United States Air Force

End-of-life care presents unique challenges in austere or resource-limited environments where traditional medical resources are scarce or absent. This review explores the complexities of providing end-of-life care under such constraints, including recognition of the dying patient and techniques to alleviate suffering and allow death with dignity in under-resourced or expeditionary environments. Moreover, it presents these techniques in an accessible manner for providers without formal hospice training to use. Based on a literature review of hospice and palliative medicine, insights from the body of literature in wilderness and austere medicine, and the authors’ experiences in practicing in austere environments, this paper discusses practical approaches to symptom management, ethical considerations in end-of-life decision making, and accessible interventions with limited resources. By addressing these challenges and offering management recommendations, this review aims to enrich the literature and provide guidance for general medical providers who may lack formal palliative and hospice care training and yet find themselves in the situation of navigating end-of-life care in challenging and austere environments.

Spatial data integration is a more popular approach for regional-scale exploration of mineral deposits. The Kaiama area, located in the southern part of the Zuru Shist Belt, northwestern Nigeria, is known for its orogenic gold and cassiterite mineralization hosted primarily in metasedimentary rocks, pegmatitic granites, and shear zones influenced by Pan-African deformation events. However, the area remains underexplored due to limited systematic integration of geoscientific datasets for predictive mineral mapping. This study applies prediction-area (PA) analysis, weight sum modelling, multifractal analysis, and receiver operating characteristics/area under curve analysis (ROC/AUC) to delineate favourable zones for gold and cassiterite mineralization. The validity of spatial data, including satellite multispectral imagery, aeromagnetic, radiometric, and structural datasets, was tested using prediction area (PA) analysis. The weight sum modeling technique was employed for spatial data integration to develop mineral potential maps (MPMs) for gold and cassiterite. Discretization of the mineral potential maps (MPMs) for gold and cassiterite was achieved through multifractal analysis, while ROC/AUC analysis was utilized to evaluate predictive accuracies. Results from spatial integration using the weight sum model indicate that gold mineralization is most favorable in the southwestern, central, and northeastern parts of the study area, where mineral occurrences strongly correlate with geological structures and geophysical anomalies. Cassiterite mineralization favorability is highest in the southeastern and northern regions, primarily associated with pegmatitic intrusions and structurally controlled zones. The application of multifractal analysis suggests that the very high, high, and low potential classes for gold mineralization account for 13.46%, 25.8%, and 47.3% of the study area, respectively, while 13.25% represents the background value. Similarly, cassiterite mineralization favorability is characterized by 18.5%, 32.4%, and 38.1% for the very high, high, and low potential classes, with 10.8% representing the background value. The predictive accuracy assessment using ROC/AUC analysis revealed prediction accuracies of 81.2% for gold and 85% for cassiterite, confirming the effectiveness of the weight sum model as a reliable predictive tool for mineral exploration in the Kaiama area. This study highlights the significance of integrating geophysical, remote sensing, and statistical modeling techniques for effective mineral prospectivity mapping in underexplored regions.

Background The potential of large-scale future conflicts require expertise in field and facility-based care of high patient volumes in Prolonged Casualty Care scenarios. In our experience, a military-civilian partnership that enables Enlisted Medical Providers (EMP) to work at their full scope of practice in civilian hospitals is ideal to train excellent and reliable patient care which improves outcomes and ultimately saves lives. Methods Creating the opportunity and understanding needed to ensure EMP participation in the Las Vegas Military-Civilian Partnership (LV-MCP) required state legislative changes, discussions with the local governmental and private entities, military and civilian executive leadership buy-in, and institutional culture change. Results Over 2 years of data collection, 566 EMPs in 8 specialties developed technical skills, decision-making experience, and self-awareness in complex, high-acuity, hands-on patient care environments. Conclusion The high level of readiness achieved in the LV-MCP can and should be replicated in other markets. This commentary describes the policy, process, and institutional efforts undertaken to achieve EMPs working to their full clinical scope of practice in the LV-MCP hospital and calls for new ways to measure effective expeditionary readiness.

The global prevalence of allergic rhinitis (AR) remains high, posing challenges due to its chronic nature and propensity for recurrence. Gut microbiota dysbiosis contributes to immune dysregulation, impacting AR pathogenesis. Limosilactobacillus reuteri (L. reuteri) has great potential in regulating immune function to alleviate AR symptoms. However, the specific active components and mechanisms underlying its therapeutic effects in AR remain incompletely clarified. This study aimed to explore the potential mechanisms of L. reuteri and its metabolites in alleviating AR. The AR mouse model was constructed using ovalbumin (OVA). The analysis of hematoxylin–eosin staining (HE staining) and enzyme-linked immunosorbent assay (ELISA) suggested that L. reuteri alleviated nasal inflammation, suppressed aberrant Th2 immune responses, and modulated the balance of Treg and Th17 cytokines. The 16S rRNA sequencing and untargeted metabolic analysis revealed that L. reuteri restored gut microbiota composition and significantly increased the abundance of Ligilactobacillus and the metabolite luteolin (LO). Through ELISA and Western blotting analysis, LO treatment restored the Th1/Th2 and Treg/Th17 cytokine balance and suppressed the MAPK/STAT3 signaling pathway in AR mice. The study highlights LO as a key metabolite contributing to the anti-inflammatory effects of L. reuteri, suggesting potential avenues for future therapeutic strategies in AR management.

To promote the engineering application of polymer-based cement joint sealant (PCJS), the durability of PCJS was studied by testing the bonding, tensile and shear properties of PCJS under different service conditions. The results show that PCJS has excellent water resistance, acid/alkali corrosions resistance, UV aging resistance and low temperature resistance. The retention rate of bonding property of PCJS can achieve 85%. After water soaking, dry–wet cycle, acid/alkali corrosion, the retention rates of tensile and shear properties of PCJS can achieve 80%. After UV aging and low temperature treatment, the tensile and shear properties of PCJS are improved. After gasoline corrosion and high temperature treatment, the retention rates of tensile and shear properties of PCJS exhibit larger than 60%. The durability indexes of PCJS fulfill the technical requirements, and PCJS exhibits even more superior properties. Consequently, PCJS can be applied to joint engineering of cement concrete pavement.

HLA‐DRB4*01:03:01:22 differs from HLA‐DRB4*01:03:01:06 by one nucleotide change in intron 2.

Adaptive behavior is paramount for independent living and is varyingly impaired in different neurodevelopmental disorders. This study aimed to investigate differences in adaptive behavior between children with autism spectrum disorder and social communication disorder, two conditions characterized by deficits in social communication. Data from 232 children with autism spectrum disorder and 90 children with social communication disorder were analyzed. Adaptive behavior was assessed using the Vineland Adaptive Behavior Scale-III. Diagnoses were made independently using the Diagnostic and Statistical Manual of Mental Disorders, fifth edition criteria and the AIIMS Modified INCLEN Diagnostic Tool-autism spectrum disorder Diagnostic Evaluation for autism spectrum disorder. Statistical analyses included non-parametric tests and generalized linear models to account for age and sex differences. The results showed that children with social communication disorder exhibited better adaptive behavior than those with autism spectrum disorder across all domains (p < 0.001). The most significant differences were observed in the Vineland Adaptive Behavior Scale-III standard scores in communication (autism spectrum disorder: 50.40 ± 15.51; social communication disorder: 70.53 ± 9.69) and socialization (autism spectrum disorder: 69.46 ± 8.77; social communication disorder: 80.07 ± 6.16) domains. Age and overall adaptive behavior scores correlated well with group membership (p < 0.001). These findings emphasize the importance of distinguishing between autism spectrum disorder and social communication disorder in clinical practice. The results support the use of adaptive behavior assessments in diagnostic evaluations, highlighting the need for tailored interventions. Lay abstract This study compared adaptive behavior skills between children with autism spectrum disorder and social communication disorder using the Vineland Adaptive Behavior Scale-III. The researchers analyzed data from 232 children with autism spectrum disorder and 90 with social communication disorder. Key findings showed that children with social communication disorder demonstrated significantly better adaptive functioning across all areas compared to those with autism spectrum disorder. The largest differences were seen in communication and social skills. However, both groups still showed impairments compared to typical development, especially in expressive language. The study also found that younger children with lower overall adaptive behavior scores were more likely to be diagnosed with autism spectrum disorder. In addition, there was a higher proportion of males in the social communication disorder group than the autism spectrum disorder group. These results highlight important differences between autism spectrum disorder and social communication disorder, supporting their classification as distinct disorders. The findings emphasize the need for comprehensive adaptive behavior assessment during diagnosis and tailored interventions for each condition. Early identification and targeted support may be particularly crucial for children with autism spectrum disorder.

Oral zinc is a proven effective treatment for diarrheal illness, and long-term monitoring is key to evaluating the success of efforts to scale up zinc treatment. We examine zinc coverage for diarrheal illness in Bangladesh since the conclusion of the Scaling Up Zinc for Young Children (SUZY) project in 2008 and provide an overview of other countries’ zinc scale-up programs to compare the long-term effectiveness of SUZY. We used data from the Bangladesh Demographic and Health Surveys from 2005–2022 to examine the proportion of children under five receiving zinc treatment for diarrheal illness and evaluate disparities in zinc coverage by urbanicity and wealth quintile. We used a qualitative framework synthesis to compare the SUZY project with national or large-scale zinc scale-up programs in other low- and middle-income countries (Ghana, India, Kenya, Nepal, Nigeria, Uganda). This method for synthesizing qualitative and quantitative data was used to break down components of the SUZY project and other national or large-scale zinc scale-up programs. In Bangladesh, zinc coverage has continued to increase since the conclusion of the SUZY project, disparities in coverage between urban and rural areas and across wealth quintiles have been resolved, and the prevalence of diarrheal illness has decreased from 10·8% in 2007 to 4·8% in 2022. The countries with the highest zinc coverage (Bangladesh, Kenya, Uganda) had national rather than regional scale-up campaigns. Our findings demonstrate the long-term success of the SUZY project and provide insights into best practices for impactful zinc scale-up programs including significant pre-launch implementation research addressing key knowledge gaps and partnering with research organizations. Long-term monitoring of scale-up campaigns is important to determine if these interventions can become socially embedded and self-sustaining, improving health outcomes in the long run.

This paper introduces a novel prediction model designed to mitigate the substantial data dependency associated with maneuver trajectory prediction in unmanned combat air vehicles (UCAVs) during air combat. Considering the characteristics of high noise, dynamic complexity, and variable data lengths inherent in short-range air combat scenarios, we employ dynamic time warping (DTW) to assess the similarity of 3D trajectory data. This approach allows us to identify and select the most analogous historical data, which we then utilize as our training dataset. In pursuit of enhanced precision for online trajectory prediction, we propose an improved convolutional neural network (CNN) that not only offers “after-zero” information but also incorporates delay compensation mechanisms. Our experimental findings indicate that the proposed prediction model not only satisfies the stringent timeliness requirements but also outperforms benchmark models in terms of prediction accuracy across various operating conditions.

A weather radar is a primary tool used by forecasters to detect and warn for tornadoes in near–real time. To assist forecasters in warning the public, several algorithms have been developed to automatically detect tornadic signatures in weather radar observations. Recently, machine learning (ML) algorithms, which learn directly from large amounts of labeled data, have been shown to be highly effective for this purpose. Since tornadoes are extremely rare events within the corpus of all available radar observations, the selection and design of training datasets for ML applications are critical for the performance, robustness, and ultimate acceptance of ML algorithms. This study introduces a new benchmark dataset, Tornado Network (TorNet), to support the development of ML algorithms in tornado detection and prediction. TorNet contains full-resolution, polarimetric, level-II WSR-88D data sampled from 9 years of reported storm events. A number of ML baselines for tornado detection are developed and compared, including a deep learning (DL) architecture capable of processing raw radar imagery without the need for manual feature extraction required for traditional ML algorithms. Despite not benefiting from manual feature engineering or other preprocessing, the DL model shows increased detection performance compared to non-DL and operational baselines. The TorNet dataset, as well as the source code and model weights of the DL baseline trained in this work, is made freely available. Significance Statement Accurate and timely detection of tornadic signatures in radar data enables forecasters to issue timely warnings and preparedness measures, ultimately saving lives and reducing the devastating impact of tornadic storms. Machine learning (ML) has already been shown to be an effective tool for detecting key signals in radar that can be used to locate and track existing tornadoes. To further advance the state of the art in this area, this study presents a benchmark dataset that can be shared across the research community for the development and validation of ML-based algorithms for tornado detection. This work also provides a baseline deep learning (DL) model that is able to efficiently detect tornadic signatures in radar data.

Suicide risk has consistently increased over the past 2.5 decades, despite growing awareness and tailored programs aimed at combating this epidemic. Suicide prevention initiatives include ensuring 24/7 access to crisis hotlines, encouraging individuals to seek mental health care, and reducing access to lethal means among high-risk populations. A recent area of focus is the physician’s office, as research shows that nearly half of those who die by suicide had seen a primary care physician within one month of their death. However, primary care physicians do not consistently inquire about suicide risk among their patients. This study presents findings from 15 interviews with family medicine residents at a U.S. military hospital. Participants identified gaps in three key areas of training: i) foundational knowledge (e.g., risk assessment flow, available tools and resources, and therapeutic skills), ii) training program structure (e.g., timing, exposure, and effectiveness), and iii) training culture (e.g., fostering courage and support). Residents recognized their central role in suicide prevention and were eager to address perceived gaps in their knowledge. They also shared their ideal training environment, which would support learning and skill development. This paper offers clear and actionable recommendations for family medicine residency programs to advance the suicide prevention agenda.

Biocement is an environmentally friendly alternative to traditional cement that is produced via microbially induced calcium carbonate precipitation (MICP) and has great potential to mitigate the environmental harms of cement and concrete use. Current production requires on-site bacterial cultivation and the application of live culture to target materials, lacking the convenience of stable formulas that enable broad adoption and application by nonscientific professionals. Here, we report the development of a dry shelf-stable formulation of Sporosarcina pasteurii, the model organism for biocement production. At laboratory scale, when inoculated at an equivalent concentration of viable cells, we show that this formulation produces biocement equal in strength to that produced using live cell cultures. We further demonstrate that this formulation forms biocement in the field within 24 h, leading to ground improvement with increased bearing capacity. These results illustrate that preserved, shelf-stable bacteria can contribute to rapid biocement production and can be adopted for scaled geotechnical and construction purposes.

Decision-makers rely on intelligence to make targeting decisions that comply with international humanitarian law (IHL), yet the relationship between intelligence and the law is not frequently discussed. This article explores crucial elements of intelligence and intelligence analysis that decision-makers should understand to increase their compliance with IHL, focusing on three crucial decision points: (1) the determination of whether a potential target is a military objective, (2) proportionality in attack analysis, and (3) the taking of effective precautions.

Purpose To investigate the diagnostic value of spectral CT in calculating extracellular volume fraction (ECV) for assessing the severity of liver cirrhosis. Methods In this retrospective study, 172 patients (127 liver cirrhosis patients and 45 controls),who underwent spectral CT liver enhancement scans, and were categorized based on the Child-Pugh classification. During the delayed phase, ECV values were derived from iodine density map. These ECV values were then compared across the control group and subclassified cirrhosis groups (Child-Pugh classes A, B, and C). Furthermore, a correlation analysis was performed to assess the relationship between ECV values and Child-Pugh scores in liver cirrhosis. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance of ECV values and MELD-Na in the Child-Pugh classification of liver cirrhosis. Results The ECV values were 25.49±3.15, 29.73±3.20, 35.64±3.15, and 45.30±5.16 for the control, Child-Pugh A, Child-Pugh B, and Child-Pugh C group, respectively, demonstrating significant intergroup differences (F=184.67 P<0.001). A strong positive correlation was observed between ECV and Child-Pugh liver function classification (r=0.791, P<0.001). The diagnostic performance of ECV for differentiating between Child-Pugh classes A and B (AUC: 0.901), B and C (AUC: 0.966) was higher compared to the MELD-Na score (AUC: 0.772 and 0.868) (P<0.05, respectively). Multivariate analyses showed that ECV was the factor independently associated with cirrhosis (OR=1.610, P<0.001). Conclusion ECV values measured using spectral CT can serve as a noninvasive biomarker for assessing the severity of liver cirrhosis.

Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher accuracy and efficiency. However, the key to achieving accurate segmentation lies in abundant clues for contour distinction, which has a high demand for the network architecture design and its practical training status. To this end, we design auxiliary and refined constraints to optimize the energy function by supplying additional guidance in training procedure, thus promoting model’s ability to capture information. Specifically, for the auxiliary constraint, a set of convolutional structures are involved into a conventional network to act as a discriminator, then adversarial network is established. Based on the obtained architecture, we further build adversarial mechanism by introducing a second discriminator into segmentor for refinement. The involvement of refined constraint contributes to ameliorate training situation, optimize model performance, and boost its ability of collecting information for segmentation. We evaluate the proposed framework on two public databases (NIH Pancreas-CT and MICCAI Sliver07). Experimental results show that the proposed network achieves comparable performance to current pancreas segmentation algorithms and outperforms most state-of-the-art liver segmentation methods. The obtained results on public datasets sufficiently demonstrate the effectiveness of the proposed model for organ segmentation.

Repeated measurements of household air pollution may provide better estimates of average exposure but can add to costs and participant burden. In a randomized trial of gas versus biomass cookstoves in four countries, we took supplemental personal 24-h measurements on a 10% subsample for mothers and infants, interspersed between protocol samples. Mothers had up to five postrandomization protocol measurements over 16 months, while infants had three measurements over one year. For the subsample, we added up to 6 supplemental postrandomization samples for mothers and 3 for infants, measuring PM2.5, black carbon (BC) (mothers only), and carbon monoxide (CO) at each visit. 310 mothers had both protocol (n = 1026) and supplemental (n = 1099) valid exposure measurements. For children, supplemental data sufficient for analysis were collected in only two countries; 94 infants had both protocol (n = 317) and supplemental (n = 234) samples. The geometric means for protocol and supplemental samples for mothers for PM2.5 were 37 μg/m³ and 38 μg/m³, respectively, while for infants, they were 42 μg/m³ and 46 μg/m³. Mixed models comparing supplemental to protocol samples, controlling for covariates, found few differences between protocol and supplemental samples. Supplemental analyses among control mothers with complete protocol measurements found that an average of three measurements explained 81% of the variance of the average of all six measurements.