Archives of Clinical and Biomedical Research

Despite the remarkable progress of artificial intelligence represented by large language models, how AI technologies can contribute to the construction of evidence in evidence-based medicine (EBM) remains an overlooked issue. Now, we need an AI that can be compatible with EBM. In the present paper, we aim to propose an example analysis that may contribute to this approach using variable Vision Transformer.

Objective: To describe the clinical characteristics of term neonates with neonatal bacterial meningitis (NBM) and explore the association between different pathogens and imaging complications, providing clinical evidence for early identification and individualized management. Methods: A retrospective study was conducted on 531 term neonates diagnosed with NBM admitted to the Capital Institute of Pediatrics from 2013 to 2025. Demographics, clinical manifestations, laboratory parameters, etiological results, imaging complications and treatment measures were collected. Patients were divided into favorable/adverse discharge outcome groups and pathogen-positive/negative groups. Statistical analyses were performed using appropriate tests, and Cramers V coefficient was used to analyze the association between pathogens and imaging complications. Results: (1) The most common clinical manifestations were abnormal body temperature (79.85%), altered consciousness (55.18%) and jaundice (46.52%). CSF/blood culture was positive in 133 cases (25.05%), with Escherichia coli (27.07%), group B streptococcus (17.29%) and Staphylococcus species (16.54%) as predominant pathogens. The overall incidence of imaging complications was 22.22%, mainly hydrocephalus (5.84%), subdural effusion (4.90%) and encephalomalacia (2.64%). (2) Adverse discharge outcomes occurred in 107 cases (20.15%). Compared with the favorable group, the adverse group had higher incidences of convulsions, altered consciousness, anterior fontanelle bulging, abnormal muscle tone and primitive reflexes (all P<0.001), more obvious laboratory abnormalities (higher CRP, CSF leukocytes and protein, lower CSF glucose, all P<0.05), higher culture positive rates and greater need for adjuvant therapy (all P<0.001). (3) Pathogen-positive patients had higher imaging complication rates. Gram-negative infections were associated with higher hydrocephalus and subdural effusion rates, while Gram-positive infections had higher brain abscess risk. Specifically, Escherichia coli correlated with hydrocephalus and subdural effusion; group B streptococcus with cerebral infarction and encephalomalacia; LM with intracranial hemorrhage and brain abscess; negative cultures correlated with no imaging complications (all P<0.05). Conclusion: Term NBM neonates have non-specific manifestations, mainly abnormal body temperature and altered consciousness. Predominant pathogens are Escherichia coli, group B streptococcus and Staphylococcus species, with hydrocephalus and subdural effusion as common imaging complications. Adverse outcomes are associated with severe symptoms, obvious laboratory abnormalities and higher pathogen positivity. Specific pathogens correlate with distinct imaging complications.

BackgroundThere is a close correlation between neuroendocrine regulation and pulpitis progression. This study aims to identify key neuroendocrine regulation-related genes in pulpitis, providing insights for its treatment. MethodsGSE77459 and GSE92681 datasets were used to validate experimental findings. Key neuroendocrine regulation-related genes were identified via Cytoscape plugin cytoHubba and expression validation. Gene set enrichment analysis, RNA-binding protein regulatory networks, post-translational modifications, molecular regulatory networks, and drug prediction were performed. Key gene expression was experimentally verified in clinical samples. ResultsTop 10 genes were obtained via cytoHubba; 4 (IL6R, OSM, IL1RN, CCL4) with significant differences between pulpitis and control samples and consistent trends in both datasets were identified as key genes. Gene set enrichment analysis showed key genes participate in pathways like cytokine-cytokine receptor interaction. Related RNA-binding proteins were ELAVL1 and HNRNPA1, with phosphorylation as the main post-translational modification. Core regulatory microRNAs included miR-519, miR-765, miR-23, and regulatory factors included FOXC1, PRRX2. Targeted drugs (e.g., sarilumab, haloperidol decanoate, cyclosporine) were predicted, and clinical sample verification confirmed consistent expression trends. Conclusion4 key neuroendocrine regulation-related genes were identified, which may have clinical significance for the diagnosis and treatment of pulpitis.

Background: Medical imaging, especially computed tomography and magnetic resonance imaging, is essential in clinical care of patients with renal cell carcinoma (RCC). Artificial intelligence (AI) research into computer-aided diagnosis, staging and treatment planning needs curated and annotated datasets. Across literature, The Cancer Genome Atlas (TCGA) datasets are widely used for model training and validation. However, re-annotation is often necessary due to limited access to public annotations, raising entry barriers and hindering comparison with prior work. Methods: We screened 1915 CT scans from three TCGA-RCC databases and employed a segmentation model to annotate kidney lesion. After a meta-data-based exclusion step, we hosted a reader study with all papillary (n=56), chromophobe (n=27) and 200 randomly selected clear cell RCC cases. Two students quality checked and corrected the data as well as annotated tumors and cysts. Uncertain cases were checked by a board-certified radiologist. Results: After data exclusion and quality control a total of 142 annotated CT scans from 101 patients (26 female, 75 male, mean age 56 years) remained. This includes 95 CTs with clear cell RCC, 29 with papillary RCC and 18 with chromophobe RCC. Images and voxel-level annotations of kidneys and lesions are open sourced at https://zenodo.org/records/19630298. Conclusion: By making the annotations open-source, we encourage accessible and reproducible AI research for renal cell carcinoma. We invite other researchers who have previously annotated any of these cohorts to share their annotations.

Drug overdose deaths in the United States reached record levels during the fentanyl era before recently declining. A plausible hypothesis is that a sudden drop in fentanyl purity beginning in 2023 caused the downturn in overdose mortality. We evaluated this hypothesis by replicating a published analysis with regional overdose data, using models that account for time trends and autocorrelation, and negative control indicators to test for spurious correlation. When fentanyl purity was rising, the national purity series did not track overdose increases in most regions and showed only a modest association in the West. When both purity and mortality later declined, the observed associations were also seen with unrelated macroeconomic indicators that shared the same time pattern. National fentanyl purity alone does not provide a sufficient explanation for recent overdose declines.

International guidelines for low-frequency electromagnetic field exposure (LF EMF) are primarily intended to prevent substantiated adverse effects. In the frameworks, limits on internal electric fields are linked to external exposure levels through computational dosimetry. However, the relationship between internal electric fields and these adverse effects remains incompletely understood. In particular, current approaches often overlook the morphological complexity and diversity of cortical neurons, which may limit the realism of neuronal activation estimates used to support these assessments. This study evaluates LF EMF-induced neural activation using 25 morphologically realistic neuron models spanning all cortical layers, embedded within 11 detailed human head models. The internal electric fields were simulated for uniform magnetic field exposures (100 Hz-100 kHz) along the three anatomical directions, and excitation thresholds were computed using a multi-scale framework combining voxel-based dosimetry with biophysical neuron simulations. A real-world exposure scenario involving a child near an acousto-magnetic article-surveillance deactivator was also analyzed. Thresholds varied across cell type, morphology, cortical location, subject anatomy, frequency, and exposure direction, with L2/3 pyramidal, L4 basket, and L5 thick-tufted pyramidal cells showing the lowest thresholds. Despite this variability, all simulated thresholds were conservative with respect to the basic restrictions and dosimetric reference limits set by IEEE ICES and ICNIRP. The smallest margin occurred at 100 kHz, where the threshold remained a factor of 2.8 above the corresponding limit. These findings indicate that current LF EMF exposure limits remain conservative when evaluated using highly detailed, morphology-based CNS activation models.

BackgroundPaediatric liquid medicines (PLMs) routinely contain sucrose to improve palatability, yet their cariogenic potential is well established. Healthcare professionals awareness and prescribing practices regarding sugar-free PLMs have received limited study in India, particularly in Uttarakhand. MethodsA descriptive cross-sectional study was conducted among 500 healthcare professionals aged [&ge;]25 years, using a pilot-tested structured questionnaire (Cronbachs = 0.85), administered online and in person across Uttarakhand districts (January-March 2024). After excluding 69 incomplete responses, 431 participants were analysed (response rate: 86.2%), comprising general medicine practitioners (49%, n = 211), paediatricians (27%, n = 116), and dental practitioners (24%, n = 104). Descriptive statistics and chi-square tests were applied (p < 0.05). ResultsPrescription decisions were primarily driven by childs age and weight (58%), cost (40%), and pharmaceutical brand (37%). While 88% recognised PLM sweetness and 67% were aware of pH-dental harm links, only 20% associated PLMs with dental caries. Overall awareness of hidden sugars was 73%. Eighty-three percent knew of sugar-free alternatives (50% local availability), yet 80% found them less palatable and 85% costlier. Only 48% routinely provided oral health advice. A statistically significant association was found between specialty and sugar-free PLM awareness (p = 0.03), with dental practitioners recording the highest awareness (90%). ConclusionsHealthcare professionals demonstrated variable levels of knowledge, attitudes, and practices regarding PLMs, with critical gaps in caries recognition (20%) and oral health counselling (48%). Despite high sugar-free PLM awareness, uptake is constrained by perceived cost and palatability barriers. Targeted continuing medical education and policy measures, including sucrose-free labelling promotion, are needed to improve paediatric oral health outcomes in Uttarakhand. KEY MESSAGESO_LIOnly 20% of healthcare professionals in Uttarakhand associated pediatric liquid medicines (PLMs) with dental caries, representing a critical knowledge gap despite 88% recognising their sweetness. C_LIO_LIOverall awareness of hidden sugars in PLMs was 73%, yet only 48% routinely provided post-prescription oral health counsellingsubstantially below international benchmarks. C_LIO_LIEighty-three percent were aware of sugar-free PLM alternatives, but adoption was constrained by perceived inferior palatability (80%) and higher cost ([~]10% premium, cited by 85%). C_LIO_LIDental practitioners demonstrated significantly higher sugar-free PLM awareness than general practitioners and pediatricians (p = 0.03), supporting the case for interprofessional oral health education in medical training. C_LIO_LITargeted continuing medical education (CME) and policy measuresincluding sucrose-free labelling mandates and institutional formulary inclusionare needed to convert awareness into prescribing practice change. C_LI

ObjectivesTo estimate current, and 5- and 10-year projected, number of cases of cancer per year in transgender and gender diverse (TGD) people in England, overall and by tumour type, accounting for uptake of gender affirming care (GAC). DesignPopulation-based epidemiological modelling study using an age-stratified Monte Carlo simulations approach and the NORDPRED method for predictions. SettingModels estimating cancer case numbers for TGD people in England based on publicly available 2023 cancer surveillance data and survey-based 2025 GAC access, and predicted at 5 and 10 years hence. ParticipantsTGD people aged 15 years and above. Main outcome measuresPrimary cancer cases per year overall, by gender, age group, tumour type, and current and planned GAC. ResultsThe estimated TGD population size in England is 441547 (95% uncertainty interval (UI) 429207- 452890). Total cases per year of cancer in TGD people is expected to be 966 (95% UI 882-1069) excluding non-melanoma skin. Most cases are expected to occur in people aged 60-64. The top 5 expected cancers in TGD people are breast (19%, n = 187, 95% UI 149-241), colorectal (12%, n = 117, 95% UI 106-129), lung (11%, n = 108, 95% UI 96-122), melanoma (7.1%, n = 69, 95% UI 64-74) and urinary (6.2%, n = 60, 95% UI 54-67). Total cases of cancer in TGD people are estimated to be 1740 (95% UI 1584-1934) in 5 years and 2258 (95% UI 2066-2507) in 10 years (excluding non-melanoma skin). If TGD people were able to access their planned level of GAC, this would reduce these figures to 1555 (95% CI 1386-1766) and 2012 (95% CI 1797-2282) respectively. ConclusionsThis study provides prediction of cancer cases in TGD people in England, supporting the planning of service provision and training. This is vital, as with increasing disclosure, and long wait times for GAC, cancer cases in TGD people are predicted to increase. Summary BoxesO_ST_ABSWhat is already known on this topicC_ST_ABSThe annual number of cases of cancer in transgender and gender diverse (TGD) people in England is currently unknown as gender incongruence is not collected as part of the National Cancer Registration and Analysis Service. Some gender-affirming care (GAC) interventions are known to modulate cancer risk. Use of testosterone and chest reconstruction for transmasculine people is known to reduce their incidence of breast cancer compared to cisgender women. Use of oestradiol alongside medical or surgical androgen suppression has been shown to reduce the incidence of prostate cancer in transfeminine people while increasing their risk of breast cancer, compared to cisgender men. What this study addsThis study found that there are likely to be approximately 966 cases of cancer (excluding non-melanoma skin) in TGD people per year in the UK. Though total annual cases of cancer in TGD people are expected to be 2258 in 10 years, improved access to gender-affirming care could reduce total cases to 2012 (a 11% reduction). These figures provide additional justification for funding to improve access to GAC via the National Health Service (NHS), as well as for training on the oncological needs of this population.

ObjectiveOnly preliminary investigations on the use of the 445 nanometer wavelength blue light laser (BLL) for various laryngeal pathologies have been described. Currently, no standard exists for reporting treatment technique and tissue effect with this modality. Here, we aim to establish and validate a classification system to describe laser-induced tissue effects. Study DesignRetrospective video-based study for classification development and reliability validation. MethodsVideo recordings from procedures performed with the BLL by multiple academic laryngologists were retrospectively reviewed. A preliminary 6-point classification (BLL 1-6) was developed based on expert consensus. Thirteen additional procedural clips were independently rated utilizing the classification schema to assess perceived tissue effect, and measure inter- and intra-rate reliability. ResultsThe final 5-point classification system (BLL 1-5) included angiolysis, blanching, tissue vaporization, ablation with mechanical tissue removal, and cutting. The consensus of the combined reviewers in rating all cases was 89% (58 of 65). Complete consensus was not achieved in 11% (7/65) of cases. Of those incorrect, 57% (4/7) were of clips illustrating the BLL-2 classification. Intra-rater reliability amongst the reviewers was 100%. ConclusionTissue effect of the 445 nm blue light laser can reliably be standardized with this proposed classification system. This rating system can be used to facilitate future systematic study of outcomes and effective communication between laryngologists and trainees.

Optimizing radiotherapy dose distributions remain a resource-intensive bottleneck. Existing AI-based dose prediction methods often have limited generalizability because they rely on small, heterogeneous datasets. We present nnDoseNetv2, an auto-configured, end-to-end framework for dose prediction across diverse disease sites (head and neck, prostate, breast, and lung), prescription levels (1.5-84 Gy), and treatment modalities (IMRT, VMAT, and 3D-CRT). By integrating machine-specific beam geometry with 3D structural information, the framework is designed to generalize across varied clinical scenarios. A single multi-site model was trained on 1,000 clinical plans. On sites seen during training, performance was comparable to specialized site-specific models. On unseen sites (liver and whole brain), the model outperformed site-specific models, with mean absolute errors of 2.46% and 6.97% of prescription, respectively. These results suggest that geometric awareness can bridge disparate anatomical domains while eliminating the need for site-specific model maintenance, providing a scalable and high-fidelity approach for personalized radiotherapy planning.

PurposeThis study evaluates the Visual Hemofilter, a novel decision-support and information transfer tool designed to assist with regional citrate anticoagulation (RCA) in hemofiltration. By representing hemofilter parameters and patient blood constituents as animated icons, the tool aims to improve clinicians interpretation of blood gas results and RCA reference tables. We hypothesized that the Visual Hemofilter would enhance clinical decision-making by enabling faster and more accurate therapy adjustments, increasing clinicians confidence in their decisions, and reducing cognitive workload compared to conventional methods. MethodsWe conducted a prospective, randomized, computer-based simulation study across four intensive care units at the University Hospital Zurich. Twenty-six critical care professionals participated, each managing regional citrate anticoagulation (RCA) scenarios using either the Visual Hemofilter or conventional methods involving blood gas analysis and reference tables. Following each scenario, participants made therapy adjustments and rated their decision confidence and cognitive workload. ResultsUse of the Visual Hemofilter significantly improved decision accuracy (odds ratio [OR] 3.96; 95% CI 2.03-7.73; p < 0.0001) and reduced decision time by an average of 33 seconds (mean difference -33.3 seconds; 95% CI -39.4 to -27.2; p < 0.0001). Participants also reported greater confidence in their decisions (OR 5.41; 95% CI 2.49-11.77; p < 0.0001) and experienced lower cognitive workload (mean difference -15.05 points on the NASA-TLX scale (National Aeronautics and Space Administration-Task Load Index); 95% CI -18.99 to -11.13; p < 0.0001). ConclusionsThe Visual Hemofilter enhances clinical decision-making in RCA by increasing accuracy and speed, boosting decision confidence, and reducing cognitive workload. This technology has the potential to reduce errors and better support critical care professionals in managing complex treatment scenarios.

Sequencing the respiratory tract transcriptome has the potential to provide insights into infectious pathogens and the hosts immune response. While DNA-based sequencing is more standard in clinical laboratories due to its stability, RNA assays offer unique advantages. RNA reflects dynamic physiological changes, and for RNA viruses, viral RNA particles directly represent copies of the viral genome, enabling greater diagnostic sensitivity. However, RNAs susceptibility to degradation remains a significant challenge, particularly in RNase-rich specimens like saliva. To address this, we conducted a systematic, combinatorial evaluation of 24 distinct mNGS workflows, crossing eight nucleic acid extraction methods with three RNA-Seq library preparation protocols. Remnant saliva samples (n = 6) were pooled and spiked with MS2 phage as a control. The SARS-CoV-2 virus was spiked into half of the samples, which were extracted using the eight different extraction methods (n = 3) and compared using RNA Integrity Number equivalent (RINe) scores and RNA concentration. The extracted RNA was then processed across the three library construction methods and subjected to short-read sequencing to assess all 24 combinations head-to-head. We compared methods based on viral read recovery and found that RINe and concentration did not correlate with viral detection. The Zymo Quick-RNA Magbead kit and the Tecan Revelo RNA-Seq High-Sensitivity RNA library kit were the extraction and library-preparation kits that yielded the most SARS-CoV-2 reads, respectively. Importantly, our combinatorial analysis revealed that any small variability attributable to different nucleic acid extraction methods was heavily overshadowed by differences in quality attributable to the RNA-Seq library preparation methods. These findings challenge the reliance on conventional RNA quality metrics for clinical metagenomics and underscore the need to redefine extraction quality standards for mNGS applications. IMPORTANCEmNGS is a powerful and unbiased approach towards pathogen detection that has mostly been applied to blood and cerebrospinal fluid samples. However mNGS has recently been applied to more areas including the respiratory pathogen detection space, with potential applications in both in-patient diagnostics and public health surveillance. Saliva samples are an ideal sample type for these use cases since they can be collected non-invasively. However, saliva is also a challenging sample type due to its high RNase activity and often yields low-quality nucleic acid. This study explores the feasibility of using saliva specimens in mNGS with contrived SARS-CoV-2 samples to optimize the combination of two factors: nucleic acid extraction and RNA-seq library preparation. Exploration in this area could enhance the sensitivity of saliva-based mNGS assays, with the goal of future expansion of this specimen type in clinical diagnostics and public health surveillance. Key PointsO_LIThe choice of RNA-Seq library preparation kit has a greater impact on pathogen detection than the nucleic acid extraction method. C_LIO_LIThe combination of Zymo Quick-RNA Magbead extraction kit and TECAN Revelo RNA-Seq High Sensitivity RNA library kit recovered the highest percentage of total SARS-CoV-2 reads. C_LIO_LIRNA quantity and RINe score do not correlate with viral read capture, indicating a need for an alternative metric to assess RNA quality for downstream mNGS clinical diagnostics. C_LI

Importance: Physicians routinely prognosticate to guide care delivery and shared decision making, particularly when caring for patients with critical illnesses. Yet, these physician estimates are prone to inaccuracy and uncertainty. Artificial intelligence, including large language models (LLMs), show promise in supporting or improving this prognostication. However, the performance of contemporary LLMs in prognosticating for the heterogeneous population of critically ill patients remains poorly understood. Objective: To characterize and compare the performance of LLMs and physicians when predicting 6-month mortality for hospitalized adults who survived critical illness. Design: Embedded mixed methods study with elicitation and comparison of prognostic estimates and reasoning from LLMs and practicing physicians. Setting: The publicly available, deidentified Medical Information Mart for Intensive Care (MIMIC)-IV v2.2 dataset. Participants: We randomly selected 100 hospitalizations of adult survivors of critical illness. Four contemporary LLMs (Open AI GPT-4o, o3- and o4-mini, and DeepSeek-R1) and 7 physicians provided independent prognostic estimates for each case (1,100 total estimates; 400 LLM and 700 physician). Main outcomes and measures: For each case, LLMs and physicians used the hospital discharge summary and demographics to predict 6-month mortality (yes/no) and provide their reasoning (free text). We assessed prognostic performance using accuracy, sensitivity, and specificity, and used inductive, qualitative content analysis to characterize reasonings. Results: Mean physician accuracy for predicting mortality was 70.1% (95% CI 63.7-76.4%), with sensitivity of 59.7% (95% CI 50.6-68.8%) and specificity of 80.6% (95% CI 71.7-88.2%). The top-performing LLM (OpenAI o4-mini) accuracy was 78.0% (95% CI 70.0-86.0%), with sensitivity of 80.0% (95% CI 67.4-90.2%) and specificity of 76.0% (95% CI 63.3-88.0%). The difference between mean physician and top-performing LLM accuracy was not statistically significant (p = 0.5). Qualitative analysis revealed similar patterns in LLM and physician expressed reasoning, except that physicians regularly and explicitly reported uncertainty while LLMs did not. Conclusion and Relevance: In this study, LLMs and physicians achieved comparable, moderate performance in predicting 6-month mortality after critical illness, with similar patterns in expressed reasoning. Our findings suggest LLMs could be used to support prognostication in clinical practice but also raise safety concerns due to the lack of LLM uncertainty expression.

Recognizing the challenges faced by primary caregivers regarding the health of children with congenital craniofacial anomalies (CCAs) contributes to strengthening healthcare programs according to patient[s] and families differential needs. This qualitative study presents the experiences of 25 caregivers of children with CCAs from Bogota and Cali, Colombia, identified from care registries and consultation statistics provideed from public high-complexity healthcare institutions. Grounded in Giorgis descriptive phenomenology and employing thematic analysis, this research utilized semi-structured interviews and focus groups to explore the diagnostic process and its impact, experiences with healthcare services, and the caregivers role and daily care activities. Data were analyzed using MAXQDA(R) qualitative software. Findings highlighted the emotional complexity of caring for childre[n]s health. Challenges included late diagnoses, pessimistic views of the children with CCAs condition by healthcare team members; lack of effective support, information, and guidance from health staff; absence of clear care and referral protocols, and limited access to specific adaptations and timely specialized care for children with CCAs. There were also reduced therapeutic services, and a pronounced gendered caregiving burden when responsibilities fall almost exclusively on mothers. System fragmentation, reflected in deficiencies in communication and a lack of clear, coordinated, and timely pathways of care, as well as the absence of adequate psychosocial support for families, emerged as common structural problems in healthcare services in both geographic settings where this research has been conducted. Gender-sensitive strategies focused on alleviating emotional concerns and the burden of caregiving from diagnosis onward within a patient and family-centered care model are decisive. Improving comprehensive CCAs training for healthcare personnel and making adjustments to care pathways are suggested to contribute to the implementation of inclusive health programs that address the diverse needs of children and their families.

Definitive radiotherapy (RT) for prostate cancer (PC) with dose intensification and/or focal boosting has excellent oncologic outcomes, but many patients experience adverse events. Dose escalation to the whole prostate improves outcomes at the expense of increased late adverse events. Intraprostatic recurrence after definitive RT typically occurs at the site of the primary tumor, suggesting that dose to the site of the dominant lesion is an important predictor of future failure. The efficacy and safety of tumor-focused RT compared to that of standard RT for definitive treatment of localized PC has not been assessed. RadTARGET (RAdiation Dose TAiloRing Guided by Enhanced Targeting) is a phase II randomized trial that aims to demonstrate superior safety of image-guided, tumor-focused RT compared to standard RT for acute genitourinary (GU) or gastrointestinal (GI) in the setting of definitive RT for intermediate- and high-risk PC. The study intervention is image-guided, tumor-focused RT with dose intensification of cancer visible on imaging and dose de-intensification to remaining prostate. Patients will be randomized to two arms: those who receive standard RT dose and those that receive tumor-focused RT. The study population will be patients with intermediate- or high-risk PC planning to undergo definitive RT with or without systemic therapy. The primary endpoint to compare between randomized arms is acute GU or GI grade [&ge;]2 adverse events. Participant and study duration are 5 years and 8 years, respectively. RadTARGET will compare the efficacy and safety of tumor-focused RT to that of standard RT for definitive treatment of localized PC. We hypothesize that the tumor-focused approach will substantially reduce adverse events after prostate RT while retaining high efficacy. If this hypothesis is confirmed, we will conclude that a phase III randomized control trial is warranted to formally establish oncologic non-inferiority compared to the current standard of whole-gland dose escalation.

BackgroundLasers have wide applications in medicine and dermatology, but are associated with pain and anxiety, particularly in younger patients. Pain mitigation is often limited to topical anesthetics in the outpatient setting. Distraction techniques are limited by the need for ocular protection, which can include adhesive eye patches that can completely occlude vision. Virtual reality is effective at managing procedural pain and anxiety under other short medical procedures and is a promising tool for this population. ObjectiveThis trial aims to assess the safety, feasibility, and efficacy of Virtual Reality Pain Alleviation Therapeutic (VR-PAT) for pain management during outpatient laser procedures. Methods40 patients requiring outpatient laser therapy for at least two sessions will be recruited from a pediatric hospital in the midwestern United States for this crossover randomized, two-arm clinical trial with a 1:1 allocation ratio. During the first laser visit, the participant will be randomly assigned to either play the VR-PAT game during their procedure or wear the headset with a dark screen. Participants will answer questions about their pain (Numeric Rating Scale (NRS) 0-10), anxiety (State Trait Anxiety Inventory for Children, NRS 0-10, Modified Yale Preoperative Anxiety Scale (mYPAS)), and pain medication usage. Those playing the VR-PAT will additionally report simulator sickness symptoms and their experience playing the game. At their second laser visit, participants will crossover to the opposite intervention from their first visit. The primary outcomes are the difference in self-reported pain and anxiety between the two interventions. Feasibility outcomes include the proportion of screened patients who are eligible, consent, and complete both visits and adverse events reported. To evaluate the efficacy of pain reduction, composite scores of pain score, pain medication will be calculated for each laser visit. To evaluate the efficacy of anxiety reduction, the change of mYPAS scores will be compared between control and VR groups at each visit using Wilcoxon rank sum tests. All statistical analyses will follow the intention-to-treat principle in regard to intervention assignment at each visit. ResultsThe study was funded in January 2023 and began enrollment at that time. A total of n=44 participants were recruited and data collection was completed in November 2025, with n=40 subjects completing both visits. The sample was balanced with n=40 subjects using the intervention and participating in the control condition. The age range of the complete sample was 6 to 21 years at recruitment and was 55% female sex. Data analysis is in progress with final results planned for June 2026. ConclusionsFindings from this innovative randomized clinical trial will provide early evidence on the efficacy of the VR-PAT for reducing self-reported pain and anxiety during outpatient laser procedures. The results from this trial will inform a large-scale, multisite study. Trial RegistrationClinicalTrials.gov: NCT05645224 [https://clinicaltrials.gov/study/NCT05645224]

Objectives: To identifiy risk factors for antimicrobial resistance (AMR) in seven pathogen-antimicrobial combinations in patients with cancer and cancer survivors. Methods: Using data from patients with recent or past cancer diagnostic codes in Oxfordshire, UK, we examined associations between 22 potential risk-factors and AMR in blood culture isolates, collected between 1-April-2015 and 31-March-2025. Results: Among 5,975 bacteraemias in 4,365 adults, we analysed 3,141 (52.6%) due to Enterobacterales and 620 (10.4%) due to Enterococcus faecalis/faecium in 2,752 patients. Fourteen risk-factors for antimicrobial-resistant bacteraemia were identified, varying across pathogen-antimicrobial combinations. Compared with no previous antimicrobial susceptibility test result, prior resistance to the same antibiotic in any culture in the last year was strongly associated with AMR across all pathogen-antimicrobial combinations (all p<=0.001). Prior antibiotic exposure and younger age were also positively associated with AMR in four and five combinations, respectively. Cancer type showed modest effects; lymphoid/haematopoietic malignancies were associated with higher odds (vs colorectal cancer) of trimethoprim-sulfamethoxazole-resistant Enterobacterales (aOR=2.07 95%CI 1.40-3.06) and vancomycin-resistant Enterococcus bacteraemia (aOR=6.68, 1.21-36.91). Conclusions: Previous resistance was the greatest risk factor for bacteraemia with AMR in cancer patients and survivors, with prior antibiotic exposure and age also contributing. Lymphoid/haematopoietic malignancies increased risk of resistance to specific antimicrobials. Keywords: antimicrobial resistance, bacteraemia, cancer, risk factors

BackgroundIn patients requiring respiratory support, clinicians rely on physical exam, radiologic, laboratory, and ventilator-derived measures for the provision of sufficient support while minimizing ventilator and "work of breathing" induced lung injury. Point of care lung ultrasound (LUS) is a widely available tool in hospital and clinic environments. To date, LUS has not been used to evaluate lung strain. MethodsWe collected LUS images in four anesthetized, neuromuscularly blocked, and mechanically ventilated pigs being used for another experiment. A feature tracking tool was developed which tracked echo-bright lung structures in ten second clips obtained in triplicate of the right and left, upper and lower lung fields using tidal volumes of 4, 6, 8, 10, and 12 mL/kg. Pleural lines were manually drawn and a program for quantifying lung strain developed with assistance from Anthropic Claude Artificial Intelligence tool. Structures were identified in inspiratory and expiratory frames and tracked bidirectionally with median strain per frame used for calculations. ResultsTriplicate measures of lung ultrasound images in four pigs had a median coefficients of variation of 35% (23-47% IQR) and linear modeling of strain with tidal volumes of 4-12 mL/kg showed positive correlation with R2 value ranging from 0.89 to 0.97. Strain measurements were similar after bronchial administration of 1.5M hydrochloric acid. ConclusionsRegional lung strain quantification using LUS is a viable and potentially useful tool for respiratory support management.

There is an ongoing Oropouche Fever (OF) outbreak in Brazil since 2024. There are dengue and chikungunya prediction models available, but none to help discriminate dengue, chikungunya, and OF. Objective: This study aims to develop and validate clinical prediction models for dengue, chikungunya, OF. Methods: This study uses surveillance data from Espirito Santo state / Brazil, from 2023-2025. Epidemiological investigations and biological samples were used to conclude cases as either (a) clinical-epidemiologically confirmed, (b) laboratory confirmed, or (c) discarded. The predictors were all data related to signs, symptoms, and comorbidities available in the notification forms. The analysis was performed using random forest regression models, one for each outcome, in development and validation datasets. Results: A total of 465,280 observations were analyzed, 261,691 dengue cases (56.6%), 18,676 chikungunya cases (4.0%), 12,174 OF cases (2.6%), and 179,115 discarded cases (38.6%). All three models had good discrimination and moderate to good calibration after scaling prediction. The models retained from 26 to 16 predictors each. Leukopenia and vomiting were the most discriminatory predictors for dengue, arthritis, arthralgia, and rash were the most discriminatory for chikungunya, and epidemiological features were the most relevant for OF. The dengue, chikungunya, and OF models had ROC AUC of 0.726, 0.851, and 0.896 in the validation set, respectively. Conclusion: This research identified predictors most discriminative between dengue, chikungunya, and OF. We developed and validated predictive models, one for each condition, with moderate to very good performance available at https://pedrobrasil.shinyapps.io/INDWELL/. One may use them in diagnostic work-up and arbovirus surveillance.

Increasing human activities along the US west coast are of concern for populations of cetaceans and particularly for a number of large whale species that are recovering from overexploitation during the era of commercial whaling. New rapid monitoring tools, such as satellite imagery analysis powered by recent advances in artificial intelligence, have potential to provide additional broad-scale and near real-time capacities for survey and monitoring. This paper investigates and demonstrates the feasibility of automatic detection of gray whales in sub-meter satellite imagery off the coast of California, USA. Observations and statistical analysis of regional imagery allowed not only an assessment of their detectability but also the development of robust signal processing and machine learning-based solutions for automated detection. To that end, a regional dataset of 221 gray whales was created using signal processing to inform a deep-learning-based detection framework, and 20 different large neural network architectures for feature extraction followed by a support vector machine algorithm for classification were evaluated for their detection performance. Neural network backbones included 19 convolutional neural networks and 1 transformer network. The best architecture generally achieved satisfying performance with an average balanced accuracy reaching up to 99.90%. It is also demonstrated that panchromatic imagery, in spite of the lesser amount of information provided, can be used to perform detection with a relatively high accuracy of 87.05%, allowing wider spatial and temporal coverage. Large-scale deployment of the best performing models over a broad range of regional satellite imagery resulted in the detection of 3353 gray whales, as well as opportunistic detections of humpback, blue and fin whales, in and going from December 28th 2009 to March 26th 2023. It also provided meaningful data points concerning the migration routes of gray whales within the Channel Islands and Southern California Bight. The large number of high-confidence detections indicates the capacity for a large-scale monitoring approach to support state and federal conservation policies such as gear mitigation, vessel speed reduction programs, or shipping lane redefinition that could also be expanded to other areas and for other species.