Citations

Abstract Overview: The emergence of effective immune therapies for multiple myeloma has highlighted the importance of understanding the roles of less abundant components within the tumor microenvironment. Single-cell analyses provide snapshots of the functional relationships of individual cells at precise moments in time, as well as insight into the overall cellular composition. By integrating single-cell datasets from multiple studies, we improve our ability to identify trends within rare cell populations, such as macrophages, and explore their clinical implications. We investigate human data here because our prior research of murine models indicated that macrophages expressing C1q, particularly those co-expressing Fcgr4, tend to correlate positively with increased tumor burden. Methods: We combined 56 human bone marrow samples – primarily collected at baseline, post-induction, or post-ASCT – with three publicly available data sets: GSE210079 (post CAR-T bone marrow microenvironment); GSE223060 (single-cell analysis of the MMRF cohort); and GSE271107 (disease progression from healthy donors and MGUS to SMM and NDMM). These data sets were integrated using Seurat to generate robust cellular clusters from over 300,000 cells across 117 samples. We validated our single-cell findings with bulk Affymetrix, whole bone marrow gene expression data (collected at baseline and post-baseline) from the Total Therapy clinical trials (GSE136324). Results: A distinct cluster of C1QA/B/C-expressing macrophages was consistently identified in all single-cell cohorts. These cells co-express FCER1G, CSF1R, and CD68 (markers also expressed in murine C1Q macrophages) and typically cluster adjacent to FCGR3A-expressing monocytes. Within the disease progression cohort, C1Q macrophages were found in all phases of progression: healthy donors, MGUS, SMM, and NDMM. However, C1Q macrophages in progressive disease (SMM and NDMM) have distinct expression features including lower levels of MRC1 and higher levels of APOBEC3A and ISG15 – genes linked to inflammatory responses. Among 11 post-induction samples submitted to lenalidomide maintenance, five with shorter responses displayed elevated expression of APOBEC3A and ISG15 in C1Q-expressing macrophages compared to six samples with longer responses. In a cohort of 22 samples 100 days post-ASCT, seven poor responders showed both an increased frequency of C1Q macrophages and elevated APOBEC3A expression compared to good responders. The MMRF cohort indicated that higher baseline frequencies of C1Q macrophages often preceded rapid disease progression (e.g. MMRF_1720, MMRF_1641). Anecdotal evidence from the CAR-T therapy cohort suggests that expansion of C1Q macrophages could serve as an early warning for tumor recurrence; sample 16 showed retention and expansion of C1Q macrophages 28 days post-CAR-T, that was subsequently followed by increased tumor cell counts at 3 months post-therapy. Analysis of bulk, whole bone marrow gene expression revealed that elevated levels of C1Q genes correlated with poorer outcomes, particularly in post-baseline samples. Multivariate Cox survival analysis revealed that high C1Q expression independently predicted inferior overall survival in post-baseline samples after adjusting for age, ISS stage, high levels of tumor-associated markers (SDC1, JCHAIN, DERL3), and high-risk UAMS7 subtypes (MF, MS, PR). Moreover, post-baseline GEP70 risk scores (from matched bone marrow aspirates) positively correlated with elevated C1Q expression in whole bone marrow samples. Conclusion: Both single-cell and bulk analyses indicate that the persistence of immunosuppressive C1Q macrophages following therapy is associated with inferior clinical outcomes. Although the precise mechanisms are yet to be fully understood, the close proximity of similar macrophages to tumor cells–as observed in other cancers–likely also occurs in myeloma and reflects the crucial relationship between tumor cells and macrophages within the bone marrow. While C1Q macrophages are typically rare in individual samples, analysis of combined cohorts is building evidence of their crucial role in immune cross-talk and therapeutic resistance mechanisms.

Study Design Retrospective cohort study using a nationally representative inpatient database. Objective To determine the incidence, timing, and predictors of new-onset psychiatric disorders following lumbar fusion surgery, and to propose a data-driven risk stratification model for early identification and intervention. Methods We queried the Nationwide Readmissions Database (2016-2019) to identify adults undergoing elective inpatient lumbar fusion. Patients with preexisting psychiatric diagnoses were excluded. Outcomes included new-onset depression, anxiety, suicidal ideation, and homicidal ideation within 12 months postoperatively. Predictors were assessed using odds ratios. Timing of onset was evaluated with density plots. Results Among 839,576 patients, 12,011 developed depression, 11,828 anxiety, 916 suicidal ideation, and 63 homicidal ideation. Most diagnoses occurred within 90 days postoperatively, with peak incidence around 3 weeks. Opioid use during the index admission significantly increased odds of suicidal ideation (OR 2.13, P = 0.001), anxiety (OR 1.26, P = 0.002), and showed a trend toward depression (OR 1.14, P = 0.087). Medicaid insurance was the strongest predictor of suicidal (OR 3.69) and homicidal ideation (OR 6.41). Female sex and younger age were associated with anxiety and depression; older age was protective against ideation. Conclusions New-onset psychiatric disorders are a frequent and early complication following lumbar fusion. Several modifiable and demographic factors - including perioperative opioid use and socioeconomic status - significantly predict risk. These findings underscore the need for structured mental health screening within the first 3 months post-surgery. Implementing a predictive risk stratification tool could enable earlier psychiatric referral, reduce long-term morbidity, and improve both recovery trajectories and overall quality of life.

Abstract Background The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. Methods We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. Results Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10−8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10−4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. Conclusions Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.

ABSTRACT Background Microbial overgrowth (MO) in the small intestine can cause gastrointestinal symptoms and may arise from stasis, such as dysmotility. Microtypes of MO include small intestinal bacterial overgrowth (SIBO) and intestinal methanogen overgrowth (IMO). Dyssynergic defecation (DD) is associated with constipation and slow colonic transit (STC). Our aim was to assess the relationship between DD and MO. Methods We retrospectively identified patients who underwent both anorectal manometry (ARM) and balloon expulsion testing (BET) for DD, and MO testing using either small intestinal aspirate culture or breath testing. SIBO was analyzed using two culture thresholds: ≥ 10 5 CFU/mL and ≥ 10 3 CFU/mL. Chi‐square tests compared positive vs. negative results. Key Results 436 patients underwent culture of SB aspirates. At the ≥ 10 5 CFU/mL threshold, 41.7% were diagnosed with SIBO, and 87.4% at ≥ 10 3 CFU/mL. At ≥ 10 5 CFU/mL, percent anal relaxation was significantly lower in SIBO‐positive patients. SIBO patients were more likely to have reduced anal relaxation ( p = 0.032), but no other ARM parameters or BET > 60 s. At ≥ 10 3 CFU/mL, a more negative recto‐anal pressure differential (RAPD) was observed, along with a combination of RAPD < −45 mmHg and resting anal pressure > 90 mmHg. 637 patients underwent breath testing for MO, with 174 positive results, predominantly showing IMO (73%). In this group, BET was significantly longer, and anal relaxation was significantly lower. SIBO at ≥ 10 3 CFU/mL was more prevalent in DD than STC (85.5% vs. 64.7%, p = 0.002). IMO was more common in DD than STC ( p = 0.021). Conclusion/Inferences DD may be a risk factor for MO, often with evidence of methanogenesis.

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.

ABSTRACT Antimicrobial‐resistant bacteria, a growing worldwide concern, reduce the effectiveness of antibiotics against a wide range of microbial infections. Various bacterial species have quickly developed antibiotic resistance since the first mention of penicillin resistance in 1947. A rise in mortality, more extended hospital stays, more healthcare expenditures, and morbidity are all brought about by these bacteria that are resistant to antibiotics. To develop resistance, bacteria may undergo genetic changes, engage in horizontal gene transfer, produce β‐lactamase, activate efflux pumps, form biofilms, and alter their metabolism to become less susceptible to drugs. Environmental factors and sublethal antibiotic exposure exacerbate resistance, particularly in cases of persistent infections caused by biofilms. This tendency is prompted by the overuse of antibiotics in both human and veterinary medicine, as well as inadequate infection control measures and environmental pollution. This review presents an extensive survey of antimicrobial resistance across bacterial taxa, with a focus on the physiological and genetic processes underlying this phenomenon. It delves into the current therapeutic landscape and showcases cutting‐edge methods—such as artificial intelligence‐driven antibiotic discovery and resistance prediction—to inform the development of next‐generation antibiotics and containment systems.

With rapid industrial expansion, air pollution’s adverse neurological effects have gained increasing attention. Children face a greater risk of neurological damage because of their higher breathing rates, developing brains, and limited ability to detoxify harmful substances. Fine particulate matter has been identified as a primary neurotoxic contributor affecting developing brains. Strong evidence connects environmental pollutant exposure to the prevalence of Autism Spectrum Disorder (ASD), a neurodevelopmental condition marked by lasting difficulties with social communication and interaction. This study explores the association between long-term PM2.5 exposure and ASD symptom exacerbation, investigating underlying mechanisms. We hypothesize that long-term PM2.5 exposure exacerbates ASD symptoms through neuroinflammatory activation, leading to neuronal damage and impaired synaptic plasticity. Our investigation employs three complementary approaches: First, integrated analysis combining Global Burden of Disease data with Mendelian randomization demonstrates a significant association between PM2.5 exposure and increased ASD severity risk. Second, utilizing the China High-Resolution Air Pollution Database in conjunction with cohort studies, we provide evidence that ambient air pollution substantially influences autism severity, with PM2.5 identified as the predominant environmental determinant. Third, through network toxicology, single-cell transcriptomics, and animal experimentation, we demonstrate that chronic PM2.5 exposure exacerbates valproic acid-induced autism-like behaviors in murine models, identifying CTNNB1, PTEN, CCR2, AKT1, and mTOR as potential core mediating genes. Importantly, these findings represent preliminary results. Several potential confounding factors such as co-exposure to other pollutants and socioeconomic variables have not been fully addressed. While our multi-modal approach provides converging lines of evidence, further validation in larger, more diverse populations with refined control of confounders will be essential to establish causality and elucidate mechanisms. Nonetheless, these early insights advance our understanding of PM2.5-induced neurotoxicity in the context of ASD and offer timely, albeit preliminary, evidence to inform public health policy.