UCLA

The oral cavity presents a highly dynamic environment where inter-microbial communications play a pivotal role. Understanding the spatial organization of microbial ecosystems has been highlighted on the microbiome and polymicrobial infection. Furthermore, cross-feeding and modulation by metabolites from the oral microbiota and host cells, such as lactate and reactive oxidative species, impact the stability and functionality of microbial communities. Traditional research focusing solely on the compositional aspects of these communities is insufficient to understand the sophisticated interactions. We evaluated recent advancements in imaging technologies, bolstered by multi-omics analyses and artificial intelligence (AI)-driven approachesinsights, to provide an more integrated understanding of the dynamics and function of the oral microbiome. Real time imaging and resolution-enhancing methods at the single-cell level have unraveled the ecology and dynamics of microbial communities, indicating unique three-dimensional architectures and biogeographical patterns associated with disease status in polymicrobial interplays. Emerging computational techniques can account for the spatial features of oral microbiome by creating image-like representations that capture the complex relationships between host tissues and microbial communities. Spatial multi-omics, help address the limitations of single-cell sequencing, deciphering molecular mechanisms between species in these biogeographical patterns. To process the massive volume of imaging-based data, AI-assisted analysis enables complex dataset integration, predictive capacity, and personalized treatment, bringing a whole new level of understanding of the oral microbiome and its relationships with the host. In this review, we highlight recent imaging-based technologies used to study the spatial biogeography of interspecies and interkingdom relationships within oral microbial communities, focusing on how these interactions and functional/metabolic alterations associated with health and disease. We further outline limitations of AI-generated predictions and imaging-based observational data. Finally, we elaborate on potential biomarkers for early diagnosis and new effective therapeutic strategies to reshape microbial dynamics.

BACKGROUND: The global refugee crisis has reached unprecedented levels, placing millions of forcibly displaced individuals at heightened risk for psychological distress. While the effects of pre-migration trauma on mental health are well established, less is known about how intersecting identity factors and post-migration systemic barriers jointly shape posttraumatic stress severity. OBJECTIVE: Guided by Intersectionality Theory and Ecological Systems Theory, this study examined how pre-migration trauma exposure, identity-related characteristics, and post-migration stressors interact to predict post-traumatic stress symptom severity among refugees and asylum seekers. METHOD: Participants included 610 refugees and asylum seekers who completed standardized assessments at the Boston Center for Refugee Health and Human Rights. PTSD symptom severity was assessed using the Harvard Trauma Questionnaire. Predictors included pre-migration trauma exposure, identity factors (e.g., education level, region of origin, English proficiency), and post-migration stressors (e.g., legal status, employment, housing, community involvement). Classification and Regression Tree (CART) analysis was used to identify hierarchical and nonlinear interactions among predictors. RESULTS: The pruned CART model identified education level, English language proficiency, and region of origin as the strongest predictors of PTSD symptom severity. Individuals with some formal education, higher English proficiency, and origins outside Uganda and Europe reported higher symptom scores. Pre-migration trauma variables were not retained in the final model, highlighting the influence of post-migration structural conditions. CONCLUSIONS: Findings underscore the importance of moving beyond trauma-focused models to address how legal insecurity, social exclusion, and linguistic integration shape refugee mental health and recovery.

Existing research highlights TB's economic and social stigma burdens and impacts behavior in households, but virtually unexplored is the dynamics of TB and parenthood. This qualitative study examines the interplay between TB diagnosis and treatment, and parenting experiences, focusing on the intersections of individual agency, societal expectations, and access to resources. From March 2021 to January 2022, we conducted in-depth interviews with 98 TB patients in Buffalo City, South Africa, including 44 mothers and 54 fathers. Guided by the Network-Individual-Resource (NIR) model, we applied layered coding, memoing, and causal mapping to analyze experiences through the lens of agency and societal expectations. Mothers reported primary caregiving responsibilities, drawing motivation from their children, and relying on multigenerational family support for childcare and treatment-adherence. Children frequently provided emotional and practical care, such as cooking and medication reminders. Fathers more often distanced themselves to protect families from infection, faced isolation, and struggled to fulfill provider roles due to job loss. Extended family support for fathers was limited, with financial pressures exacerbating treatment challenges. These results illustrate how gender norms and resources structure the TB experience. Strengthening maternal caregiving networks and reimagining fatherhood beyond economic provision can foster better adherence and more equitable health outcomes.

Signal transducer and activator of transcription 1 (STAT1) defects present with diverse clinical phenotypes. We describe an unusual genotype/phenotype: disseminated mycobacterial infection without chronic mucocutaneous candidiasis in the context of a pathogenic STAT1 gain-of-function variant (p.Lys388Glu). To our knowledge, this is the first report of such a case.

Abstract  

Importance: Vaccination during pregnancy protects both the pregnant individual and the fetus from severe infectious diseases. Maternal immunization has expanded over the past decade, with new vaccines, robust safety data, and clear evidence of neonatal benefit through transplacental IgG transfer. 

Objective: To summarize current evidence regarding the safety, efficacy, and clinical recommendations for vaccines administered before and during pregnancy, outline strategies to address vaccine hesitancy, and provide an updated reference of vaccines recommended and contraindicated during pregnancy. 

Evidence Acquisition: Evidence synthesized from guidelines issued by the CDC, ACOG, SMFM, and major peer-reviewed publications including randomized trials, observational studies, vaccine safety surveillance systems, and meta-analyses. Literature informing disease burden, immune physiology, and perinatal outcomes associated with vaccine-preventable diseases also reviewed. 

Results: Influenza, Tdap, and COVID-19 vaccines demonstrate strong maternal and neonatal protective effects with excellent safety profiles. The RSV vaccine offers additional infant protection against severe lower respiratory tract disease. Several other vaccines (e.g., hepatitis A and B, meningococcal, polio, rabies) can be administered in pregnancy when clinically indicated. Live-attenuated vaccines remain contraindicated. Maternal immunization is most effective when integrated into routine prenatal care with onsite availability. Vaccine hesitancy can be mitigated through evidence-based counseling, addressing safety concerns, and improving the provider-patient trust relationship. 

Conclusion: Maternal immunization is safe, effective, and essential for preventing morbidity and mortality among pregnant individuals and infants. 

Relevance: This review provides clinicians with an updated guide for counseling, administering, and optimizing vaccine use before, during, and after pregnancy. 

The primary mission of NASA's Earth Surface Mineral Dust Source Investigation (EMIT) is to improve our understanding of mineral dust in atmospheric radiative forcing using a novel set of spectroscopically-based mineral observations. In order to estimate mineral mass abundance, information on median surface grainsize is required. Knowledge of clay and silt fractions as well as the sub-components of the sand fractions is required for calculation of median grainsize. Therefore, this study aimed to produce a map of surface soil texture suitable for the intended application. Spectral reflectance data were incorporated into a multi-output Random Forest model to predict mass fraction for seven particle size classes: silt (TSI), clay (Clay), and five sand size classes: very coarse sand (S1, 1–2 mm diameter), coarse sand (S2, 1/2–1 mm), medium sand (S3, 1/4–1/2 mm), fine sand (S4, 1/8–1/4 mm), and very fine sand (S5, 1/16–1/8 mm diameter). To account for the presence of vegetation in EMIT spectral data, spectra of soils were mixed linearly with spectra of green and nonphotosynthetic vegetation in various combinations and amounts and the mixed spectra were used as training data. A five-fold cross-validation approach allowed us to estimate independent errors for each of the texture classes as well as three parameters estimated post hoc: total sand fraction (TSA, calculated as the sum of the five sand classes), mean grain size (GSmean), and median grain size (GSmedian). Sand fractions have estimated mean absolute errors (MAEs) <6% (R2 = 0.54–0.81), while the error for TSA is <12% (R2 = 0.78). MAE for TSI and Clay is <4% (R2 = 0.66) and < 7% (R2 = 0.81), respectively. Estimated MAE for GSmedian is 30 μm (R2 = 0.78). For GSmean estimated MAE is 7.3 μm (R2 = 0.73). Above 70% soil cover (below 30% vegetation cover), vegetation did not appear to influence estimates. A final model trained on all available data was used to produce estimates of soil texture class mass fractions for dust-producing areas worldwide at 0.1° resolution suitable for supporting estimates of mineral mass fraction from EMIT data. Despite drastically different estimation approaches, our results are broadly consistent with other global mineral datasets, although differences are apparent. Overall, our approach indicates the value of imaging spectrometer data for estimation of surface grain size for application to EMIT's primary mission of better constraining dust radiative forcing, though additional paired soil spectra-texture measurements would improve results. The products may also be useful for other applications in the Earth sciences, especially those related to global modeling of dust.

Cellular organelles are uniquely specialized membrane-bound structures that enable cells to organize and coordinate biochemical processes. Specifically, mitochondria are essential organelles for cellular metabolism, coordinating energy production, and connecting signaling networks for cellular homeostasis. 99% of mitochondrial proteins are encoded by nuclear genes that require precise and efficient translation and import into mitochondria for biological processes. This process is mediated by coordinated pathways involving the mitochondrial specific translocation complexes, chaperones, and specialized targeting routes. Tight regulation of these import mechanisms allows for proper protein localization, folding, and assembly. Disruptions in the mitochondrial protein import pathway compromise organelle homeostasis and activate proteostatic stress and quality control pathways. Such defects have been observed in a wide range of pathophysiological conditions, including cardiovascular disease, neurodegeneration, and cancer. The import defects destabilizing mitochondrial proteins can impair oxidative phosphorylation and metabolic signaling. In sum, defects to mitochondrial function can highlight a central role of mitochondrial protein import beyond maintaining cellular function and how defects at distinct stages of import contribute to disease, underscoring opportunities for therapeutic intervention targeting mitochondrial proteostasis.