Evaluation of Mental Health and Well-Being Among Kansas Pharmacists Post-COVID-19 Pandemic.

Background COVID-19 has profoundly affected pharmacists, causing burnout from heavier workloads, personal stressors, and disrupted healthcare delivery. Research on pharmacists' mental health during the pandemic, especially in rural areas like Kansas, remains limited. Objectives This study aimed to understand perceptions, experiences, and impacts on the mental, emotional, and psychological well-being of active Kansas pharmacists during the COVID-19 pandemic, including evaluating workplace modifications on mental health. Methods Kansas licensed pharmacists were recruited via email distributions through five Kansas pharmacy organizations and informal referrals among colleagues. After consenting, respondents completed a 15-minute, 28-question survey via Qualtrics. The survey included 11 questions concerning demographics and employment characteristics, along with 17 questions designed to assess the impact of COVID-19 on mental health, structured according to existing literature. Participation was uncompensated, and incomplete surveys were omitted from the analysis. Results One hundred and seven respondents (83.59% completion) represented 3.25% of Kansas's 3,290 pharmacists. They were aged 26-66 (M=38.7), the majority female (72.57%) and white (84.84%), with 14.24 years average practice duration (SD=10.94). Data covered 12 rural and 11 urban counties, with 50.91% staff pharmacists and 22.73% pharmacy managers. Many worked over 40 hours weekly in 13 settings. Findings showed increased workload (24.68%), medication shortages (24.03%), and burnout (24.32%) affecting job considerations. Workplace changes impacted personal mental health, with the main stressors being work-related factors (19.21%), social distancing (18.95%), and health concerns (12.63%). Conclusion This study underscores the pandemic's profound toll on Kansas pharmacists' mental, emotional, and physical health, leading to burnout, job dissatisfaction, and decreased effectiveness. It emphasizes the urgency of organizational interventions.

Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder.

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2-30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.

Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purpose To develop a DL model to predict the presence of csPCa by using patient-level labels without information about tumor location and to compare its performance with that of radiologists. Materials and Methods Data from patients without known csPCa who underwent MRI from January 2017 to December 2019 at one of multiple sites of a single academic institution were retrospectively reviewed. A convolutional neural network was trained to predict csPCa from T2-weighted images, diffusion-weighted images, apparent diffusion coefficient maps, and T1-weighted contrast-enhanced images. The reference standard was pathologic diagnosis. Radiologist performance was evaluated as follows: Radiology reports were used for the internal test set, and four radiologists' PI-RADS ratings were used for the external (ProstateX) test set. The performance was compared using areas under the receiver operating characteristic curves (AUCs) and the DeLong test. Gradient-weighted class activation maps (Grad-CAMs) were used to show tumor localization. Results Among 5735 examinations in 5215 patients (mean age, 66 years ± 8 [SD]; all male), 1514 examinations (1454 patients) showed csPCa. In the internal test set (400 examinations), the AUC was 0.89 and 0.89 for the DL classifier and radiologists, respectively (P = .88). In the external test set (204 examinations), the AUC was 0.86 and 0.84 for the DL classifier and radiologists, respectively (P = .68). DL classifier plus radiologists had an AUC of 0.89 (P < .001). Grad-CAMs demonstrated activation over the csPCa lesion in 35 of 38 and 56 of 58 true-positive examinations in internal and external test sets, respectively. Conclusion The performance of a DL model was not different from that of radiologists in the detection of csPCa at MRI, and Grad-CAMs localized the tumor. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Johnson and Chandarana in this issue.

The autophagy proteome in the brain.

As one of the most important cellular housekeepers, autophagy directly affects cellular health, homeostasis, and function. Even though the mechanisms behind autophagy are well described, how molecular alterations and dysfunctions can lead to pathology in disease contexts still demands deeper investigation. Proteomics is a widely employed tool used to investigate molecular alterations associated with pathological states and has proven useful in identifying alterations in protein expression levels and post-translational modifications in autophagy. In this narrative review, we expand on the molecular mechanisms behind autophagy and its regulation, and further compile recent literature associating autophagy disturbances in context of brain disorders, utilizing discoveries from varying models and species from rodents and cellular models to human post-mortem brain samples. To outline, the canonical pathways of autophagy, the effects of post-translational modifications on regulating each step of autophagy, and the future directions of proteomics in autophagy will be discussed. We further aim to suggest how advancing proteomics can help further unveil molecular mechanisms with regard to neurological disorders.

Enterobacter adelaidei sp. nov. Isolation of an extensively drug resistant strain from hospital wastewater in Australia and the global distribution of the species.

BACKGROUND: Enterobacter species are included among the normal human gut microflora and persist in a diverse range of other environmental niches. They have become important opportunistic nosocomial pathogens known to harbour plasmid-mediated multi-class antimicrobial resistance (AMR) determinants. Global AMR surveillance of Enterobacterales isolates shows the genus is second to Klebsiella in terms of frequency of carbapenem resistance. Enterobacter taxonomy is confusing and standard species identification methods are largely inaccurate or insufficient. There are currently 27 named species and a total of 46 taxa in the genus distinguishable via average nucleotide identity (ANI) calculation between pairs of genomic sequences. Here we describe an Enterobacter strain, ECC3473, isolated from the wastewater of an Australian hospital whose species could not be determined by standard methods nor by ribosomal RNA gene multi-locus typing. AIM: To characterise ECC3473 in terms of phenotypic and genotypic antimicrobial resistance, biochemical characteristics and taxonomy as well as to determine the global distribution of the novel species to which it belongs. METHODS: Standard broth dilution and disk diffusion were used to determine phenotypic AMR. The strain's complete genome, including plasmids, was obtained following long- and short read sequencing and a novel long/short read hybrid assembly and polishing, and the genomic basis of AMR was determined. Phylogenomic analysis and quantitative measures of relatedness (ANI, digital DNA-DNA hybridisation, and difference in G+C content) were used to study the taxonomic relationship between ECC3473 and Enterobacter type-strains. NCBI and PubMLST databases and the literature were searched for additional members of the novel species to determine its global distribution. RESULTS: ECC3473 is one of 21 strains isolated globally belonging to a novel Enterobacter species for which the name, Enterobacter adelaidei sp. nov. is proposed. The novel species was found to be resilient in its capacity to persist in contaminated water and adaptable in its ability to accumulate multiple transmissible AMR determinants. CONCLUSION: E. adelaidei sp. nov. may become increasingly important to the dissemination of AMR.

Physician-reported patient involvement and treatment decisions in first-line ovarian cancer in the USA and Europe.

OBJECTIVES: Real-world data evaluating how approvals of novel treatment regimens for ovarian cancer have impacted the treatment paradigm, including first-line maintenance, are lacking. This analysis aimed to describe treatment patterns for advanced epithelial ovarian cancer in Europe and the USA in the first-line maintenance setting. Patient characteristics, biomarker testing rates, and drivers of treatment choice were also evaluated. METHODS: A retrospective chart review study of electronic medical records in Europe and the USA was conducted for patients diagnosed with epithelial ovarian cancer (June 1, 2017-May 31, 2020), in line with Healthcare Market Research guidelines. Eligible physicians extracted data from electronic medical records by completing standardized patient record forms, including questions on patient involvement in treatment decisions. Patients with advanced (stage III/IV) disease were stratified by country and diagnosis date to provide information on treatment patterns. RESULTS: Patient record forms for 7072 patients with epithelial ovarian cancer were completed by 416 physicians; 5386 patients had stage III/IV ovarian cancer. Over time, the percentage of patients who were tested for BRCA mutations or homologous recombination deficiency increased. Patient preference was documented as a reason for treatment selection in approximately one-sixth of cases in the first-line adjuvant and first-line maintenance settings. The use of first-line maintenance poly(ADP-ribose) polymerase inhibitor monotherapy increased over time, while the use of vascular endothelial growth factor inhibitor monotherapy decreased. CONCLUSIONS: This real-world study showed that treatment patterns for advanced epithelial ovarian cancer varied by country. Rates of physician-reported patient involvement in treatment decisions in the first-line adjuvant and maintenance treatment settings for ovarian cancer were low, highlighting an unmet need for initiatives to improve patient involvement in shared decision-making regarding maintenance therapy selection.

Transinfection of Wolbachia wAlbB into Culex quinquefasciatus mosquitoes does not alter vector competence for Hawaiian avian malaria (Plasmodium relictum GRW4).

Avian malaria is expanding upslope with warmer temperatures and driving multiple species of Hawaiian birds towards extinction. Methods to reduce malaria transmission are urgently needed to prevent further declines. Releasing Wolbachia-infected incompatible male mosquitoes could suppress mosquito populations and releasing Wolbachia-infected female mosquitoes (or both sexes) could reduce pathogen transmission if the Wolbachia strain reduced vector competence. We cleared Culex quinquefasciatus of their natural Wolbachia pipientis wPip infection and transinfected them with Wolbachia wAlbB isolated from Aedes albopictus. We show that wAlbB infection was transmitted transovarially, and demonstrate cytoplasmic incompatibility with wild-type mosquitoes infected with wPip from Oahu and Maui, Hawaii. We measured vector competence for avian malaria, Plasmodium relictum, lineage GRW4, of seven mosquito lines (two with wAlbB; three with natural wPip infection, and two cleared of Wolbachia infection) by allowing them to feed on canaries infected with recently collected field isolates of Hawaiian P. relictum. We tested 73 groups (Ntotal = 1176) of mosquitoes for P. relictum infection in abdomens and thoraxes 6-14 days after feeding on a range of parasitemias from 0.028% to 2.49%, as well as a smaller subset of salivary glands. We found no measurable effect of Wolbachia on any endpoint, but strong effects of parasitemia, days post feeding, and mosquito strain on both abdomen and thorax infection prevalence. These results suggest that releasing male wAlbB-infected C. quinquefasciatus mosquitoes could suppress wPip-infected mosquito populations, but would have little positive or negative impact on mosquito vector competence for P. relictum if wAlbB became established in local mosquito populations. More broadly, the lack of Wolbachia effects on vector competence we observed highlights the variable impacts of both native and transinfected Wolbachia infections in mosquitoes.

Interhospital transfer dynamics for patients with intracranial hemorrhage in Massachusetts.

Introduction: Intracranial hemorrhages present across a spectrum of clinical phenotypes, with many patients transferred across hospitals to access higher levels of neurocritical care. We sought to characterize patient dispositions following intracranial hemorrhage and examine disparities associated with interhospital transfers. Methods: Using the Healthcare Cost and Utilization Project database, we mapped and identified factors influencing the likelihood of patient transfers and receipt of specialist interventional procedures following intracranial hemorrhage. Results: Of 11,660 patients with intracranial hemorrhage, 59.4% had non-traumatic and 87.5% single compartment bleeds. After presentation, about a quarter of patients were transferred to another facility either directly from the ED (23.0%) or after inpatient admission (1.8%). On unadjusted analysis, patients who were white, in the upper income quartiles, with private insurance, or resided in suburban areas were more frequently transferred. After adjusting for patient-and hospital-level variables, younger and non-white patients had higher odds of transfer. Hospital capabilities, residence location, insurance status, and prior therapeutic relationship remained as transfer predictors. Transferred patients had a similar hospital length of stay compared to admitted patients, with 43.1% having no recorded surgical or specialist interventional procedure after transfer. Discussion: Our analysis reveals opportunities for improvement in risk stratification guiding transfers, as well as structural challenges likely impacting transfer decisions.

Non-invasive Brain-Computer Interfaces: State of the Art and Trends.

Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to widely influence research, clinical and recreational use. Non-invasive BCI approaches are particularly common as they can impact a large number of participants safely and at a relatively low cost. Where traditional non-invasive BCIs were used for simple computer cursor tasks, it is now increasingly common for these systems to control robotic devices for complex tasks that may be useful in daily life. In this review, we provide an overview of the general BCI framework as well as the various methods that can be used to record neural activity, extract signals of interest, and decode brain states. In this context, we summarize the current state-of-the-art of non-invasive BCI research, focusing on trends in both the application of BCIs for controlling external devices and algorithm development to optimize their use. We also discuss various open-source BCI toolboxes and software, and describe their impact on the field at large.

A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks.

BACKGROUND: There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imaging (MRI). The goal of the current study is to investigate whether modern, flexible architectures such as EfficientNet provide any performance boost over more standard architectures. METHODS: MRI data was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and processed with a minimal preprocessing pipeline. Among the various architectures tested, the minimal 3D convolutional neural network SFCN stood out, composed solely of 3x3x3 convolution, batch normalization, ReLU, and max-pooling. We also examined the influence of scale on performance, testing SFCN versions with trainable parameters ranging from 720 up to 2.9 million. RESULTS: SFCN achieves a test ROC AUC of 96.0% while EfficientNet got an ROC AUC of 94.9 %. SFCN retained high performance down to 720 trainable parameters, achieving an ROC AUC of 91.4%. COMPARISON WITH EXISTING METHODS: The SFCN is compared to DenseNet and EfficientNet as well as the results of other publications in the field. CONCLUSIONS: The results indicate that using the minimal 3D convolutional neural network SFCN with a minimal preprocessing pipeline can achieve competitive performance in AD classification, challenging the necessity of employing more complex architectures with a larger number of parameters. This finding supports the efficiency of simpler deep learning models for neuroimaging-based AD diagnosis, potentially aiding in better understanding and diagnosing Alzheimer's disease.

BLADDER score: evaluating a tool to support urinary diagnostic and antibiotic stewardship in hospitalized adults.

OBJECTIVE: Antibiotic overuse for asymptomatic bacteriuria is common in older adults and can lead to harmful outcomes including antimicrobial resistance. Our objective was to evaluate the impact of a simple scoring tool on urine culturing and antibiotic prescribing for adults with presumed urinary tract infections (UTI). DESIGN: Quasi-experimental study using interrupted time series with segmented regression to evaluate urine culturing and urinary antibiotic use and length of stay (LOS), acute care transfers, and mortality 18 months before and 16 months after the intervention. SETTING: 134-bed complex continuing care and rehabilitation hospital in Ontario, Canada. PARTICIPANTS: Nurses, nurse practitioners, physicians, and other healthcare professionals. INTERVENTION: A multifaceted intervention focusing on a 6-item mnemonic scoring tool called the BLADDER score was developed based on existing minimum criteria for prescribing antibiotics in patients with presumed UTI. The BLADDER score was combined with ward- and prescriber-level feedback and education. RESULTS: Before the intervention, the mean rate of urine culturing was 12.47 cultures per 1,000 patient days; after the intervention, the rate was 7.92 cultures per 1,000 patient days (IRR 0.87; 95% CI, 0.67-1.12). Urinary antibiotic use declined after the intervention from a mean of 40.55 DDD per 1,000 patient days before and 25.96 DDD per 1,000 patient days after the intervention (IRR 0.68; 95% CI, 0.59-0.79). There was no change in mean patient LOS, acute care transfers, or mortality. CONCLUSIONS: The BLADDER score may be a safe and effective tool to support improved diagnostic and antimicrobial stewardship to reduce unnecessary treatment for asymptomatic bacteriuria.

Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R 2 = 0.210, P < 0.001), performing significantly better than the theory-based biomarkers of lesion load of the corticospinal tract (R 2 = 0.132, P < 0.001) and of multiple descending motor tracts (R 2 = 0.180, P < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R 2 = 0.200, P < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R 2 = 0.167, P < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved the strength of associations for theory-based and data-driven biomarkers. Combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R 2 = 0.241, P < 0.001. Overall, these results demonstrate that out-of-sample associations between chronic motor outcomes and data-driven imaging features, particularly when lesion data is represented in terms of structural disconnection, are stronger than associations between chronic motor outcomes and theory-based biomarkers. However, combining both theory-based and data-driven models provides the most robust associations.

Greater severity of depressive symptoms is associated with changes to perceived sweating, preferred ambient temperature, and warmth-seeking behavior.

The thermosensory system is relevant to both the conceptualization and treatment of depression. There is evidence that depression is associated with changes in thermoregulatory functioning, and that thermosensory pathways can be recruited to influence affect and reduce depressive symptoms. In this study, we investigated the relationship between severity of depressive symptoms and changes to measures of subjective experiences associated with thermoregulatory processes as well as the relationship between severity of depressive symptoms and affective responses to warm stimuli, specifically frequency of warmth-seeking behavior. Participants (N = 529) completed measures of depressive symptoms, subjective experiences associated with thermoregulatory processes (i.e., perceived sweating and preferred ambient temperature) and frequency of warmth-seeking behavior (e.g., long hot baths, saunas, etc.). We demonstrate that, controlling for age and gender, greater severity of depressive symptoms is associated with greater perceived sweating and lower preferred ambient temperature. Furthermore, we demonstrate that greater severity of depressive symptoms is associated with more frequent warmth-seeking behavior, and that something other than thermal preference (i.e., stated preference for warmer temperature) is driving this behavior. These data highlight the importance of incorporating the thermoregulatory system in our conceptualization of the pathophysiology of depression and support the potential to recruit thermosensory pathways to target depressive symptoms.

Apollo Next Generation Sample Analysis (ANGSA): an Apollo Participating Scientist Program to Prepare the Lunar Sample Community for Artemis.

As a first step in preparing for the return of samples from the Moon by the Artemis Program, NASA initiated the Apollo Next Generation Sample Analysis Program (ANGSA). ANGSA was designed to function as a low-cost sample return mission and involved the curation and analysis of samples previously returned by the Apollo 17 mission that remained unopened or stored under unique conditions for 50 years. These samples include the lower portion of a double drive tube previously sealed on the lunar surface, the upper portion of that drive tube that had remained unopened, and a variety of Apollo 17 samples that had remained stored at -27 °C for approximately 50 years. ANGSA constitutes the first preliminary examination phase of a lunar "sample return mission" in over 50 years. It also mimics that same phase of an Artemis surface exploration mission, its design included placing samples within the context of local and regional geology through new orbital observations collected since Apollo and additional new "boots-on-the-ground" observations, data synthesis, and interpretations provided by Apollo 17 astronaut Harrison Schmitt. ANGSA used new curation techniques to prepare, document, and allocate these new lunar samples, developed new tools to open and extract gases from their containers, and applied new analytical instrumentation previously unavailable during the Apollo Program to reveal new information about these samples. Most of the 90 scientists, engineers, and curators involved in this mission were not alive during the Apollo Program, and it had been 30 years since the last Apollo core sample was processed in the Apollo curation facility at NASA JSC. There are many firsts associated with ANGSA that have direct relevance to Artemis. ANGSA is the first to open a core sample previously sealed on the surface of the Moon, the first to extract and analyze lunar gases collected in situ, the first to examine a core that penetrated a lunar landslide deposit, and the first to process pristine Apollo samples in a glovebox at -20 °C. All the ANGSA activities have helped to prepare the Artemis generation for what is to come. The timing of this program, the composition of the team, and the preservation of unopened Apollo samples facilitated this generational handoff from Apollo to Artemis that sets up Artemis and the lunar sample science community for additional successes.

Transcallosal white matter and cortical gray matter variations in autistic adults ages 30-73 years: A bitensor free water imaging approach.

Background: Autism spectrum disorder (ASD) has long been recognized as a lifelong condition, but brain aging studies in autistic adults aged >30 years are limited. Free water, a novel brain imaging marker derived from diffusion MRI (dMRI), has shown promise in differentiating typical and pathological aging and monitoring brain degeneration. We aimed to examine free water and free water corrected dMRI measures to assess white and gray matter microstructure and their associations with age in autistic adults. Methods: Forty-three autistic adults ages 30-73 years and 43 age, sex, and IQ matched neurotypical controls participated in this cross-sectional study. We quantified fractional anisotropy (FA), free water, and free water-corrected FA (fwcFA) across 32 transcallosal white matter tracts and 94 gray matter areas in autistic adults and neurotypical controls. Follow-up analyses assessed age effect on dMRI metrics of the whole brain for both groups and the relationship between dMRI metrics and clinical measures of ASD in regions that significantly differentiated autistic adults from controls. Results: We found globally elevated free water in 24 transcallosal tracts in autistic adults. We identified negligible differences in dMRI metrics in gray matter between the two groups. Age-associated FA reductions and free water increases were featured in neurotypical controls; however, this brain aging profile was largely absent in autistic adults. Additionally, greater autism quotient (AQ) total raw score was associated with increased free water in the inferior frontal gyrus pars orbitalis and lateral orbital gyrus in autistic adults. Limitations: All autistic adults were cognitively capable individuals, minimizing the generalizability of the research findings across the spectrum. This study also involved a cross-sectional design, which limited inferences about the longitudinal microstructural changes of white and gray matter in ASD. Conclusions: We identified differential microstructural configurations between white and gray matter in autistic adults and that autistic individuals present more heterogeneous brain aging profiles compared to controls. Our clinical correlation analysis offered new evidence that elevated free water in some localized white matter tracts may critically contribute to autistic traits in ASD. Our findings underscored the importance of quantifying free water in dMRI studies of ASD.

Gadolinium Enhanced T2 FLAIR is an Imaging Biomarker of Radiation Necrosis and Tumor Progression in Patients with Brain Metastases.

BACKGROUND AND PURPOSE: Differentiating radiation necrosis (RN) from tumor progression (TP) after radiotherapy for brain metastases is an important clinical problem requiring advanced imaging techniques which may not be widely available and are challenging to perform at multiple time points. The ability to leverage conventional MRI for this problem, could have meaningful clinical impact. The purpose of this study was to explore contrast enhanced T2 FLAIR (T2FLAIRc) as a new imaging biomarker of RN and TP. MATERIALS AND METHODS: This single-institution retrospective study included patients with treated brain metastases undergoing DSC-MRI between January 2021 and June 2023. Reference standard assessment was based on histopathology or serial follow-up including results of DSC-MRI for a minimum of 6 months from the first DSC-MRI. The index test was implemented as part of the institutional brain tumor MRI protocol and preceded the first DSC-MRI. T2FLAIRc and gadolinium enhanced T1 MPRAGE (T1c) signal were normalized against normal brain parenchyma and expressed as z-score. Mean signal intensity of enhancing disease for RN and TP groups were compared using unpaired t-test. Receiver operator characteristic (ROC) curves and area under the ROC curve (AUC) were derived by bootstrapping. DeLong test was used to compare AUC. RESULTS: 56 participants (mean age, 62 years +/-12.7 [SD]; 39 females); 28 RN, 28 TP were evaluated. The index MRI was performed on average 73 days +/-34 [SD] before the first DSC-MRI. Significantly higher z-scores were found for RN using T2FLAIRc (8.3 versus 5.8, p<0.001) and T1c (4.1 versus 3.5, p=0.02). AUC for T2FLAIRc (0.83, 95% CI, 0.72-0.92) was greater than T1c (0.70, 95% CI, 0.560.83) (p = 0.04). The AUC of DSC derived rCBV (0.82, 95% CI, 0.70-0.93) was not significantly different from T2FLAIRc (p = 0.9). CONCLUSIONS: A higher normalized T1c and T2FLAIRc signal intensity was found for RN. In a univariable test, mean T2FLAIRc signal intensity of enhancing voxels showed good discrimination performance for distinguishing RN from TP. The results of this work demonstrate the potential of T2FLAIRc as an imaging biomarker in the work-up of RN in patients with brain metastases. ABBREVIATIONS: AUC = area under the receiver operating characteristic curve; RN = radiation necrosis; ROC = receiver operating characteristic; SRS = stereotactic radiosurgery; T1c = contrast enhanced T1; T2FLAIRc = contrast enhanced T2 FLAIR; TP = tumor progression.

Gemogenovatucel-T (Vigil): bi-shRNA plasmid-based targeted immunotherapy.

We describe in this review the historical evidence leading up to the concept and design of Vigil and subsequent clinical applications including safety and efficacy in a randomized, controlled Phase IIB trial. Vigil (gemogenovatucel-T) is a unique triple function targeted immunotherapy that demonstrates preclinical and clinical systemic anticancer activity. Construction of Vigil involves harvest of autologous malignant tissue for neoantigen targeting (ideally containing clonal neoantigens) followed by a two-day process involving transfection with a plasmid to provide a permissive 'training environment' for the patient's immune system. Transfected plasmid components contain an expressive human GMCSF DNA segment to enhance anticancer immune functional response and a second component expressing bi-shRNAfurin which reduces TGFß isomers (TGFß1 and TGFß2) thereby reducing cancer inhibition of the targeted immune response. Results generated to date justify advancement to confirmatory clinical trials supporting product regulatory approval.

Vigil is an anticancer treatment that employs three methods of enhancing the body's immune system to identify and kill cancer cells. The construction of Vigil involves cancer cells from the same person being treated (personalized therapy) in combination with added anticancer genetic signals to enhance the number and function anti-anticancer immune cells and to guide the immune cells to the cancer and not to normal organs of the body. In this manner, an army of immune cells are created that can move to attacking the cancer using blood vessels to get to the cancer anywhere it tries to grow in the body. One study (Phase I) performed with this product to determine safety and dose range demonstrated an optimal dose and schedule. Another study (Phase IIA) showed initial clinical benefit. A third more complex study (Phase IIB) in patients treated with Vigil compared with standard of care without Vigil demonstrated the ability to prolong the patients life and time without their cancer getting worse without any significant side effects associated with the treatment in a unique subset of ovarian cancer patients, those with the ability to repair their DNA. Based on the composite of these results, Vigil is an attractive targeted immunotherapy justified for late-stage clinical testing.

Effect of Diabetes on Microvascular Morphology and Permeability of Rat Skeletal Muscle: In Vivo Imaging Using Two-Photon Laser Scanning Microscopy.

This investigation evaluated the microvascular permeability and ultrastructure of skeletal muscle capillaries in skeletal muscle of diabetic (DIA) rats using two-photon laser scanning microscopy (TPLSM) and transmission electron microscopy (TEM). Microvascular permeability was assessed in the tibialis anterior muscle of control (CON) and DIA (streptozocin) male Wistar rats (n = 20, 10-14 wk) by in vivo imaging using TPLSM after fluorescent dye intravenous infusion. Fluorescent dye leakage was quantified to determine microvascular permeability. The ultrastructure was imaged by TEM ex vivo to calculate the size and number of intercellular clefts between capillary endothelial cells and also intracellular vesicles. Compared with control, the volumetrically determined interstitial fluorescent dye leakage, the endothelial cell thickness, and the number of intercellular clefts per capillary perimeter were significantly higher, and the cleft width was significantly narrower in TA of DIA (interstitial fluorescent dye leakage, 2.88 ± 1.40 vs. 10.95 ± 1.41 µm3 x min x 106; endothelial thickness 0.28 ± 0.02 vs. 0.45 ± 0.03 µm; number of intercellular clefts per capillary perimeter 6.3 ± 0.80 vs. 13.6 ± 1.7 /100 µm; cleft width 11.92 ± 0.95 vs. 8.40 ± 1.03 nm, CON vs. DIA respectively, all p <0.05). The size of intracellular vesicles in the vascular endothelium showed an increased proportion of large vesicles in the DIA group compared to the CON group (p < 0.05). Diabetes mellitus enhances the microvascular permeability of skeletal muscle microvessels, due, in part, to a higher density and narrowing of the endothelial intercellular clefts, and larger intracellular vesicles.

Magnetically Guided Microcatheter for Targeted Injection of Magnetic Particle Swarms.

The initial delivery of small-scale magnetic devices such as microrobots is a key, but often overlooked, aspect for their use in clinical applications. The deployment of these devices within the dynamic environment of the human body presents significant challenges due to their dispersion caused by circulatory flows. Here, a method is introduced to effectively deliver a swarm of magnetic nanoparticles in fluidic flows. This approach integrates a magnetically navigated robotic microcatheter equipped with a reservoir for storing the magnetic nanoparticles. The microfluidic flow within the reservoir facilitates the injection of magnetic nanoparticles into the fluid stream, and a magnetic field gradient guides the swarm through the oscillatory flow to a target site. The microcatheter and reservoir are engineered to enable magnetic steering and injection of the magnetic nanoparticles. To demonstrate this approach, experiments are conducted utilizing a spinal cord phantom simulating intrathecal catheter delivery for applications in the central nervous system. These results demonstrate that the proposed microcatheter successfully concentrates nanoparticles near the desired location through the precise manipulation of magnetic field gradients, offering a promising solution for the controlled deployment of untethered magnetic micro-/nanodevices within the complex physiological circulatory systems of the human body.