Voorhees College

As we head into 2026, artificial intelligence (AI), blockchain, and other emerging technologies are moving from experiments into core healthcare systems. That shift promises tangible benefits: fewer people left untreated, faster discovery of lifesaving treatments, and simpler, lower‑cost ways to move money and data across borders. It also brings real risks-speculative hype, erosion of institutional trust, and rushed rollouts that fail patients-so adoption must be disciplined and values-driven. This annual predictions article, informed by ConV2X Symposium speakers, highlights practical advances likely to matter at the bedside and beyond: programmable stablecoins that lower cross‑border payment friction; AI that surfaces pediatric risks earlier; verifiable digital credentials that ease clinician mobility; post‑quantum cryptography to safeguard sensitive records; domain‑specific AI designed for regulatory compliance; consumer apps that put usable health tools in people's pockets; and the rise of Decentralized Science (DeSci) to restore transparency and funding momentum to stalled research. Realizing these possibilities will require deliberate choices, commitment, and coordinated stewardship across innovators, clinicians, and policymakers. With that effort, these tools can help build a more verifiable, equitable, and resilient global healthcare system-technology shaped to serve people, not the other way around; aspirations for healing, dignity, and universal well-being. While uncertainties persist, the path forward is clear: responsible innovation today will shape a healthier, more inclusive tomorrow.

Many patients are taking psychotropic medications concomitantly with repetitive transcranial magnetic stimulation (rTMS), the latter of which is indicated for the treatment of moderate and severe depressive episodes that have not responded to first-line pharmacotherapy. While preclinical evidence suggests that psychotropic drugs can generally affect rTMS, the specific effect on the clinical response of rTMS for depression is not fully clear. A systematic search of all papers published prior to January 2023 in PubMed, APA, PsycInfo, and Scopus was conducted to identify clinical studies that examine the effects of different psychotropic medications on clinical outcomes in patients undergoing rTMS for depression. A total of 10 articles were identified and extracted for inclusion. This review outlines the results from 10 clinical studies and summarizes the current state of the literature describing rTMS outcomes with concomitant use of several groups of psychotropic agents, including antipsychotics, mood stabilizers, psychostimulants, and benzodiazepines. Antidepressants were excluded from this review due to the evidence base that already exists describing their efficacy with and without rTMS.

In this paper, the concept of pairwise fuzzy Baire dense sets in fuzzy bitopological spaces is introduced by means of pairwise fuzzy first category sets. Several characterizations of pairwise fuzzy Baire dense sets are established. The conditions for fuzzy bitopological spaces to become pairwise fuzzy Baire spaces, pairwise fuzzy second category spaces and pairwise fuzzy resolvable space, are obtained.

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat) is one of the hallmark of biological tools, contemplated as a valid and hopeful alternatives to genome editing. Advancements in CRISPR-based technologies have empowered scientists with an editing kit that allows them to employ their knowledge for deleting, replacing and lately “Gene Surgery”, and provides unique control over genes in broad range of species, and presumably in humans. These fast-growing technologies have high strength and flexibility and are becoming an adaptable tool with implementations that are altering organism’s genome and easily used for chromatin manipulation. In addition to the popularity of CRISPR in genome engineering and modern biology, this major tool authorizes breakthrough discoveries and methodological advancements in science. As scientists are developing new types of experiments, some of the applications are raising questions about what CRISPR can enable. The results of evidence-based research strongly suggest that CRISPR is becoming a practical tool for genome-engineering and to create genetically modified eukaryotes, which is needed to establish guidelines on new regulatory concerns for scientific communities.

We first introduce several sphere packing ways such as simple cubic packing (SC), face-centered cubic packing (FCC), body-centered cubic packing (BCC), and rectangular body-centered cuboid packing (recBCC), where the rectangular body-centered cuboid packing means the packing method based on a rectangular cuboid whose base is square and whose height is times the length of one side of its square base such that the congruent spheres are centered at the 8 vertices and the centroid of the cuboid. The corresponding lattices are denoted as SCL, FCCL, BCCL, and recBCCL, respectively. Then we consider properties of those lattices, and show that FCCL and recBCCL are the same. Finally we point out some possible applications of the recBCC lattices.

Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data assimilation framework using Sequential Monte Carlo (SMC) methods for wildfire spread simulations. The models and algorithms of the framework are described, and experimental results are provided. This work demonstrates the feasibility of applying SMC methods to data assimilation of wildfire spread simulations. The developed framework can potentially be generalized to other application areas where sophisticated simulation models are used.