BACKGROUND: Modern warfare operations are volatile, highly complex environments, placing immense physiological, psychological, and cognitive demands on the warfighter. To maximize cognitive performance and warfighter resilience and readiness, training must address psychological stress to enhance performance. Resilience in the face of adversity is fundamentally rooted in an individual\'s psychophysiological stress response and optimized through decreased susceptibility to the negative impact of trauma exposure. The current project aims to optimize warfighter expertise, resilience, adaptability, and performance by utilizing a validated Full Dive Virtual Reality (FDVR) training platform to provide high-fidelity, safe, and scalable, controlled stress exposure in highly realistic simulated training scenarios with the most advanced, immersive technology available.
METHODS: Following Institutional Review Board approval and consent, 2 operators were fitted with high-fidelity virtual reality headsets with hand and eye tracking, full-body haptic feedback suits, a 360° omnidirectional treadmill, and Food and Drug Administration (FDA) cleared biometric monitors. Following acclimation, operators were placed in an industrial fire scenario and instructed to respond as a firefighter and paramedic, to search for and resuscitate any casualties, extinguish the fire, and exfiltrate safely. Following initial acclimation and after each demonstration (n = 2), 3 semistructured interviews asked operators their perceptions and experiences related to FDVR, focusing on usability, feasibility, and safety. Biometric data were continuously recorded using the Caretaker Medical VitalStream.
RESULTS: Proof-of-concept (POC) testing proved that the FDVR training platform is usable, safe, and feasible. It creates an immersive environment with physiological responses to mimic realistic Mass Casualty Events (MCEs). Using a case study approach, transcript data were analyzed using thematic analysis. Three major themes emerged: Sensory deficits reduced realism, but sensory feedback improved fidelity, vestibular discord affected the virtual reality experience but only when the system did not respond naturally to operator movement after acclimation, and movement accommodations were made by operators to enhance usability, especially for fine motor movements. Biometric data analysis correlated timestamps from the VitalStream unit with operator responses to stress-inducing events (i.e., explosions, fires, and a deceased victim). Both operators exhibited significant physiological responses, including elevated heart rate, systolic blood pressure, and mean arterial pressure, particularly following explosions, encountering fire, and encountering the deceased victim within the training environment.
CONCLUSIONS: The FDVR training platform overcomes the obstacles of in-person simulation training and provides the closest to real-life experience available. It will allow warfighters to train with their teams in immersive environments that replicate the conditions in which they are expected to perform their duties. The POC demonstrated that physiological responses can be mapped to scenario events to allow tracking of stress responses, cognitive load, as well as performance, and decision-making of the warfighter. The POC only involved 2 operators, but served to prove that the platform was safe and effective. Future testing plans to include 200 warfighters in operational teams of 10 to 12 to further validate the training effectiveness of the FDVR platform.

OBJECTIVE: This article presents a systematic review of recent advancements in telemedicine architectures for continuous monitoring, providing a comprehensive overview of the evolving software engineering practices underpinning these systems. The review aims to illuminate the critical role of telemedicine in delivering healthcare services, especially during global health crises, and to emphasize the importance of effectiveness, security, interoperability, and scalability in these systems.
METHODS: A systematic review methodology was employed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. As the primary research method, the PubMed, IEEE Xplore, and Scopus databases were searched to identify articles relevant to telemedicine architectures for continuous monitoring. Seventeen articles were selected for analysis, and a methodical approach was employed to investigate and synthesize the findings.
RESULTS: The review identified a notable trend towards the integration of emerging technologies into telemedicine architectures. Key areas of focus include interoperability, security, and scalability. Innovations such as cognitive radio technology, behavior-based control architectures, Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, cloud computing, decentralized systems, and blockchain technology are addressing challenges in remote healthcare delivery and continuous monitoring.
CONCLUSIONS: This review highlights major advancements in telemedicine architectures, emphasizing the integration of advanced technologies to improve interoperability, security, and scalability. The findings underscore the successful application of cognitive radio technology, behavior-based control, HL7 FHIR standards, cloud computing, decentralized systems, and blockchain in advancing remote healthcare delivery.

Long-read sequencing holds great potential for characterizing complex microbial communities, yet taxonomic profiling tools designed specifically for long reads remain lacking. We introduce Melon, a novel marker-based taxonomic profiler that capitalizes on the unique attributes of long reads. Melon employs a two-stage classification scheme to reduce computational time and is equipped with an expectation-maximization-based post-correction module to handle ambiguous reads. Melon achieves superior performance compared to existing tools in both mock and simulated samples. Using wastewater metagenomic samples, we demonstrate the applicability of Melon by showing it provides reliable estimates of overall genome copies, and species-level taxonomic profiles.

BACKGROUND: The utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on human diseases and genetic studies in animals and plants. However, due to the complexity of the linkage relationships between SNP loci and sequencing errors in the reads, the recent methods still cannot yield satisfactory results.
RESULTS: In this study, we present a graph-based algorithm, GCphase, which utilizes the minimum cut algorithm to perform phasing. First, based on alignment between long reads and the reference genome, GCphase filters out ambiguous SNP sites and useless read information. Second, GCphase constructs a graph in which a vertex represents alleles of an SNP locus and each edge represents the presence of read support; moreover, GCphase adopts a graph minimum-cut algorithm to phase the SNPs. Next, GCpahse uses two error correction steps to refine the phasing results obtained from the previous step, effectively reducing the error rate. Finally, GCphase obtains the phase block. GCphase was compared to three other methods, WhatsHap, HapCUT2, and LongPhase, on the Nanopore and PacBio long-read datasets. The code is available from https://github.com/baimawjy/GCphase .
CONCLUSIONS: Experimental results show that GCphase under different sequencing depths of different data has the least number of switch errors and the highest accuracy compared with other methods.

BACKGROUND: Point-of-care cardiovascular left ventricle ejection fraction (LVEF) quantification is established, but automatic tablet-based stroke volume (SV) quantification with handheld ultrasound (HAND) devices is unexplored. We evaluated a tablet-based monoplane LVEF and LV volume quantification tool (AutoEF) against a computer-based tool (Tomtec) for LVEF and SV quantification.
METHODS: Patients underwent HAND scans, and LVEF and SV were quantified using AutoEF and computer-based software that utilized either apical four-chamber views (Auto Strain-monoplane [AS-mono]) or both apical four-chamber and apical two-chamber views (Auto Strain-biplane [AS-bi]). Correlation and Bland-Altman analysis were used to compare AutoEF with AS-mono and AS-bi.
RESULTS: Out of 43 participants, eight were excluded. AutoEF showed a correlation of .83 [.69:.91] with AS-mono for LVEF and .68 [.44:.82] for SV. The correlation with AS-bi was .79 [.62:.89] for LVEF and .66 [.42:.81] for SV. The bias between AutoEF and AS-mono was 4.88% [3.15:6.61] for LVEF and 17.46 mL [12.99:21.92] for SV. The limits of agreement (LOA) were [-5.50:15.26]% for LVEF and [-8.02:42.94] mL for SV. The bias between AutoEF and AS-bi was 6.63% [5.31:7.94] for LVEF and 20.62 mL [16.18:25.05] for SV, with LOA of [-1.20:14.47]% for LVEF and [-4.71:45.94] mL for SV.
CONCLUSIONS: LVEF quantification with AutoEF software was accurate and reliable, but SV quantification showed limitations, indicating non-interchangeability with neither AS-mono nor AS-bi. Further refinement of AutoEF is needed for reliable SV quantification at the point of care.

The implementation of information technologies into treatment practice of modern health care significantly increases efficiency of diagnosis and treatment of dorsopathies. The purpose of the study is to develop software module to evaluate post-urological status of patients with spine dorsopathy to optimize diagnostic process and treatment planning. The article describes application of original software module to assess the post-urological status of patients with spine dorsopathy. The proposed software module is designed for objective assessment of post-urological status of patients with spine dorsopathy based on photographic images of examined patient. The most important advantage of software module is built-in forecasting function that is implemented by calculation of severity of pain perceptions and movement disorders using generated regression equations. The possibility to record data obtained during measurements permits to store information in single system. The implementation of software module into clinical practice will contribute to qualitative improvement of diagnostic processes, simplify work of osteopath and reduce the time spent on examination.

Managing and evaluating the literature review process can be a time-consuming challenge when working one-on-one with students to teach scientific process skills, whether through an independent study, honors contract, or undergraduate research. In this article, I share my solution to address the pedagogical and organizational challenge posed by this type of teaching and mentoring: the citation management software Zotero used in conjunction with a template worksheet. Overall, this approach will save time, introduce students to a new multipurpose software tool, and lead to a set of reusable resources for future teaching.

UNASSIGNED: Human papillomavirus (HPV) is the main risk factor for the development of squamous cell cervical cancer, and E6 oncoprotein and E7 oncoprotein are important components of the viral genome and its oncogenic potential. It is known that different viral variants of HPV16 have different pathology and impact on the development of neoplasia, although few studies have been performed on South American variants.
UNASSIGNED: Therefore, the present study aimed to analyze in silico the genomic diversity of HPV16 in 20 complete genome variants of South America in the National Center for Biotechnology Information (NCBI) database.
UNASSIGNED: We performed a descriptive study to characterize the polymorphic regions of the E6 and E7 genes in HPV16 variants, using software for genomic data and single nucleotide polymorphism (SNP) analysis and others for phylogenetic analysis.
UNASSIGNED: The variants analyzed included six SNPs linked to cancer (A131G, G145T, C335T, T350G, C712A, and T732C) and significant variation (798 nucleotide substitutions). Despite this, the variants showed low genetic diversity. Eighteen variants of unclear significance (VUS) were identified, 10 of which were in the coding E6 regions and 8 in the coding E7 regions. The prevalence of lineage D variants is of concern due to their pathology in cervical cancer and requires more research and epidemiological vigilance regarding their prevalence in the population.
UNASSIGNED: The data obtained in this study may contribute to future research on South American variants of HPV16, their pathogenicity, and the development of treatments.

Vital signs are important indicators to evaluate the health status of patients. Channel state information (CSI) can sense the displacement of the chest wall caused by cardiorespiratory activity in a non-contact manner. Due to the influence of clutter, DC components, and respiratory harmonics, it is difficult to detect reliable heartbeat signals. To address this problem, this paper proposes a robust and novel method for simultaneously extracting breath and heartbeat signals using software defined radios (SDR). Specifically, we model and analyze the signal and propose singular value decomposition (SVD)-based clutter suppression method to enhance the vital sign signals. The DC is estimated and compensated by the circle fitting method. Then, the heartbeat signal and respiratory signal are obtained by the modified variational modal decomposition (VMD). The experimental results demonstrate that the proposed method can accurately separate the respiratory signal and the heartbeat signal from the filtered signal. The Bland-Altman analysis shows that the proposed system is in good agreement with the medical sensors. In addition, the proposed system can accurately measure the heart rate variability (HRV) within 0.5m. In summary, our system can be used as a preferred contactless alternative to traditional contact medical sensors, which can provide advanced patient-centered healthcare solutions.

Keeping up to date with the latest clinical advances in prostate cancer can be challenging. We investigated the impact of guideline use on quality of treatment decisions as well as the impact of a novel, CE-certified clinical decision support tool (Siemens AIPC software) on the amount of time clinicians spend on decision-making in a multicenter setting. Ten urologists assessed ten clinical cases (screening and localized prostate cancer) in three settings: without support, using a digital version of the EAU guidelines, and with the AIPC tool, resulting in 300 clinical decisions. Comparison involved time spent, decision correct- and completeness. Using AIPC compared to digital guidelines led to a significant reduction of expenditure of time at a per case level (3.57 min and 0:14 min, p < 0.01) and for overall time per urologist (39.45 min and 02:20 min, p < 0.01). Decision options without guidelines support, online guideline usage and usage of AIPC were complete in 61%, 80% and 100%, respectively (p < 0.01). Decision making without guidelines support, online guideline usage and usage of AIPC was correct including all options in 28%, 66% and 100%, respectively (p < 0.01).Clinical decision support systems have the potential to reduces decision-making time and to enhance decision quality.