Citations

Background/Objectives: Stereotactic body radiation therapy (SBRT) for skull base reirradiation is particularly challenging, as patients have already received substantial radiation doses to the region, and nearby normal organs may have approached their tolerance limit from prior treatments. In this study, we reviewed the characteristics and capabilities of four advanced external beam radiation delivery systems and four modern treatment planning systems and evaluated the treatment plan quality of each technique using skull base reirradiation patient cases. Methods: SBRT plans were generated for sixteen skull base reirradiation patients using four modalities: the GK plan for the Elekta Leksell Gamma Knife Perfexion/ICON, the CyberKnife (CK) plan for the Accuray CyberKnife, the intensity-modulated proton therapy (IMPT) plan for the Hitachi ProBeat-FR proton therapy machine, and the volumetric-modulated arc therapy (VMAT) plan for the Varian TrueBeam STx. These plans were evaluated and compared using two novel gradient indices in addition to traditional dosimetry metrics for targets and organs at risk (OARs). The steepest border gradient quantified the percent prescription dose fall-off per millimeter at the boundary between the target and adjacent critical structures. This gradient index highlighted the system’s ability to spare nearby critical OARs. The volume gradient assessed the extent of dose spread outside the target toward the patient’s body. Results: All plans achieved comparable target coverage and conformity, while IMPT and VMAT demonstrated significantly better uniformity. The GK plans exhibited the highest border gradient, up to 20.9%/mm, followed by small-spot-size IMPT plans and CK plans. Additionally, IMPT plans showed the benefit of reduced dose spread in low-dose regions and the lowest maximum and mean doses to the brainstem and carotid artery. Conclusions: The advanced external beam radiotherapy modalities evaluated in this study are well-suited for SBRT in skull base reirradiation, which demands precise targeting of tumors with highly conformal doses and steep dose gradients to protect nearby normal structures.

ABSTRACT Symmetry refers to properties that remain invariant upon mathematical transformations. The principles of symmetry have guided numerous important discoveries in physics and chemistry but not in biology and medicine. Here, we aim to explore the presence of symmetry relationships at the gene expression level as a mean to distinguish between healthy and disease states. We deployed Learning-Based Invariant Feature Engineering - LIFE, a hybrid machine learning approach implemented with two symmetric invariant feature functions (IFFs) to identify Invariant Feature Genes (IFGs), which are gene pairs whose IFF single-value outputs remain invariant across individual samples in a given biological phenotype. Our multiclass classification results across the transcriptomes of 25 normal organs, 25 cancer types, and blood samples obtained from 4 different types of neurodegenerative diseases revealed the presence of unique phenotype-specific IFGs. We constructed networks using these IFGs (IF-Nets) and intriguingly, we demonstrated that the hubs could serve as information encoders, capable of reconstructing sample-wise expression values in relation to their counterpart genes. More importantly, we found that hubs of cancer IF-Nets were enriched with both approved and clinical trial drugs, highlighting “symmetry breaking” as a novel approach for treating diseases.

AbstractBackgroundNormative models (NM) of brain metrics based on large, diverse populations offer novel strategies to detect individual brain abnormalities. To create an age‐dependent statistical model of brain microstructure over the human lifespan, we built the largest multi‐site NM of white matter (WM) diffusion tensor imaging (DTI) metrics based on 54,591 subjects. We used state‐of‐the‐art tools to adjust for site‐dependent effects.MethodWe used Hierarchical Bayesian Regression (HBR) to determine the age trajectory and lifespan centile curves (ages 3‐100 years) by merging data from different sections of the lifespan. We analyzed 19 international public datasets with the ENIGMA‐DTI protocol. Mean fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AxD, RD) were extracted for 21 bilateral ROIs from the JHU‐WM atlas and the whole WM skeleton. Regressions were run with each metric per ROI as a function of age and sex, and the DTI protocol as the batch effect (37 protocols). Z‐scores were derived for 81 patients with Alzheimer’s disease (AD; mean age: 77y±8.4, 46M/35F) and 225 MCI participants (mean age: 75.1y±8.1, 127M/98F) and extreme deviations (Z>|2|) were quantified for each condition. ROI‐wise areas under the ROC curve were calculated to determine the classification accuracy of the Z‐scores.ResultThe average FA for the overall WM skeleton reached its peak value at 27 years, with minima for MD, RD and AxD at 47, 42 and 54 years, respectively. We found extreme FA deviations (Z>2) for MCI (AUC=0.67) and AD (AUC=0.66) in the genu of the corpus callosum, extreme RD deviations (Z<2) in the tapetum (AUC=0.73) and Extreme AxD deviations (Z>2) for MCI in the superior longitudinal fasciculus (AUC=0.65). See Figures 1 & 2.ConclusionNM of brain microstructure with HBR yielded lifespan trajectories for widely‐used DTI metrics. By pooling large scale multi‐site dMRI to create a statistical reference, single subject deviations are detectable across the lifespan for AD and MCI. These NM will be publicly available to the research community.

AbstractBackgroundNormative models (NM) of brain metrics based on large, diverse populations offer novel strategies to detect individual brain abnormalities. To create an age‐dependent statistical model of brain microstructure over the human lifespan, we built the largest multi‐site NM of white matter (WM) diffusion tensor imaging (DTI) metrics based on 54,591 subjects. We used state‐of‐the‐art tools to adjust for site‐dependent effects.MethodWe used Hierarchical Bayesian Regression (HBR) to determine the age trajectory and lifespan centile curves (ages 3‐100 years) by merging data from different sections of the lifespan. We analyzed 19 international public datasets with the ENIGMA‐DTI protocol. Mean fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AxD, RD) were extracted for 21 bilateral ROIs from the JHU‐WM atlas and the whole WM skeleton. Regressions were run with each metric per ROI as a function of age and sex, and the DTI protocol as the batch effect (37 protocols). Z‐scores were derived for 81 patients with Alzheimer’s disease (AD; mean age: 77y±8.4, 46M/35F) and 225 MCI participants (mean age: 75.1y±8.1, 127M/98F) and extreme deviations (Z>|2|) were quantified for each condition. ROI‐wise areas under the ROC curve were calculated to determine the classification accuracy of the Z‐scores.ResultThe average FA for the overall WM skeleton reached its peak value at 27 years, with minima for MD, RD and AxD at 47, 42 and 54 years, respectively. We found extreme FA deviations (Z>2) for MCI (AUC=0.67) and AD (AUC=0.66) in the genu of the corpus callosum, extreme RD deviations (Z<2) in the tapetum (AUC=0.73) and Extreme AxD deviations (Z>2) for MCI in the superior longitudinal fasciculus (AUC=0.65). See Figures 1 & 2.ConclusionNM of brain microstructure with HBR yielded lifespan trajectories for widely‐used DTI metrics. By pooling large scale multi‐site dMRI to create a statistical reference, single subject deviations are detectable across the lifespan for AD and MCI. These NM will be publicly available to the research community.

Background: Very-late-onset myasthenia gravis (VLOMG) refers to myasthenia gravis (MG) with onset at age 65 or older. Current research on VLOMG prognosis remains limited, especially regarding factors influencing outcomes. Objectives: To identify the clinical factors that affect the short- and long-term prognosis of MG patients with an onset age ⩾65 years. Design: This was a single-center, retrospective cohort study of AChR-ab positive VLOMG patients, classified into two subgroups based on age of onset: sub-very-late-onset MG (S-VLOMG, onset age ⩾65 and <75 years), and super-late-onset MG (SLOMG, onset age ⩾75 years). Methods: A total of 93 patients were included, including 75 in the S-VLOMG group and 18 in the SLOMG group. Clinical, therapeutic, and prognosis data were reviewed, and the Cox regression model was used to identify factors influencing short- and long-term prognosis. Results: Patient characteristics were well balanced between the groups. Overall, 49.5% of patients achieved minimal symptom expression (MSE) within 6 months and 86% within 24 months. There was no significant difference between the groups in the proportion achieving MSE at 6 months ( p = 0.635) or 24 months ( p = 0.714). The median time to achieve MSE was also comparable between the S-VLOMG and SLOMG groups (199.0 days vs 280.5 days, p = 0.463). Low baseline MG-ADL score and steroid therapy were associated with better short-term prognosis ( p = 0.007 and p = 0.002, respectively). For long-term prognosis, baseline bulbar and limb involvement, time to treatment initiation, and use of immunosuppressants were significant factors ( p = 0.025, p = 0.004, p = 0.025, and p < 0.0001, respectively). There were no significant differences in side effects or drug withdrawal rates between two groups. Conclusion: This study demonstrated that AChR-ab positive VLOMG patients have a favorable prognosis and responded well to medication, with age and comorbidities showing no significant impact.

Abstract Genetic modifiers are believed to play an important role in the onset and severity of polycystic kidney disease (PKD), but identifying these modifiers has been challenging due to the lack of effective methodologies. In this study, we investigated zebrafish mutants of IFT140 , a newly identified ADPKD gene, and observed phenotypes similar to those seen in mammalian models, including kidney cysts and bone defects. Using efficient microhomology-mediated end joining (MMEJ)-based genome editing technology, we demonstrated that ift140 CRISPRants recapitulate mutant phenotypes while bypassing the early lethality of the mutants to allow for renal cyst analysis in adult fish. In addition to cilia defects, non-cilia phenotypes, such as disrupted cell polarity and aberrant microtubule stabilization in kidney epithelial cells, may also contribute to ift140 -associated cystogenesis. Importantly, the ability to detect ift140 -associated renal cysts with ease allowed us to develop an F0-based genetic screen to identify potential protective modifiers. A pilot screen of sixteen genes previously linked to dysregulated signaling pathways in ADPKD revealed both known and novel modifiers, including mtor and ulk1a . Inhibition of mtor and ulk1a reversed both cilia-related and non-cilia-related abnormalities. In summary, our study underscores the potential of using zebrafish as a model for the efficient discovery of genetic modifiers of kidney cysts.

Metabolic syndrome (MetS) is associated with low-grade inflammation, which can be exacerbated by renal artery stenosis (RAS) and renovascular hypertension, potentially worsening outcomes through pro-inflammatory cytokines. This study investigated whether mesenchymal stem/stromal cells (MSCs) could reduce fat inflammation in pigs with MetS and RAS. Twenty-four pigs were divided into Lean (control), MetS, MetS + RAS, and MetS + RAS + MSCs. In the MSC-treated group, autologous adipose-derived MSCs (107 cells) were injected into the renal artery six weeks after RAS induction. After four weeks, fat volumes and inflammatory markers were assessed. MSC treatment reduced levels of pro-inflammatory cytokines (MCP-1, TNF-a, IL-6) in the renal vein blood and in perirenal fat. The MSCs also decreased fat fibrosis, restored adipocyte size, and altered adipogenesis-related gene expression, particularly in the perirenal fat. These effects were less pronounced in subcutaneous fat. The MSC therapy attenuated fat inflammation and improved metabolic outcomes in pigs with MetS + RAS, suggesting that adipose-derived MSCs may offer a promising therapeutic approach for metabolic disorders.

Abstract Aging is associated with intestinal dysbiosis, a condition characterized by diminished microbial biodiversity and inflammation. This leads to increased vulnerability to extraintestinal manifestations such as autoimmune, metabolic, and neurodegenerative conditions and accelerates mortality. As such, modulation of the gut microbiome is a promising way to extend healthspan. In this study, we explore the effects of fecal microbiota transplant (FMT) from long-living Ames dwarf donors to their normal littermates, and vice versa, on the recipient gut microbiota and liver transcriptome. Importantly, our previous studies highlight differences between the microbiomes of Ames dwarf relative to their normal siblings, potentially contributing to their extended lifespan and healthspan. Our findings demonstrate that FMT from Ames dwarf mice to normal mice significantly alters the recipient’s gut microbiota, reprograms bacterial metabolic functions related to healthy aging, and changes the liver transcriptome, indicating improved metabolic health. Our liver mRNA sequencing and RT-PCR validation reveals that FMT in normal recipient animals administered dwarf donor stool may contribute to the significant downregulation of p21, Elovl3, and Insig2, genes involved with cellular senescence and liver metabolic pathways. Our data suggest a regulatory axis exists between the gut and liver, highlighting the potential of microbiome-targeted therapies in promoting healthy aging. Future research should validate these findings and study the underlying mechanisms that confer geroprotection.