Severe pneumonia is one of the critical illness with the highest incidences and mortality rates in the intensive care unit. Its core threat lies not in the pulmonary infection itself, but in the subsequent multiple organ dysfunction syndrome (MODS). This review systematically delineates how severe pneumonia collectively accelerates organ injury through systemic inflammatory responses, type 2 immunity mediated by airway epithelial alarmins, endothelial thrombo-inflammatory pathways, as well as inappropriate oxygen therapy and mechanical ventilation. It further reviews the key points for early identification at the subclinical state, specific early-warning biomarkers, and bedside monitoring techniques applicable to extrapulmonary organs, including the heart, kidney, liver, and gastrointestinal tract, and proposes an integrated organ-protective strategy. This review emphasizes the concept of shifting the diagnostic and therapeutic window earlier, and aims to provide a reference for improving the comprehensive management of severe pneumonia by synthesizing current evidence and clinical practice.

We report the emergence of macrolide-resistant Bordetella pertussis during a pertussis outbreak in Peru. Among 68 cases, 31% carried the A2047G gene mutation, conferring resistance to macrolides. Whole-genome sequencing revealed 2 genetically distinct groups, indicating multiple introductions into Peru. Our findings support strengthening surveillance for macrolide-resistant pertussis to inform control strategies.

Asthma and chronic obstructive pulmonary disease (COPD) affect more than 650 million people worldwide and remain leading causes of disability, with a rising burden as populations age. Conversational agents (CAs) may offer a more interactive alternative. However, the evidence in obstructive lung disease has not been mapped.

The aim of this study is to map the literature on CA use in asthma and COPD, to describe the roles they have been designed to perform, the outcomes that have been measured, and to map the study designs and methods characterizing the current evidence.

A scoping review was conducted following the Arksey and O'Malley framework. A total of 9 databases (CINAHL, CENTRAL, Embase, IEEE Xplore, PubMed, ProQuest, Scopus, Web of Science, and Google Scholar) were searched from January 1, 2014, to February 22, 2025. Data were synthesized using inductive content analysis and organized via the Patterns, Advances, Gaps, Evidence for Practice, and Research Recommendations (PAGER) framework. Reporting followed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.

A total of 6275 records were screened, and 16 reports from 15 studies were included. Studies reported CAs being used for information provision and patient education, health data collection, and emotional or motivational support. Only 4 studies measured direct clinical outcomes (eg, asthma control or medication adherence). Reported usability and satisfaction findings were mixed, with recurring concerns about conversational flow, responsiveness, trust, and personalization.

The evidence base for CAs in asthma and COPD remains in early developmental stages. It consists mainly of small feasibility, developmental, and pilot studies, providing limited evidence on whether CAs improve clinical outcomes. Current evidence can mainly describe the roles CAs have been designed to perform and the user-experience factors that influence engagement. Adequately powered, longitudinal trials using clinically meaningful and standardized endpoints are required before effectiveness can be assessed. Future development should place patients, as the end users, at the center of co-design, with clinicians, including nurses, involved to ensure clinical relevance and safe integration into care.

Nonpharmaceutical interventions (NPIs), including digital contact tracing (DCT), are central to control the spread of airborne pathogens. Nevertheless, the effectiveness of individual interventions and the role of personal behavior remain insufficiently understood.

This study aimed to understand how NPI combination and the individual's behavior contribute to reducing airborne pathogen transmission.

We disentangle the efficacy of individual NPIs, including DCT, with a novel microlevel, behavior-driven agent-based model (ABM) that simulates individual human behavior to analyze the effectiveness of nonpharmaceutical interventions on airborne pathogen propagation. Our model's Zeitgeber architecture delineates contextual characteristics, including daytime, daily routines, locations, and activities. Our method determines each agent's current location and behavior in a realistic environment under NPI restrictions. We model viral load transfer between agents from contact duration, distance, and the infected agent's infectiousness level. We examine the effects of DCT-related behavior parameters, including adoption, adherence, and compliance, with a default intervention, and with further restricting and relaxing NPIs, on key pandemic indicators.

The effect analysis of personal choices regarding DCT indicates that a high DCT adoption rate (activate DCT) should be the first goal of a DCT implementation campaign, followed by adherence (notify others), and compliance (follow recommendations, if notified). There is no monotonic path in the behavior parameter space to improve pandemic characteristics. Assuming realistic behavior with regard to DCT, total infections were reduced by 43% in the default intervention, while DCT combined with other NPIs reduced total infections by up to 52%. Surprisingly, however, some restricting NPI combinations do not improve pandemic characteristics.

DCT implementations will face challenges, as pandemic characteristics do not consistently improve when behavior parameters ( adoption, adherence, and compliance) are increased. When considering realistic behavior, more is not always better; NPI combinations can interfere with each other to the detriment of pandemic control. Our approach offers fine-grained insight on the effectiveness of NPI combinations that cannot be obtained in human studies due to confounding effects. Thus, our approach can guide future pandemic control efforts and prioritization for pandemic preparedness.

Protecting populations against pathogens that induce non-sterilizing immunity remains a major public health challenge. However, conventional mathematical models are often incompatible with within-host data, offering limited insight into how immune responses drive epidemics. To address this gap, we develop a modular, data-driven mathematical framework that links immunological and virological dynamics to population-level transmission. Our approach derives infectiousness from viral load and protection against reinfection from time-varying immune responses, allowing epidemic trajectories to emerge from the summation of individual-level processes. As an example, we use viral load quantified in a SARS-CoV-2 human challenge study and binding antibody levels post-vaccination against SARS-CoV-2. The framework captures individual-level infection dynamics and shows that immune responses fundamentally shape epidemic trajectories. We show that weak correlations between antibody levels and protection lead to frequent reinfections and endemic circulation, whereas strong correlations generate recurrent explosive outbreaks. We fit the model to simulated case data to demonstrate its ability to recover underlying protection and reinfection dynamics. Applied to real-world data, this framework could provide new insights into the drivers of epidemic patterns and inform vaccination strategies for pathogens with non-sterilizing immunity.

Spinal cord stroke (SCS) is an underrecognized cause of severe acute myelopathy in children that can be misdiagnosed as an infectious or inflammatory process. Limited characterization of clinical and neuroimaging features of pediatric SCS impede timely and accurate diagnoses. Our objectives were to (1) identify clinical and radiologic features of SCS to prompt timely evaluation and accurate diagnosis and (2) gain insight into mechanisms of SCS.

We conducted a retrospective case analysis of children (<18 years old) diagnosed with SCS at a specialized referral institution between 2010 and 2025. Clinical records, imaging, and laboratory data were reviewed to identify key features of pediatric SCS.

Among the 56 included patients, 28 were male (50%), and the age distribution was bimodal, with peaks at 1 and 14 years. Pediatric SCS presented with hyperacute onset of motor, sensory, bladder/bowel, and pain symptoms. The lesions were predominantly anterocentral, longitudinally extensive, with the cervical spinal cord being most often affected. Over time from the onset of symptoms, MRI diffusion restriction decreased, while gadolinium enhancement increased. CSF profile was noninflammatory in 95% of cases. Suspected mechanisms were identified in 63% of cases, with vascular compression being the most commonly identified mechanism in 17 participants (30%), while 38% were idiopathic. Chiari I malformations were identified as the main suspected cause of vascular compression, occurring in a subgroup of young patients with cervical SCS (n = 8, 14%). Serum studies identified hypercoagulability in 38% of the 40 participants evaluated.

Recognition of specific clinical and neuroimaging profiles in pediatric patients with SCS may facilitate earlier identification and intervention. We propose a systematic evaluation of clinical history, with particular attention to the temporal course from symptom onset to nadir, early acquisition of spine MRIs with diffusion-weighted imaging sequences, serologic evaluations for hypercoagulability, and CSF studies to exclude inflammatory etiologies. This approach will provide a framework for continued efforts to better understand the mechanisms underlying pediatric SCS and develop prevention and treatment strategies.

Patients with chronic traumatic brain injury (cTBI) experience long-term exacerbated neurological decline, which is even more severe in older patients, yet the underlying mechanisms remain unclear. Cranial bone marrow (CBM) has recently been recognized as an adjacent immune surveillance organ of the brain that rapidly responds to acute brain injury. We investigated whether CBM-derived immune cells contribute to chronic neuroinflammation after cTBI by integrating clinical specimens, cell-tracing strategies, multiomics profiling, transgenic animal assays, adoptive cell transfer, and a proof-of-concept randomized clinical trial. We found that cTBI induced persistent aberrant myelopoiesis in the CBM, characterized by expansion of inflammatory monocytes/macrophages (Mo/Macs), which aggravated with aging. These CBM-derived Mo/Macs actively migrated into brain parenchyma, where they fueled chronic neuroinflammation and drove neurological deficits. Mechanistically, age-related peroxisome proliferator-activated receptor α (PPARα) deficiency caused lipid metabolism dysfunction in these Mo/Macs, enhancing H3K4me3-mediated regulation of inflammatory chromatin states and thereby promoting neuroinflammation. Activation of PPARα via fenofibrate rectified Mo/Mac lipid metabolism and reduced inflammatory Mo/Mac infiltration into the brain. In a proof-of-concept randomized clinical trial enrolling 40 older adult patients with cTBI, fenofibrate treatment reduced plasma neurofilament light chain levels and improved cognitive functions. Together, these findings demonstrate that CBM-originated inflammatory Mo/Macs may serve as key inflammatory drivers of cTBI and further show that fenofibrate represents a potential therapeutic strategy for cTBI treatment in older adults.

Existing clinical assessments for upper-limb motor rehabilitation poststroke pose limitations as end points for efficient clinical trials. This study aimed to develop a digital metric for assessing motor recovery using accelerometer data collected in naturalistic environments. We constructed the digital arm performance scale (DAPS) by analyzing ∼23,000 hours of data from 215 participants, including healthy individuals and subacute and chronic stroke survivors. We decomposed continuous upper-limb accelerometer data into lower-level movement segments, from which key features were extracted and aggregated using a linear mixed-effects model to produce an interpretable digital biomarker. DAPS demonstrated excellent reliability, sensitivity, concurrent validity, known-groups validity, discriminant validity, and responsiveness. Power analysis indicated that DAPS could reduce the required sample size for clinical trials with upper-limb motor recovery end points by more than 60% compared with traditional assessments. These findings highlight the potential of DAPS as a low-burden, scalable assessment tool for upper-limb motor recovery, with potential applications in both clinical trials and practice.