects; by comparison, dulaglutide and liraglutide use was associated with fewer significant GI AEs. Conclusions: Analysis of the FAERS data reveals that GLP-1 RAs, particularly semaglutide and exenatide, are significantly associated with specific GI AEs, such as nausea, vomiting, delayed gastric emptying, and pancreatitis. Clinicians should be aware of these potential risks to ensure optimal monitoring and patient safety. This study demonstrated the utility of pharmacovigilance data in identifying safety signals, which can inform future pharmacoepidemiological investigations to confirm causal relationships. Clinicians should be aware of these potential risks to ensure optimal monitoring and patient safety.