The University of Texas at Brownsville (abbreviated as UTB and formerly known as the University of Texas at Brownsville and Texas Southmost College [UTB/TSC]) was an educational institution located in Brownsville, Texas. The university was on the land once occupied by Fort Brown. It was a member of the University of Texas System. The institution was formed from a 1991 partnership between the two-year Texas Southmost College and University of Texas-Pan American at Brownsville. The partnership ended in 2011 as UTB became a standalone University of Texas institution, and Texas Southmost College returned to being an independent community college. UTB itself offered baccalaureate and graduate degrees in liberal arts, sciences, education, business, and professional programs.In 2015, the UT Brownsville merged with UT–Pan American, to form The University of Texas Rio Grande Valley.
Background:Inflammatory breast cancer (IBC) is a rare and highly aggressive form of breast cancer with an increased propensity to metastasize to distant organs including the brain. Higher serum levels of the calcium-binding proteins S100A8/A9, particularly of S100A9, have emerged as a clinically and biologically significant factor in aggressive breast cancers that are associated with poorer prognosis, tumor progression, and resistance to therapy. However, its contribution in IBC specifically remains undefined. Here, we investigated whether serum levels of S100A8/A9 predict outcomes in patients with IBC. Methods:Serum S100A8/A9 levels were measured in a cohort of 304 IBC patients using ELISA assay. S100A8/A9 levels were categorized by their third quartile value (S100A8/A9-low ≤ 3rd quartile; S100A8/A9-high > 3rd quartile). Overall survival (OS) and breast cancer-specific survival (BCSS) were analyzed with Kaplan-Meier curves, log-rank tests, and Cox proportional hazard regression models. The cumulative incidence of any metastases and the cumulative incidence of brain metastases were analyzed using Aalen-Johansen method, Gray test, and Fine-Gray models. Results:The median follow-up time was 64 months. Forty-six percent of patients had estrogen receptor (ER)-negative tumors, 61.3% were stage III-IV, 77% high grade, 16.8% received adjuvant chemotherapy and 53.6% received adjuvant radiation. On univariate analysis, S100A8/A9 levels, disease stage, ER status, PR status, HER2 status, adjuvant chemotherapy, and adjuvant radiation therapy were significantly associated with OS and BCSS. Patients with high S100A8/A9 serum levels had poor OS (P=0.01) and BCSS (P=0.007) and had a higher risk of developing brain metastasis (P=0.01) but not other metastasis. On multivariate analysis, high S100A8/A9 serum levels were independently associated with reduced OS (hazard ratio [HR]=1.7, 95% CI 1.1 to 2.6, P=0.01), reduced BCSS (HR=1.8, 95% CI 1.2 to 2.8, P=0.006), and increased cumulative incidence of developing brain metastasis (subdistribution hazard ratio (sHR)=1.8, 95% CI 1.1 to 3.0, P=0.03). Conclusions:In patients with IBC, high serum levels of S100A8/A9 are an independent prognostic factor for brain metastasis and poor clinical outcomes. These findings support the potential of S100A8/A9 as predictive biomarker for identifying increased risk of brain metastasis and unfavorable prognosis in patients with IBC.
Generative AI models, for example diffusion models, have emerged as state-of-the-art methods for generating novel images described by a text prompt. Open-source AI models can furthermore be fine-tuned to produce images similar to a given dataset of images. However, bad actors may seek to use illegal images to fine-tune a model so that it produces inappropriate and harmful images. We investigate various methods for detecting whether such images have been used to fine-tune a given diffusion model. This task raises two key challenges: (1) Images from a suspicious model should not be produced. (2) Any prompts yielding inappropriate images may be obfuscated. We propose a multi-layered framework to overcome these challenges. We combine embedding analysis, trajectory classification, parameter inspections, and neural network encoding in a promising framework, and suggest that controlled experiments should be conducted to test this strategy in future work.
INTRODUCTION:Little research on the association of neighborhood environment with physical activity in resource-poor communities has been done. This study assessed changes in perceptions of the neighborhood environment and the association between those perceptions and physical activity in Mexican Americans on the Texas-Mexico border in an area where there would be community efforts to enhance pedestrian and cycling infrastructure and programming. METHODS:We analyzed data from a population-based cohort of Mexican American individuals on the Texas-Mexico border. From 2008 to 2018, interviewer-administered questionnaires were used to collect perceptions of neighborhood environment and physical activity at baseline, 5- and 10-year follow-ups, and at other ancillary study visits, with an average of 3 data points per participant. We conducted multivariable longitudinal logistic regression analyses to assess if the changes in odds of positive perceptions of the neighborhood environment over the study years differed by physical activity patterns. RESULTS:The sample (n = 1036) was mostly female (71%), born in Mexico (70%), and had no health insurance (69%). We saw improvements in the perceptions of several neighborhood environment attributes from 2008 to 2018, though we saw different longitudinal trajectories in these perceptions based on an individual's longitudinal physical activity patterns. By 2014-2018, we saw significantly higher positive perceptions of the neighborhood environment for those who consistently met physical activity guidelines compared with those who did not (adjusted rate ratio = 1.12, P = .049). DISCUSSION:We found that perceptions of many neighborhood environment attributes improved between 2008 and 2018, and that overall positive perceptions were associated with consistently meeting physical activity guidelines over time.
Similar to other brain regions, the neurons in the lateral septum (LS) are of heterogeneous populations. However, their resting membrane potential (RMP) on average is not too far apart. Cells were characterized based on biological markers by using brain slices, as under these in vitro conditions, neurons retain their morphologies. Since the LS neurons are not spontaneously excitable at RMP, the action potentials (APs) were evoked via injections of currents of moderate magnitude during the patch-clamp recordings. In coronal brain slices of rats, a smaller portion of neurons generated a train of APs of complex nature. In order to define the types of neurons with similar phenotypes, we subsequently used the four lines of td-Tomato transgenic mice. The brains of these mice express the promoter fluorophore td-Tomato and enhanced green fluorescent protein (eGFP). Therefore, recordings were conducted in a targeted manner in neurons expressing glutamic acid decarboxylase (GAD), parvalbumin (PV), somatostatin (SOM), or vasoactive intestinal polypeptide (VIP). Similar spike phenotypes that we refer to as type III, in order to distinguish from AP in principal cells - type I and those in interneurons - type II, also exist in mice, substantiating a similitude among rodents. The type III AP is selectively triggered by Ca2+ in GAD and SOM-positive neurons. Conclusions are supported by established pharmacologic tools, nimodipine, TTX, and ZD7288, a selective HCN channel antagonist. Collectively, these observations revitalize our knowledge from pioneering studies with regard to the brain of mammals in general and septal structures in particular.
Voltage-dependent K + (Kv) channels are diverse, comprising the classical Shab − Kv2, Shaker − Kv1, Shal − Kv4, and Shaw − Kv3 families. The Shaker family alone consists of Kv1.1, Kv1.2, Kv1.3, Kv1.4, Kv1.5, Kv1.6, and Kv1.7. Moreover, the Shab family comprises two functional (Kv2.1 and Kv2.2) and several “silent” alpha subunits (Kv2.3, Kv5, Kv6, Kv8, and Kv9), which do not generate K current. However, e.g., Kv8.1, via heteromerization, inhibits outward currents of the same family or even that of Shaw . This property of Kv8.1 is similar to those of designated beta subunits or non-selective auxiliary elements, including ADAM or AMIGO proteins. Kv channels and, in turn, ADAM may modulate the synaptic long-term potentiation (LTP). Prevailingly, Kv1.1 and Kv1.5 are attributed to respective brain and heart pathologies, some of which may occur simultaneously. The aforementioned channel proteins are apparently involved in several brain pathologies, including schizophrenia and seizures.
