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Metabolic reprogramming within the tumor microenvironment (TME) is a pivotal driver of CD8+ T cell dysfunction in cancer. Tumor cells outcompete T cells for essential nutrients, including glucose and amino acids, while accumulating immunosuppressive metabolites such as lactate and 2-hydroxyglutarate. Beyond direct functional impairment, emerging research reveals that these metabolic alterations orchestrate CD8+ T cell transcriptional programs by remodeling their epigenome-via histone modifications, DNA methylation, and non-coding RNA networks-thereby dictating their differentiation, cytotoxic potential, and memory formation. A deeper understanding of how TME-derived metabolic signals shape the epigenetic landscape of CD8+ T cells is crucial for improving current cancer immunotherapeutic strategies. This review systematically delineates how key TME metabolic features, including nutrient deprivation and oncometabolite accumulation, regulate CD8+ T cell fate through epigenetic pathways. Furthermore, we discuss promising therapeutic strategies that target the metabolism-epigenetics axis to reinvigorate CD8+ T cell anti-tumor immunity, offering novel perspectives for enhancing adoptive cell therapy and immune checkpoint blockade. In cancer, tumor cells create a harsh environment that weakens the body’s key fighter cells, known as CD8+ T cells. These T cells are essential for attacking and destroying cancer, but tumors outcompete them for vital nutrients and fill the area with suppressive chemicals.Recent research shows that these chemical changes do more than just starve or poison the T cells. They actually rewire the T cells’ internal “control switches,” changing which genes are turned on and off. This rewiring dictates whether a T cell becomes a powerful attacker, gets exhausted, or forms a long-lasting memory.This article explains how the lack of nutrients and buildup of harmful chemicals in the tumor environment manipulate these control switches to impair T cell function. We also discuss promising new treatment strategies that target this link between metabolism and control switches to reinvigorate T cells, with the goal of making current immunotherapies more effective.
Pulmonary infections in elderly patients cause high morbidity and mortality. Conventional culture has low sensitivity and slow turnaround, delaying targeted therapy. Metagenomic next-generation sequencing (mNGS) is an emerging technology, but its diagnostic performance and cost-effectiveness are unclear. This study therefore aims to evaluate its diagnostic performance compared to conventional culture in older adults with pulmonary infections and to assess its cost-effectiveness. From March 2020 to March 2023, 522 patients (aged 55-69 years) diagnosed with pulmonary infections were enrolled at Peking University Shenzhen Hospital. Of these, 168 patients underwent simultaneous mNGS and conventional culture testing using bronchoalveolar lavage fluid (BALF) samples, while the remaining 354 patients received culture testing alone. Pathogen detection results were compared to assess the diagnostic performance of mNGS versus traditional culture methods. Additionally, cost-effectiveness analyses of the two diagnostic strategies-as well as the impact of mNGS testing timing post-admission-were conducted in the overall cohort and across stratified subgroups. Among the 168 patients who underwent both tests, mNGS identified a greater diversity and abundance of microorganisms than culture (overall detection: 89.88% vs. 26.79%; pathogen detection: 67.86% vs. 18.45%, p < 0.001). mNGS testing yielded a net economic benefit of 1202.70 CNY per patient overall and 3831.15 CNY among pathogen-positive cases. Delaying mNGS testing tended to be associated with increased hospitalization length of stay (LOS) and costs, with the most pronounced difference observed around 6 days after admission (p < 0.001). Early mNGS testing (within 6 days of admission) provided a net benefit of 6346.00 CNY. BALF-based mNGS showed higher positivity rates and a broader pathogen detection spectrum compared to conventional culture methods in this study. Early implementation of mNGS shows strong potential to guide the treatment of pulmonary infections and reduce healthcare costs for elderly and aging patients. Diagnosing pulmonary infections in elderly patients is challenging because traditional “culture” methods often fail to find the specific germ. In this study, we analyzed bronchoalveolar lavage fluid samples from 522 patients aged 55–69, we compared a newer DNA-based testing technology called metagenomic next-generation sequencing (mNGS), a method that can simultaneously detect all genetic material in samples and identify virtually any pathogen capable of causing infection, with traditional culture methods. We then compared the results of the two methods and calculated total hospital costs—including testing, length of stay, and other related expenses. Our findings highlight three key advantages. First, mNGS detected a much wider variety of pathogens than culture methods. Second, despite the higher upfront cost of the sequencing test, using mNGS was actually cheaper overall than relying on culture, saving an average of 1202.70 CNY per patient by guiding better treatment. Third, timing is critical: performing mNGS early (within 6 days of admission) saved significantly more money compared to delayed testing. These results suggest that using mNGS early is not only clinically superior but also reduces the financial burden on patients and hospitals.
The case for early vasopressor initiation in septic shock has been argued in detail in physiologic reviews and randomized trials. The evidence base is no longer the limiting factor. What remains limiting is delivery. Across most U.S. emergency departments and many international settings, patients with septic shock still do not reliably receive norepinephrine within the first hour of recognition. This review reframes the early-vasopressor question from a physiologic argument into an implementation problem and identifies three structural barriers that operate independently of any individual clinician's understanding of the underlying evidence. The first is regulatory: the SEP-1 quality measure, despite a documented physician exception for the fluid requirement, continues to incentivize a fluids-first sequence as the institutional default. The second is cultural: the gap between policies that permit peripheral norepinephrine administration and the workflows, scope-of-practice arrangements, and standing orders required to actually start it at the bedside. The third is upstream: time-to-vasopressor is partly a downstream surrogate for time-to-recognition, and interventions that target only the pressor decision miss the larger source of delay. We propose a parallel resuscitation framework with explicit protocolized triggers and stratify implementation considerations across U.S. academic centers, U.S. community emergency departments, and resource-limited international settings. Closing the gap means stopping the physiology argument and rebuilding the operational architecture.
State space exploration has been a core challenge in reinforcement learning, due to the difficulty in designing an intrinsic reward function that can guide agents to precisely find high-novelty states in the state space. Most existing methods design the reward function without fully utilizing any knowledge concerning environments, which often produces inaccurate reward signals. Instead, this paper assumes the hindsight knowledge extracted from the agent's previous exploration experience can help intrinsic reward designs. Therefore, the counterfactual intrinsic reward assignment method is presented, which uses the counterfactual reasoning mechanism from the causal learning field to mine hindsight knowledge from previously explored states, and utilizes this knowledge for better intrinsic reward assignment to the agent. The core idea is to "backtrack" the agent's obtained exploration result and "reflect on" whether a more novel state could have been discovered, if the agent had selected another action at the time. Concretely, the method first samples a batch of "counterfactual" actions that differ from the current action from a policy, then uses a structural causal model to predict their corresponding next states. Subsequently, the method will give a large reward if the state actually explored is more novel than that in counterfactual reasoning, otherwise a low reward, thereby "rewarding" agents to effectively identify and find novel states in the state space. Simulations show the effectiveness in improving state space exploration, with the performance enhanced over 10 OpenAI Gym environments.
Treating autosomal dominant polycystic kidney disease (ADPKD) has always been a challenge because the disease is too complex for single-target drugs, which are often held back by side effects. This narrative review explores a different strategy: using plant-derived polyphenols to target multiple disease pathways at the same time. Looking at research from 2005 to 2026, we break down how key compounds like resveratrol, curcumin, naringenin, quercetin, and epigallocatechin-3-gallate (EGCG) actually work. Preclinical studies show these molecules can slow down cyst growth by tackling inflammation, rapid cell division, and tissue scarring all at once, while also resetting the skewed energy metabolism of cystic cells. Some mechanisms are strikingly specific, such as naringenin's direct interaction with polycystin-2 and quercetin's ability to clear senescent cells. Yet, the real-world hurdle is poor absorption; a recent clinical trial with standard curcumin fell short simply because the compound could not reach the kidneys in high enough concentrations. Moving forward, the field needs to focus on testing these compounds in realistic animal models, designing smart nanoformulations to improve bioavailability, and exploring combinations that could safely complement current therapies like tolvaptan.
Heat is now the deadliest weather hazard in the United States. California faces this hazard in two forms, acute heat waves affecting millions and chronic exposure, which affects its large agricultural workforce. The state directs climate resilience resources using tools such as composite vulnerability indices and competitive grant programs. Whether these instruments reach the communities actually experiencing heat-related illness has not been systematically tested against health outcomes. We map heat-related Medicaid claims across California ZIP codes from 2011 to 2019 to evaluate who bears the health burden of heat, how well existing vulnerability indices capture it, and whether state grant funding reaches the highest-burden communities. We show that ZIP codes with the highest claim rates have lower median incomes, more farmworkers, and more mobile homes than the state average. Heat-related claim rates rise 24.4% per 1 °C in majority-cropland ZIP codes, compared with 20.6% per 1 °C in majority built-up areas. Of three vulnerability indices tested, only the CDC heat and health index, which itself incorporates emergency room data, correlates strongly with observed claim rates. Our analysis suggests that State Extreme Heat and Community Resilience Program funding broadly tracks county-level claim counts, but several high-burden counties, including Kern, Fresno, and Imperial, are substantially underfunded. We conclude that using medical claims data in conjunction with indices could lead to a more effective allocation of funding to communities experiencing heat risk in California than considering indices alone.
Parkinson disease (PD) is a pervasive neurodegenerative disorder globally, largely characterized by motor symptoms. Most existing artificial intelligence models for PD detection are trained on participants in well-resourced settings with confirmed clinical diagnoses. However, specialist-confirmed labels are often infeasible in low-resource settings. We developed a web platform for structured mouse data collection through pattern tracing tests. We sought to assess the feasibility of leveraging data from a community-recruited sample of participants with suspected but undiagnosed PD to train artificial intelligence models that achieve respectable performance in predicting diagnosed PD. We tested whether using weaker diagnostic labels that may be more feasible to collect in community or global health settings, where access to professional neurologists is sparse or nonexistent, can lead to models that learn predictive signals that are diagnostically useful. 261 participants (73 self-reported PD, 155 non-PD, and 33 suspected PD) were recruited from community organizations in Hawaii and completed 3 pattern tracing tasks on our custom web assessment: straight line, sine wave, and spiral wave. During each task, cursor positions, screen dimensions, and an in-target boolean flag were recorded. From these data, we engineered features and generated mouse trace images. We built 3 categories of classifiers: (1) a feed-forward neural network using engineered features, (2) fine-tuned computer vision deep learning models, and (3) multimodal models concatenating a feed-forward neural network with computer vision models. Performance was evaluated using 1 primary experiment and 2 secondary analyses. The primary experiment involved training on suspected PD versus non-PD and testing on self-reported PD versus non-PD. A secondary analysis evaluated the reverse direction by training on participants with self-reported PD and without PD and then testing on participants with suspected PD versus participants without PD. Additionally, a cross-validation analysis was conducted using participants with self-reported PD versus those without PD with 5-fold cross-validation to establish baseline performance under well-defined diagnostic labels. The best-performing models included a multimodal Vision Transformer in the primary experiment (F1: mean 0.7619, SD 0.0535), a multimodal ResNet-50 in the secondary analysis (F1: mean 0.9353, SD 0.0334), and an image-based DenseNet-201 in the cross-validation analysis (F1: mean 0.9027, SD 0.0332). Training on patients with suspected PD yielded meaningful performance in predicting self-reported PD, supporting the feasibility of using lower-specificity labels for model development. This pilot feasibility study suggests that remotely collected mouse-tracing data can support PD screening models under data labeling conditions of low diagnostic specificity: models trained on suspected PD from a community sample may learn signals that can transfer to predicting actual PD. Future work may consider pretraining using weaker labels and then fine-tuning on stronger clinical labels.
Emergency nursing involves rapid decision-making, undifferentiated patient presentations, and limited opportunity for follow-up, often leaving patient and family outcomes unknown. Although outcome ambiguity has been linked to occupational distress, its nature and impact remain poorly understood. Existing knowledge is largely inferred from broader research on burnout and secondary trauma, leaving a gap in understanding how 'not knowing' shapes the professional and personal lives of emergency nurses. This study aimed to explore the frequency, scope, and impact of ambiguity relating to patient and/or significant others' clinical, personal, and social outcomes, and to identify strategies used by emergency nurses to mitigate its effects. A 17-item online survey was analysed using descriptive and inferential statistics and reflexive thematic analysis of free-text responses. Almost all participants (99%) reported experiencing outcome ambiguity, most related to whether a patient survived or died. Negative impacts were reported on professional practice (74.8%) and personal life (84.9%). Three themes describing ambiguity salience were identified in free-text data: the impact of extreme events, the vulnerability of paediatric patients, and impacts on the clinician self. Outcome ambiguity is pervasive in emergency nursing and affects both professional practice and personal wellbeing. Rare but extreme cases carry disproportionate emotional weight, highlighting the inseparability of clinical, emotional, and ethical dimensions of emergency nursing. Addressing ambiguity is critical to supporting emergency nurses' wellbeing.
The introduction of rapid, high-sensitivity cardiac troponin (hs-cTn)-based algorithms has markedly changed the work-up of patients admitted to the emergency department (ED) with suspected acute coronary syndrome (ACS). However, when applied to real-world ED populations, these algorithms perform worse than in clinical studies of derivation and validation. The main reasons for this discrepancy are that patients tested for hs-cTn in real-world settings tend to be older and less clinically preselected. Nevertheless, ACS must often be ruled out in patients with atypical presentations. Routine patients also more frequently have impaired renal function and pre-existing cardiac diseases, such as atrial fibrillation, heart failure, or coronary artery disease. These conditions do not necessarily cause the actual acute ED presentation. Using the standard decision limits of the 0 h, 0/1 h, or 0/2 h algorithms does not hinder the exclusion of ACS in the ED. However, using them in real-world conditions substantially decreases the positive predictive value for acute myocardial infarction (AMI) and classifies a higher percentage of patients into the "observe (gray) zone" than reported in clinical studies. Patients classified with a working diagnosis of "rule-in AMI" often require hospital admission for other reasons, though their discharge diagnosis may differ from AMI. A major challenge in real-world EDs is the high proportion of gray zone hs-cTn concentrations in approximately 50% of tested patients. Therefore, additional hs-cTn sampling at 3 h after admission is often necessary to rule out acute myocardial injury. This review summarizes and critically discusses the evidence for adjusting hs-cTn ED algorithm decision limits according to age, sex, and renal function. It also discusses the critical differential diagnosis of acute and chronic myocardial injury in the ED.
To develop and validate a prognostic prediction model for patients with traumatic multiple fractures and hemorrhagic shock using an Automated Machine Learning (AutoML) framework, evaluating its predictive performance and clinical utility. A total of 1,028 patients with traumatic multiple fractures and hemorrhagic shock admitted to the Emergency Departments and Intensive Care Units of seven public hospitals between January 2020 and December 2025 were retrospectively enrolled, with data from 4 hospitals designated as the training set (n = 720) and data from 3 hospitals serving as the test set (n = 308). Multidimensional data-including demographic characteristics, trauma/injury features, admission vitals/perfusion indices, and laboratory parameters-were extracted. The improved beaver behavior optimizer (IBBO) algorithm synchronously optimized feature subsets, base learners, and hyperparameter combinations. Clinical rationality of features was verified using LASSO regression and SHAP interpretability analysis. The IBBO algorithm demonstrated superior stability and outperformed the original BBO and comparative algorithms in most test functions. The AutoML model achieved best performance. The test set further confirmed its robustness, yielding a ROC-AUC of 0.9357 and PR-AUC of 0.9270. Decision curve analysis demonstrated that the AutoML model's clinical net benefit surpassed that of traditional methods across a threshold range of 1-96%; The calibration curve likewise indicated high consistency between predicted probabilities and actual outcomes, with a Brier score as low as 0.110. SHAP analysis identified key predictors in descending order of importance: GCS score, ISS score, time from injury to ER admission, lactate, and fibrinogen. The IBBO-based AutoML prognostic model provides an efficient, accurate tool for in-hospital mortality prediction in traumatic multiple fractures with hemorrhagic shock. The model identified that core predictors-including GCS score, ISS score, time to ER admission, lactate, and fibrinogen-critically influence in-hospital mortality outcomes. Clinical decision support software derived from this model offers visual, intelligent guidance for stratified care, promising utility in trauma emergency practice.
Accurate and reliable groundwater-level monitoring in deep observation wells remains difficult for conventional non-contact ultrasonic systems because narrow tubular geometries intensify multipath reflections, signal attenuation, and echo ambiguity. This study proposes a dual-signal direct time-of-flight (ToF) method that combines radiofrequency (RF) synchronization with one-way airborne ultrasonic propagation to a floating receiver located at the groundwater surface. In the proposed architecture, the RF signal provides a near-instantaneous time reference, whereas the ultrasonic signal defines the propagation delay, thereby eliminating dependence on echo-based ranging. The system integrates a wellhead surface unit for synchronized transmission and control, a floating unit for ToF acquisition and embedded processing, and an optional reference channel for in situ estimation of the effective sound speed. A duty-cycled power architecture is used to support low-power long-term deployment, while a multi-shot acquisition strategy with a median-like estimator improves robustness against startup transients, timing jitters, and false detections. Field validation was conducted over a 12-month period under actual groundwater-monitoring conditions, during which the groundwater depth varied between 14 m and 30 m below the wellhead datum. Within this field-validation interval, the proposed system achieved a mean absolute error of 0.048 m, a maximum absolute error of 0.050 m, and an overall valid detection rate of 99.4% over 358 valid cycles out of 360 scheduled cycles. In addition, a separate range-dependent confined-tubular propagation test was conducted to evaluate the extended detection capability of the RF-synchronized one-way ultrasonic ToF architecture. This test demonstrated stable acoustic-link ToF detection up to 300 m inside the tested 170 mm confined plastic pipeline. Therefore, the 300 m result should be interpreted as a range-dependent valid-detection result rather than as a 12-month groundwater-depth validation over the full 300 m interval. These results demonstrate that the proposed direct-ToF method provides an RF-synchronized one-way ultrasonic ToF framework with a floating receiver for groundwater-level monitoring in deep observation wells, while remaining compatible with low-power and IoT-based environmental monitoring systems.
The ROX index was proposed as a decision-support tool to assess the effectiveness of high-flow oxygen therapy (HFOT) in patients with acute hypoxemic respiratory failure (AHRF) affected by pneumonia. The purpose of this work was to assess the discriminative power of the ROX index across heterogeneous intensive care unit (ICU) populations. As a secondary, hypothesis-generating objective, we explored whether ROX-based risk stratification may provide a standardized reference for describing variability in observed intubation practices across datasets and centers. Patients affected by AHRF and receiving oxygen support were identified from two large public ICU databases (MIMIC-IV and eICU). Oxygen support was stratified based on recorded flow rates, i.e., LPMO2 ≥ 6 for conventional oxygen therapy (COT) and ≥ 30 for HFOT. All AHRF patients were initially considered, regardless of the underlying pathology, with a subgroup analysis performed in patients with pneumonia. ROX index predictions were compared with actual intubation rates in different datasets, and alternative thresholds were explored using Youden's method. In the primary three-category analysis, ROX risk strata produced only modest likelihood ratios (LRs) for observed intubation. In the merged cohorts, high-risk ROX categories showed LR values ranging from 1.36 to 2.06, whereas low-risk categories showed LR values close to 1, ranging from 0.85 to 0.90. Binary cut-off analyses confirmed limited discrimination, with AUROC values between 0.56 and 0.64. When applied across heterogeneous real-world populations, the ROX index shows limited discriminative ability for predicting intubation and should not be used as a standalone decision tool. However, it may serve as a standardized reference to explore variability in intubation practices across centers, particularly in retrospective analyses.
Background: While recent literature emphasizes the predictive value of composite inflammatory and nutritional indices for vascular outcomes, this study evaluates the actual predictive capacity of preoperative indices (PNI, GNRI, SII, NLR, PLR) for de novo arteriovenous fistula (AVF) maturation and 1-year primary patency. Methods: We retrospectively analyzed 945 end-stage renal disease patients who underwent strictly radio-cephalic autologous AVF creation. Preoperative indices were calculated from routine parameters. Diagnostic accuracy for predicting 1-year patency loss was assessed using receiver operating characteristic (ROC) curves, and a multivariate logistic regression model was constructed to adjust for baseline anatomical and clinical variables. Targeted subgroup analyses evaluated high-risk populations, including those with diabetes, coronary, and peripheral artery disease. Results: The 1-year primary and secondary patency rates were 73.3% and 93.1%, respectively. In contrast to prevalent reports, no significant differences in preoperative PNI, GNRI, NLR, PLR, or SII scores existed between patients with patent and thrombosed fistulas (p > 0.05). ROC analyses showed no predictive utility (AUC: 0.476-0.518). Crucially, multivariate logistic regression revealed that preoperative arterial (OR: 0.58, p < 0.001) and venous diameters (OR: 0.51, p < 0.001) were the strongest independent predictors of AVF failure, whereas all systemic biomarkers lacked independent predictive significance. Subgroup analyses confirmed these indices failed to predict AVF outcomes even in high-risk settings with severe endothelial dysfunction. Conclusions: Preoperative composite nutritional and inflammatory indices do not independently predict AVF maturation or long-term patency when adjusted for local anatomy. Local anatomical features and hemodynamics heavily dominate vascular outcomes, indicating that systemic biomarkers have limited standalone clinical utility for guiding preoperative vascular access planning.
Adhesive joints typically require high safety factors, as their mechanical performance is highly sensitive to environmental and manufacturing variations. Health monitoring can reduce these safety factors by continuously assessing the condition of the joint. While intrinsic and extrinsic sensing approaches exist, they are often based on periodic inspection or manual sensor integration, which limits their suitability for continuous in-service monitoring. This study investigates a novel sensor placement using additively manufactured strain sensors deposited by jet dispensing across the adhesive gap. Tensile lap-shear specimens were fabricated using CFRP (carbon-fiber-reinforced plastic) laminate, an epoxy adhesive, and silver-ink strain sensors placed internally within the joint and externally across the adhesive gap. Mechanical testing revealed that externally printed sensors produced an average resistance change of 65.3% near the failure stress of the adhesive joint, an order of magnitude higher than sensors embedded within the adhesive layer with 6.6% average resistance change. However, the average coefficient of variation increased as well, from 7.6% for internal to 32.6% for external. This sensor response exceeds reported environmentally induced variations in printed sensors and thus represents a promising candidate for condition monitoring. Further work is required to demonstrate actual damage detection capabilities and assess long-term stability under environmental and cyclic loading conditions.
Immigration Removal Centres (IRCs) are used to detain people for immigration control by the UK government. This scoping review aims to examine the experiences of detainees within UK IRCs, specifically how conditions within them, including the regime, affect their mental wellbeing. The Joanna Briggs Institute guideline for scoping reviews was followed. Six bibliographic databases and additional grey literature sources were searched for quantitative and qualitative evidence. Descriptive analyses and quality assessments were conducted. Fifteen research studies and nine pieces of grey literature were included, comprising a total of 1353 participants and 11 IRCs. The majority of data was qualitative in methodology and published after 2015. Main findings from articles were charted according to Maslow's (1943) Hierarchy of Needs, revealing persistent failings across all dimensions (physiological, safety, belongingness, esteem, and self-actualization). The regime within the IRCs as well as the psychosocial environment led to emotional distress and feelings of disempowerment, dehumanization, and criminality. This review highlights the negative impact of IRCs within the UK on the mental wellbeing of detainees and the need for urgent policy reform. Changes addressing temporal uncertainty of detention and use of community-based settings are proposed for UK governmental review.
Background/Objectives: Cipepofol is a novel intravenous anesthetic whose pharmacokinetics (PK) may vary with dosing regimens, sampling sites, and physiological differences across populations. However, clinical PK data remain fragmented across study settings and are limited for special populations and individualized perioperative use, highlighting the need for a mechanistic modeling framework. This study aimed to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for cipepofol across diverse populations. Methods: Clinical data from nine studies were included, comprising 371 subjects and 3521 plasma concentration measurements. The model was established in healthy adults using HSK3486-101, qualified using healthy-adult data from HSK3486-111 and anesthesia induction datasets, and extrapolated to hepatic impairment, renal impairment, and elderly populations using pathophysiology-informed adjustments. Individualized external validation was further performed in adult and pediatric surgical patients using actual clinical dosing histories. Model performance was evaluated using concentration-time profiles, goodness-of-fit plots, fold error, and geometric mean fold error (GMFE) for Cmax and AUC0-t. Results: The model adequately described both arterial and venous plasma concentration-time profiles across the establishment, qualification, extrapolation, and external validation datasets. Most predicted concentrations were within two-fold of the observed values, and the overall GMFE values were 1.22 for Cmax and 1.21 for AUC0-t. Simulated exposure differences in hepatic impairment, renal impairment, and elderly subjects were generally limited, suggesting no clinically meaningful PK changes from a PK exposure perspective in these populations. The model also reproduced arterial-venous concentration differences and supported the major contributions of UGT1A9 and CYP2B6 to cipepofol clearance. Conclusions: This PBPK model provides a mechanistic framework for characterizing cipepofol disposition and may inform future model-informed dosing studies.
Background and Objectives: The surgical management of three-wall orbital fractures remains a significant challenge due to complex anatomy, limited exposure, and the absence of clear landmarks. These extensive reconstructions are rare and traditionally burdened by high complication rates and inconsistent outcomes. This study presents a standardized surgical protocol for complex three-wall orbital reconstruction, highlighting the role of digital planning and a novel two-piece interlocking patient-specific implant (PSI). Materials and Methods: Between 2018 and 2024, 17 patients with unilateral three-wall orbital fractures underwent reconstruction using digitally planned, patient-specific two-piece titanium implants designed to restore the orbital floor, medial, and lateral walls. Implant positioning was assessed through qualitative evaluation of postoperative CT scans and quantitative comparison between planned and actual implant positions, as well as orbital volume analysis between reconstructed and unaffected orbits. Clinical outcomes were evaluated pre- and postoperatively. Results: Reconstruction was classified as ideal in 16 cases (94.1%) and satisfactory in one case (5.9%). Quantitative analysis demonstrated a high level of concordance between the planned and postoperative implant positions, with a mean deviation of 0.982 ± 0.107 mm (95% CI: 0.927-1.037 mm). All implants were positioned within 1.5 mm of the planned location. Postoperative orbital volumes closely approximated those of the contralateral side, with a mean volume difference of 1.371 ± 0.176 cm3 (95% CI: 1.280-1.461 cm3). Diplopia resolved in all patients, and enophthalmos was fully corrected in 15 cases (88.2%). No major complications or revision surgeries were observed. Conclusions: The proposed two-piece interlocking PSI enabled precise and reproducible reconstruction of complex three-wall orbital fractures. This approach demonstrates that even technically demanding orbital reconstructions can be performed with greater reliability, leading to favorable functional and aesthetic outcomes.
Although oxygen therapy (OT) is a fundamental and life-saving intervention in the management of hypoxemia, it may lead to serious complications when applied incorrectly or in an uncontrolled manner. The aim of this study is to evaluate the knowledge levels and attitudes of healthcare professionals working in a tertiary hospital in Somalia regarding OT administered to non-intubated patients. This descriptive cross-sectional study was conducted between 17 February and 2 March 2025 at Mogadishu Recep Tayyip Erdoğan Training and Research Hospital. A structured 23-item survey evaluating knowledge and attitudes related to OT was administered face-to-face to healthcare professionals consisting of nurses, resident physicians, and attending physicians. Knowledge levels were classified as poor (< 60%), moderate (60-79%), and good (≥ 80%) based on the percentage of correct responses. p < 0.05 was considered statistically significant. A total of 195 healthcare professionals participated in the study (42.6% nurses, 35.9% resident physicians, and 21.5% attending physicians). Overall, 49.2% of the participants (95% CI: 42.2% - 56.2%) reported knowing how to administer OT, though knowledge levels were predominantly poor across all professional groups regarding specific technical parameters, and 70% of nurses and 90% of physicians stated that OT training should be received (p = 0.223). The rate of receiving training before starting duty was higher among nurses (71.1%) than among residents (44.3%) (p = 0.003). As training sources, nurses more frequently reported school and orientation training, while attending physicians more frequently reported conferences/course programs (p < 0.001). Guideline use rates were similar across groups and were generally limited. Knowledge levels regarding low- and high-flow OT systems, indications, and monitoring parameters were poor in all groups. Knowledge of oxygen toxicity was higher among physicians than nurses (p = 0.001), and no significant difference was found among the groups in terms of awareness of morbidity and mortality (p = 0.189). This study provides descriptive and hypothesis-generating data highlighting significant knowledge gaps regarding OT among healthcare professionals in a resource-limited setting. The findings suggest a critical need for standardized protocols and continuous training programs, though future prospective studies are required to determine the causal relationships between these knowledge gaps and actual clinical practice or patient outcomes.
Incidence of chronic obstructive pulmonary disease and asthma diagnosis were lower during and after the Coronavirus disease 2019 pandemic in Alberta, Canada. However, it is unknown whether incidences were actually lower or if the pandemic created circumstances where patients did not seek care. As such, the objective of the current study was to explore the impact of COVID-19 on patient and clinician experiences of healthcare access and delivery. The study was conducted between October 2023 and July 2024. We used interpretive description, a qualitative approach with the end-goal of informing clinical decisions. Analysis was informed by Braun and Clarke's six phases of reflexive thematic analysis. We completed thirteen interviews. Two key themes were generated: (1) The pandemic impacted care-seeking behaviours; and (2) A time and place for virtual and in-person care. Clinicians discussed how access to entry points to the health system were impacted by the pandemic and highlighted how strategies to manage health and stressors impacted symptoms and subsequent care-seeking behaviours. Participants highlighted the positives of virtual and in-person care with the consensus that both are valuable. Future use of virtual care modalities should include a visual element at minimum and prioritize the therapeutic relationship.
Engineered biocatalysts enable highly selective chemical transformations with low environmental impact. Development of biocatalysts by directed evolution requires screening many enzyme variants for improved catalytic properties. The low throughput of commonly used label-free screening methods, e.g., liquid chromatography-mass spectrometry (LC-MS), becomes the rate-limiting step in biocatalyst development, limiting the coverage of protein sequence space explored. Direct MS methods have been applied to biocatalyst screening; however, these methods cannot be used to evaluate isomer selectivity. Ion mobility spectrometry is a separation technique readily combined with MS, facilitating isomer differentiation on the millisecond time scale. Here, we present a droplet microfluidic system coupled to cyclic ion mobility-mass spectrometry (cIM-MS) to enable the screening of isomeric products. The system was applied to the separation of biaryl benzofuran dimers formed by the fungal cytochrome P450 KtnC. The isomeric 5,7'-bibenzofuran and 7,7'-bibenzofuran products from KtnC variants were baseline-resolved within 32 ms by cIM. By infusing biocatalytic reaction mixtures as 5 nL droplets, an analysis throughput of 1.2 s/droplet was achieved using cIM-MS. Droplet cIM-MS was used to quantify standards in the reaction matrix with high agreement to actual concentrations of each isomer (i.e., R2 of 0.97 and 0.98 for total bibenzofuran content and fractional 5,7'-bibenzofuran content, respectively). For samples with enzymatically formed product isomers, droplet cIM-MS identified the same active variants and had comparable reproducibility (RSD of 10-15%) to analysis by LC-MS and produced this data 128 times faster. The method is expected to be suitable for improving the rate of biocatalyst development for isomer-selective reactions.