Enhancing brain tumor surgery precision with multimodal connectome imaging: Structural and functional connectivity in language-dominant areas.

Objectives: Language is a critical aspect of human cognition and function, and its preservation is a priority for neurosurgical interventions in the left frontal operculum. However, identification of language areas can be inconsistent, even with electrical mapping. The use of multimodal structural and functional neuroimaging in conjunction with intraoperative neuromonitoring may augment cortical language area identification to guide the resection of left frontal opercular lesions.

Global transcriptome profiling of ST09 treated breast cancer cells identifies miR-197-5p/GPX3 antioxidant axis as a regulator of tumorigenesis.

ROS (Reactive Oxygen Species) has a dual role in tumorigenesis. Some cancers have high ROS conditions, and others have low ROS. TNBC thrives on high ROS compared to other Breast Cancer subtypes.

A high-throughput, microplate reader-based method to monitor in vitro HIV latency reversal in the absence of flow cytometry.

J-Lat cells are derivatives of the Jurkat CD4 T cell line that contain a non-infectious, inducible HIV provirus with a GFP tag. While these cells have substantially advanced our understanding of HIV latency, their use by many laboratories in low and middle-income countries is restricted by limited access to flow cytometry. To overcome this barrier, we describe a modified J-Lat assay using a standard microplate reader that detects HIV-GFP expression following treatment with latency-reversing agents (LRAs).

Impact of climate change and land management on nitrate pollution in the high plains aquifer.

High concentrations of nitrate in groundwater pose risks to human and environmental health. This study evaluates the potential impact of climate change, land use, and fertilizer application rates on groundwater nitrate levels in the High Plains Aquifer under four Shared Socioeconomic Pathway (SSP) scenarios. A random forest model, with predictors such as fertilizer application rates, cropland coverage, and climate variables from six Coupled Model Intercomparison Project models, is used to project future nitrate concentrations.

Osseointegration for transfemoral amputees: Influence of femur length and implant sizing on bone-implant contact and alignment.

Introduction: Clinical data on osseointegration (OI) for limb replacement indicates a concerning increase in mechanical complications after five years post-implantation. Since adequate bone-implant contact and proper implant alignment are critical factors for successful osseointegration, it is essential to identify the factors influencing these outcomes. This study aimed to assess the effects of residual femur length and implant sizing on bone-implant contact and implant alignment.

Syphilis-positive and false-positive trends among US blood donors, 2013-2023.

Background: US blood donors are tested for syphilis because the bacterial agent is transfusion transmissible. Here we describe trends over an 11-year period of donations positive for recent and past syphilis infections, and donations classified as syphilis false positive (FP).

Methods: Data from January 1, 2013, to December 31, 2023 (11 years) were compiled for all American Red Cross blood donations to evaluate demographics/characteristics and longitudinal trends in donors testing syphilis reactive/positive.

Bioaccumulation of perfluorooctane sulfonic acid (PFOS) and oxidative stress in snapping turtles in New York, USA.

Perfluorooctane sulfonic acid (PFOS) is an anthropogenic chemical found in aqueous film-forming foams (AFFFs) and many consumer products. Despite its environmental ubiquity and persistence, little is known about the effects of PFOS on stress levels in wild animals. Here, we examined PFOS bioaccumulation and correlations between PFOS exposure and oxidative stress in snapping turtles (Chelydra serpentina) downstream of Griffiss Air Force Base in Rome, New York, a known source of AFFF contamination.

Validation of UniverSeg for Interventional Abdominal Angiographic Segmentation.

Automatic segmentation of angiographic structures can aid in assessing vascular disease. While recent deep learning models promise automation, they lack validation on interventional angiographic data. This study investigates the feasibility of angiographic segmentation using in-context learning with the UniverSeg model, which is a cross-learning segmentation model that lacks inherent angiographic training.

Assessing online chat-based artificial intelligence models for weight loss recommendation appropriateness and bias in the presence of guideline incongruence.

Background And Aim: Managing obesity requires a comprehensive approach that involves therapeutic lifestyle changes, medications, or metabolic surgery. Many patients seek health information from online sources and artificial intelligence models like ChatGPT, Google Gemini, and Microsoft Copilot before consulting health professionals. This study aims to evaluate the appropriateness of the responses of Google Gemini and Microsoft Copilot to questions on pharmacologic and surgical management of obesity and assess for bias in their responses to either the ADA or AACE guidelines.

Green synthesis of silica-coated gold nanoparticles employing femtosecond laser, solid targets, and water.

Gold nanoparticles are widely used in biomedical applications due to their unique properties. However, traditional synthesis methods generate contaminants that cause cytotoxicity and compromise the biocompatibility of the nanomaterials. Therefore, green synthesis methods are essential to produce pure and biocompatible nanoparticles, ensuring their effectiveness in biomedical applications.

The influence of lightning on insect and fungal dynamics in a lowland tropical forest.

Lightning strikes are a common source of disturbance in tropical forests, and a typical strike generates large quantities of dead wood. Lightning-damaged trees are a consistent resource for tropical saproxylic (i.e.

Cryogenic in-memory computing using magnetic topological insulators.

Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.

Selective use of ferumoxytol-enhanced magnetic resonance angiography in patients with renal insufficiency: insights from a pilot study.

The use of conventional contrast agents in computed tomography (CT) and magnetic resonance (MR) imaging is often limited in patients with chronic kidney disease (CKD) due to potential nephrotoxicity. Ferumoxytol, originally developed for iron supplementation, has emerged as a promising alternative MR contrast agent that is safer for patients with CKD. This study aims to present our center's experience with ferumoxytol as a contrast agent in CKD patients.

Sustaining the planet by sustaining ourselves.

The author transitions his career in oncology to one in planetary health. The career pivot begins after he recognizes similarities between the pandemic and the climate crisis. The author determines that stepping away from his role as chair of radiology for a one-year sabbatical is the most efficient way to learn about sustainability.

Extensive benchmarking of a method that estimates external model performance from limited statistical characteristics.

Predictive model performance may deteriorate when applied to data sources that were not used for training, thus, external validation is a key step in successful model deployment. As access to patient-level external data sources is typically limited, we recently proposed a method that estimates external model performance using only external summary statistics. Here, we benchmark the proposed method on multiple tasks using five large heterogeneous US data sources, where each, in turn, plays the role of an internal source and the remaining-external.

The Sub 2-h Official Marathon is Possible: Developing a Drafting Strategy for a Historic Breakthrough in Sports.

Background: Drafting for drag reduction is a tactic commonly employed by elite athletes of various sports. The strategy has been adopted by Kenyan runner Eliud Kipchoge on numerous marathon events in the past, including the 2018 and 2022 editions of the Berlin marathon (where Kipchoge set two official world records), as well as in two special attempts to break the 2 h mark for the distance, the Nike Breaking2 (2017) and the INEOS 1:59 Challenge (2019), where Kipchoge used an improved drafting formation to finish in 1:59:40, although that is not recognized as an official record.

Results: In this study, the drag of a realistic model of a male runner is calculated by computational fluid dynamics for a range of velocities.

Comparison of software-assisted and freehand methods of rotational assessment for diaphyseal tibia fractures.

Objective: Accurate rotational reduction following tibial shaft fracture fixation is absent in up to 36% of cases yet may be critical for lower extremity biomechanics. The objective of this cadaveric study was to compare the results of freehand methods of reduction with software-assisted reduction.

Methods: Four fellowship-trained orthopaedic trauma surgeons attempted rotational correction in a cadaveric model with fluoroscopic assistance (without radiographic visualization of the fracture site) using (1) their method of choice (MoC) and (2) software assistance (SA).

Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms.

Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training.