Cost-effectiveness analysis of regorafenib dose optimization for refractory metastatic colorectal cancer

BACKGROUND

New regimens have emerged as third-line or later therapies for metastatic colorectal cancer (mCRC), including regorafenib dose optimization (ReDO), trifluridine/tipiracil and bevacizumab (TAS-BEV) combination therapy, and fruquintinib. We evaluated relative cost-effectiveness of these therapies in patients with mCRC from a US payer's perspective.

MATERIALS AND METHODS

A partitioned survival model (PSM) was constructed to estimate total costs and quality-adjusted life years (QALYs). Clinical parameters were obtained from pivotal trials of the respective therapies and incremental cost-effectiveness ratios (ICERs) were estimated to assess relative cost-effectiveness of these treatments. Model robustness was assessed using deterministic (DSA) and probabilistic sensitivity analysis (PSA). Three scenario analyses were conducted: (1) assuming equal efficacy across treatments, (2) with prior exposure to anti-vascular endothelial growth factor (VEGF) therapy, and (3) alternative clinical inputs for fruquintinib from a different clinical trial.

RESULTS

Under the conventional willingness-to-pay (WTP) threshold in US ($150,000 per QALY gained), ReDO was cost-effective when compared with TAS-BEV and was dominant over fruquintinib. TAS-BEV was associated with an incremental QALY of 0.197 over ReDO, resulting in an ICER at $554,567 per QALY gained. The base case results were robust in DSA and PSA. Most influential parameters were treatment cost and effectiveness. In patients with prior anti-VEGF therapy, ReDO remained cost-effective compared to TAS-BEV and fruquintinib under the conventional WTP threshold.

LIMITATION

Differences in trial populations may affect the comparability of the outcomes. Sensitivity and scenario analyses were conducted to address these limitations.

CONCLUSION

ReDO was cost-effective compared with TAS-BEV from the US payer's perspective despite a higher QALY gain associated with TAS-BEV. ReDO was dominant over fruquintinib, consistently having a higher QALY gain and lower cost.

Cost-effectiveness of PD-1 inhibitors combined with chemotherapy for first-line treatment of oesophageal squamous cell carcinoma in China: a comprehensive analysis

BACKGROUND

Programmed death-1 (PD-1) inhibitors combined with chemotherapy have become a standard first-line treatment for advanced oesophageal squamous cell carcinoma (ESCC). Given the high costs associated with immunotherapy, evaluating the cost-effectiveness of different PD-1 inhibitors in the Chinese healthcare setting is essential for guiding treatment decisions and policy development.

METHODS

A cost-effectiveness analysis was conducted comparing six PD-1 inhibitors-sintilimab, toripalimab, tislelizumab, camrelizumab, serplulimab, and pembrolizumab-combined with chemotherapy for first-line treatment of advanced ESCC. A partitioned survival model was used to calculate incremental cost-effectiveness ratios (ICERs) from healthcare system perspective, with a willingness-to-pay (WTP) threshold set at $36,598.19 per quality-adjusted life year (QALY). Sensitivity analyses were performed to evaluate the robustness of the results.

RESULTS

The ICERs for toripalimab, camrelizumab, pembrolizumab, serplulimab, sintilimab, and tislelizumab were $32,356.79/QALY, $48,410.64/QALY, $312,743.54/QALY, $121,200.84/QALY, $29,663.42/QALY, and $35,304.33/QALY, respectively. Sintilimab, toripalimab, and tislelizumab were below the WTP threshold. Among all regimens, the top three in life years (LYs) gained were toripalimab, serplulimab, and tislelizumab. Sensitivity analysis showed that utility values and drug prices were key factors influencing ICERs. Probabilistic analysis indicated that toripalimab, sintilimab, and tislelizumab had the highest probabilities of being cost-effective, at 83.1%, 81.4%, and 70.0%, respectively.

CONCLUSION

Sintilimab, toripalimab, and tislelizumab are the most cost-effective PD-1 inhibitors when combined with chemotherapy for the first-line treatment of advanced ESCC in China, with ICERs below the WTP threshold. While all six PD-1 inhibitors demonstrated clinical benefits, pembrolizumab and serplulimab were less favourable from a cost-effectiveness standpoint. Sensitivity analysis confirmed that drug prices and utility values are significant determinants of cost-effectiveness.

Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) is a fatal disease, and radioresistance is an important factor leading to treatment failure and disease progression. The objective of this research was to detect radioresistance-related genes (RRRGs) with prognostic value in NSCLC.

METHODS

The weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were performed to identify RRRGs using expression profiles from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. Then, Immune landscape and drug sensitivity were evaluated. The biological functions exerted by the key gene LBH were verified by in vitro experiments.

RESULTS

Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2 and TSPAN2) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. The immune landscape and sensitivity to anti-tumour drugs showed significant disparities between patients categorized into different risk score subgroups. In vitro experiments indicated that overexpression of LBH enhanced the radiosensitivity of A549 cells, and knockdown LBH reversed the cytotoxicity induced by X-rays.

CONCLUSION

Our study developed an eight-gene risk score model with potential clinical value that can be adopted for choice of drug treatment and prognostic prediction. Its clinical routine use may assist clinicians in selecting more rational practices for individuals, which is important for improving the prognosis of NSCLC patients. These findings also provide references for the development of potential therapeutic targets.

Integration of transcriptome and Mendelian randomization analyses in exploring the extracellular vesicle-related biomarkers of diabetic kidney disease

BACKGROUND

Diabetic Kidney Disease (DKD) is a common complication in patients with diabetes, and its pathogenesis remains incompletely understood. Recent studies have suggested that extracellular vesicles (EVs) may play a significant role in the initiation and progression of DKD. This study aimed to identify biomarkers associated with EVs in DKD through bioinformatics and Mendelian randomization (MR) analysis.

METHODS

This study utilized two DKD-related datasets, GSE96804 and GSE30528, alongside 121 exosome-related genes (ERGs) and 200 inflammation-related genes (IRGs). Differential analysis, co-expression network construction, and MR analysis were conducted to identify candidate genes. Machine learning techniques and expression validation were then employed to determine biomarkers. Finally, the potential mechanisms of action of these biomarkers were explored through Immunohistochemistry (IHC) staining, enrichment analysis, immune infiltration analysis, and regulatory network construction.

RESULTS

A total of 22 candidate genes were identified as causally linked to DKD. CMAS and RGS10 were identified as biomarkers, with both showing reduced expression in DKD. IHC confirmed low RGS10 expression, providing new insights into DKD management. CMAS was involved primarily in mitochondria-related pathways, while RGS10 was enriched in the extracellular matrix and associated pathways. Significant differences were observed in neutrophils and M2 macrophages between DKD and normal groups, correlating strongly with the biomarkers.

CONCLUSION

This study identified two EV-associated biomarkers, CMAS and RGS10, linked to DKD and elucidated their potential roles in disease progression. These results offer valuable insights for further exploration of DKD pathogenesis and the development of new therapeutic targets.

Neural dynamics of deception: insights from fMRI studies of brain states

Deception is a complex behavior that requires greater cognitive effort than truth-telling, with brain states dynamically adapting to external stimuli and cognitive demands. Investigating these brain states provides valuable insights into the brain's temporal and spatial dynamics. In this study, we designed an experiment paradigm to efficiently simulate lying and constructed a temporal network of brain states. We applied the Louvain community clustering algorithm to identify characteristic brain states associated with lie-telling, inverse-telling, and truth-telling. Our analysis revealed six representative brain states with unique spatial characteristics. Notably, two distinct states-termed truth-preferred and lie-preferred-exhibited significant differences in fractional occupancy and average dwelling time. The truth-preferred state showed higher occupancy and dwelling time during truth-telling, while the lie-preferred state demonstrated these characteristics during lie-telling. Using the average z-score BOLD signals of these two states, we applied generalized linear models with elastic net regularization, achieving a classification accuracy of 88.46%, with a sensitivity of 92.31% and a specificity of 84.62% in distinguishing deception from truth-telling. These findings revealed representative brain states for lie-telling, inverse-telling, and truth-telling, highlighting two states specifically associated with truthful and deceptive behaviors. The spatial characteristics and dynamic attributes of these brain states indicate their potential as biomarkers of cognitive engagement in deception.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-025-10222-4.

Integrated bioinformatics analysis identifies neutrophils and villous cytotrophoblasts infiltration characterized by BLC6 upregulation as associated with the co-occurrence of gestational diabetes mellitus and pre-eclampsia

BACKGROUND

Gestational diabetes mellitus (GDM) patients are one of the important high-risk groups for the development of pre-eclampsia (PE). The pathogenesis of PE in GDM patients is not fully understood. This study aims to identify hub genes and pathways associated with the co-occurrence of GDM and PE.

METHODS

The matrix files of GDM and PE datasets were downloaded from GEO to identify differentially expressed genes (DEGs). The common DEGs were predicted for functional analysis through GO and KEGG analysis. A protein-protein interaction (PPI) network was constructed to determine the common hub genes for GDM and PE. Diagnostic hub genes were obtained through regression modeling and ROC analysis, and validated in publicly available datasets. The differences in immune infiltration between GDM and PE were analyzed. The expression and role of common hub genes in the co-occurrence of GDM and PE were explored through analysis of single-cell sequencing data.

RESULTS

A total of 104 DEGs were identified between GDM and PE. These common DEGs were found to be involved in mucosal immune response and the JAK-STAT signaling pathway. A total of 27 common hub genes were identified for both GDM and PE. BCL6, DNAH9, and SCG2 were identified as potential diagnostic biomarkers for PE. BCL6 showed high expression in neutrophils and villous cytotrophoblasts (VCTs) in both PE and GDM.

CONCLUSION

This study identified BCL6, DNAH9, and SCG2 as common hub genes in GDM and PE. BCL6 is expected to be a new target for the diagnosis and treatment of GDM concurrent with PE.

Reutericyclin, a specialized metabolite of Limosilactobacillus reuteri, mitigates risperidone-induced weight gain in mice

The role of xenobiotic disruption of microbiota, corresponding dysbiosis, and potential links to host metabolic diseases are of critical importance. In this study, we used a widely prescribed antipsychotic drug, risperidone, known to influence weight gain in humans, to induce weight gain in C57BL/6J female mice. We hypothesized that microbes essential for maintaining gut homeostasis and energy balance would be depleted following treatment with risperidone, leading to enhanced weight gain relative to controls. Thus, we performed metagenomic analyses on stool samples to identify microbes that were excluded in risperidone-treated animals but remained present in controls. We identified multiple taxa including Limosilactobacillus reuteri as a candidate for further study. Oral supplementation with L. reuteri protected against risperidone-induced weight gain (RIWG) and was dependent on cellular production of a specialized metabolite, reutericyclin. Further, synthetic reutericyclin was sufficient to mitigate RIWG. Both synthetic reutericyclin and L. reuteri restored energy balance in the presence of risperidone to mitigate excess weight gain and induce shifts in the microbiome associated with leanness. In total, our results identify reutericyclin production by L. reuteri as a potential probiotic to restore energy balance induced by risperidone and to promote leanness.

Machine learning-based integration develops a disulfidptosis-related lncRNA signature for improving outcomes in gastric cancer

Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need for innovative prognostic methods. Disulfidptosis, a recently discovered programmed cell death triggered by disulphide stress, presents a fresh avenue for therapeutic exploration. This research examines disulfidptosis-related long noncoding RNAs (DRLs) in gastric cancer, with the goal of leveraging these lncRNAs as potential markers to enhance patient outcomes and treatment approaches. Comprehensive genomic and clinical data from stomach adenocarcinoma (STAD) were obtained from The Cancer Genome Atlas (TCGA). Employing least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic model was devised incorporating five key DRLs to forecast survival rates. The effectiveness of this model was validated using Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, and extensive functional enrichment studies. The importance of select lncRNAs and the expression variability of genes tied to disulfidptosis were validated via quantitative real-time PCR (qRT-PCR) and Western blot tests, establishing a solid foundation for their prognostic utility. Analyses of functional enrichment and tumour mutation burden highlighted the biological importance of these DRLs, connecting them to critical cancer pathways and immune responses. These discoveries broaden our comprehension of the molecular framework of gastric cancer and bolster the development of tailored treatment plans, highlighting the substantial role of DRLs in clinical prognosis and therapeutic intervention.

In vitro to in vivo extrapolation modeling to facilitate the integration of transcriptomics data into genotoxicity assessment

In vitro transcriptomics holds promise for high-throughput, human-relevant data but is not yet integrated into regulatory decision-making due to the lack of standardized approaches. For genotoxicity assessment, transcriptomic biomarkers such as GENOMARK and TGx-DDI facilitate qualitative and quantitative analysis of complex in vitro transcriptomic datasets. However, advancing their use in quantitative testing requires standardized methods for deriving transcriptomic Points of Departure (tPoDs) and linking them to in vivo responses. Herein, we investigated different approaches to calculate tPoDs and applied in vitro to in vivo extrapolation to obtain administered equivalent doses (AEDs). Human HepaRG cells were exposed for 72 h to 10 known in vivo genotoxicants (glycidol, methyl methanesulfonate, nitrosodimethylamine, 4-nitroquinoline-N-oxide, aflatoxin B1, colchicine, cyclophosphamide, mitomycin C, ethyl methanesulfonate, and N-Nitroso-N-ethylurea) from the highest concentration that induces up to 50 % cytotoxicity through a range of lower concentrations. Gene expression data was generated using a customized version of the TempO-Seq® human S1500 + gene panel. The GENOMARK and TGx-DDI biomarkers produced genotoxic calls for all of these reference genotoxicants. Next, we performed benchmark concentration (BMC) modeling to generate both genotoxicity-specific biomarker (tPoDbiomarkers) and generic tPoDs (tPoD S1500+). High-throughput toxicokinetic models estimated the human AEDs for these tPoDs, which were compared with (a) previously reported genotoxicity-specific AEDs from other New Approach Methodologies, and (b) in vivo PoDs from animal studies. We found that the generic AEDs were more conservative than genotoxicity-specific biomarker AEDs. For six of the nine genotoxicants, transcriptomic AEDs were lower than the in vivo PoDs; refined kinetic models may improve predictions. Overall, in vitro transcriptomic data in HepaRG cells provide protective estimates of in vivo genotoxic concentrations, consistent with other in vitro genotoxicity testing systems.

Morphometric analysis of the sperm midpiece during capacitation

In mammalian sperm, mitochondria are very densely packed and form a helical sheath in the midpiece of the flagellum. Mitochondria from somatic cells can rapidly change shape to adapt to environmental conditions. During capacitation, mammalian spermatozoa undergo morphological and physiological changes to acquire fertilization ability, evidenced by changes in sperm motility patterns (hyperactivation) and the ability to perform the acrosome reaction. Whether there are changes in sperm mitochondrial morphology during capacitation is unknown. This work aimed to quantify morphometric changes in the sperm midpiece during capacitation. Using mitochondrial fluorescent probes and a combination of freely available software, we quantified the dimensions and fluorescence intensity of the midpiece. After capacitation, the area occupied by the mitochondria decreased due to a reduction in the width but not the length of the midpiece. The decrease in the area of the midpiece occurred in spermatozoa that underwent the acrosome reaction, suggesting a reorganization of the mitochondria during capacitation. Ultrastructural analysis supported these results. The application of image processing to fluorescence microscopy images may help to identify morphological changes during capacitation.

Interpretation of thyroid-relevant bioactivity data for comparison to in vivo exposures: A prioritization approach for putative chemical inhibitors of in vitro deiodinase activity

Many ToxCast assay endpoints can be mapped to molecular initiating events (MIEs) within the thyroid adverse outcome pathway (AOP) network. Herein, we provide a framework for interpretation of thyroid-relevant bioactivity data across MIEs. As a proof-of-concept, we used ToxCast data on the inhibition of deiodinase (DIO) enzymes, which convert thyroid hormones between active and inactive forms, and identified substances most likely to inhibit DIO enzymes. Data from 4 relevant cell-free in vitro assays are available for > 2000 chemicals in single concentration screening and 327 chemicals in multi-concentration screening. We filtered to identify chemicals that demonstrated inhibition for each DIO enzyme less likely to be confounded by assay interference, refining the list of putatively active chemicals from 523 to 135. In vitro bioactivity data were then used to estimate administered equivalent doses (AEDs) using a novel high-throughput toxicokinetic (HTTK) model for in vitro to in vivo extrapolation (IVIVE) of dose. To consider potential thyroid-disrupting activity in an appropriate life-stage and dose context, we extended an existing human maternal-fetal HTTK model to allow for simulations involving the first trimester of pregnancy. For many chemicals, using modeled fetal tissue concentrations produced lower AED estimates than using modeled maternal plasma concentrations alone, at least partially due to conservative assumptions in our HTTK model of complete gestation. This extensible approach for MIE groups of thyroid-related bioactivity data from ToxCast may inform further screening or analyses for potential adverse outcomes during pregnancy and development.

Unraveling the core symptoms across distinct trajectories of problematic Internet use among 27,577 adolescents: Cross-lagged panel network analyses

Problematic Internet Use (PIU) often begins in adolescence and develops along diverse trajectories. Understanding the interaction between PIU symptoms and identifying the core PIU symptoms that drive distinct trajectories among adolescents are crucial. We employed cross-lagged panel network analyses to examine temporal relationships of PIU symptoms among Chinese adolescents across four developmental trajectories over two time points. A total of 27,577 adolescents (Mean age = 13.8, SD = 1.5) participated in this study. Demographic variables and PIU symptoms were collected from December 17 to 26, 2021 (T1) and from May 17 to June 5, 2022 (T2). The symptom "Reluctant to stop" in the resilient group and "Uncontrollable checking" in the alleviating group at T1 were most predictive of remission of other symptoms at T2, while "Empty life" in the deteriorating group and "Feeling of missing" in the persistent dysfunction group were most strongly associated with worsening of other PIU symptoms. Improving self-control is likely to promote healthy and beneficial Internet use among adolescents, while enhancing social connection and fostering exercise habits may help mitigate negative emotions and reduce the emergence of additional PIU symptoms among adolescents who have developed PIU.

Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset

BACKGROUND

Endometriosis is a clinical condition characterized by the presence of endometrial glands outside the uterine cavity. While its incidence remains mostly uncertain, endometriosis impacts around 180 million women worldwide. Despite the presentation of several epidemiological and clinical explanations, the precise mechanism underlying the disease remains ambiguous. In recent years, researchers have examined the hereditary dimension of the disease. Genetic research has aimed to discover the gene or genes responsible for the disease through association or linkage studies involving candidate genes or DNA mapping techniques.

AIM

To identify genetic biomarkers linked to endometriosis by the application of machine learning (ML) approaches.

METHODS

This case-control study accounted for the open-access transcriptomic data set of endometriosis and the control group. We included data from 22 controls and 16 endometriosis patients for this purpose. We used AdaBoost, XGBoost, Stochasting Gradient Boosting, Bagged Classification and Regression Trees (CART) for classification using five-fold cross validation. We evaluated the performance of the models using the performance measures of accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1 score.

RESULTS

Bagged CART gave the best classification metrics. The metrics obtained from this model are 85.7%, 85.7%, 100%, 75%, 75%, 100% and 85.7% for accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1 score, respectively. Based on the variable importance of modeling, we can use the genes CUX2, CLMP, CEP131, EHD4, CDH24, ILRUN, LINC01709, HOTAIR, SLC30A2 and NKG7 and other transcripts with inaccessible gene names as potential biomarkers for endometriosis.

CONCLUSION

This study determined possible genomic biomarkers for endometriosis using transcriptomic data from patients with/without endometriosis. The applied ML model successfully classified endometriosis and created a highly accurate diagnostic prediction model. Future genomic studies could explain the underlying pathology of endometriosis, and a non-invasive diagnostic method could replace the invasive ones.

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder

BACKGROUND

Early diagnosis and treatment of mental illnesses is hampered by the lack of reliable markers. This study used machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD).

METHODS

Case-control samples (aged 18-25 years), including participants with Anorexia Nervosa (AN), Bulimia Nervosa (BN), MDD, AUD, and matched controls, were used for diagnostic classification. For risk prediction, we used a longitudinal population-based sample (IMAGEN study), assessing adolescents at ages 14, 16 and 19. Regularized logistic regression models incorporated broad data domains spanning psychopathology, personality, cognition, substance use, and environment.

RESULTS

The classification of EDs was highly accurate, even when excluding body mass index from the analysis. The area under the receiver operating characteristic curves (AUC-ROC [95 % CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. In the longitudinal population sample, the models exhibited moderate performance in predicting the development of future ED symptoms (0.71 [0.67-0.75]), depressive symptoms (0.64 [0.60-0.68]), and harmful drinking (0.67 [0.64-0.70]).

CONCLUSIONS

Our findings demonstrate the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.

Cross-species comparisons of plasma binding and considerations for data evaluation

The US Environmental Protection Agency is increasingly employing new approach methods (NAMs), including in vitro plasma binding and hepatocyte clearance experiments to collect chemical-species specific data. This paper presents data from plasma binding experiments using rapid equilibrium dialysis (RED) devices and plasma from humans, rats, and rainbow trout with a 4-h incubation time. A total of 54 chemicals, utilizing two concentrations, were tested across the three species resulting in 238 chemical-species specific datasets. Mass balance controls for chemical plasma stability and dialysis system recovery were used to evaluate the datasets and almost 40 % of the datasets (92/238 datasets) produced quantitative measurements. Cross-species comparisons and evaluations of the impact of physicochemical properties on chemical-assay performance were also evaluated. Comparisons of human-rat plasma binding revealed rat plasma generally demonstrated higher fup values for chemicals than human. While fup values in trout plasma were frequently lower than rat or human plasma. A comparison with literature data was performed and correlations between plasma binding, expressed as fraction unbound in plasma (fup), and log Kow across all three species indicate that the strongest relationship occurs at log Kow values between 1.5 and 4. The obtained datasets exhibited a wide range of behaviors, emphasizing the need for a robust approach to data quality assessment. The broader analysis of fup values indicates that chemicals with log Kow > 4.5 will be highly bound (fup ≤ 0.0001), difficult to measure, and have low reproducibility across laboratories, suggesting that use of different methods may be needed across different physicochemical properties.

Protective and risk factors in problematic mobile phone use among adolescents: A three-wave longitude study

Adolescents' problematic mobile phone use (PMPU) has become increasingly severe. This study examined the changes in relationships between dimensions of PMPU, protective (positive psychological capital, i.e., optimism, hope, resilience, core self-evaluation) and risk (psychological distress, i.e., anxiety, depression, loneliness, and stress) factors within the network, aiming to identify the most influential factors over time and find the longitudinal predictive relationships between the factors and PMPU. A total of 1,170 Chinese adolescents participated over three waves (T1: January 2023, T2: August 2023, T3: February 2024). Cross-section network analysis showed that "core self-evaluation", "depression", "hope", "loss of control", and "stress_P" were the central nodes. "Stress_N" (sense of losing control and negative affective reactions) in risk factors and "affect control" (ability to regulate emotions) in protective factors were the bridge symptoms in the network across three timepoints. As shown in network comparison, the global strength of the network remained stable from T1 to T2 but increased from T2 to T3. The edge strength between "family support", "anxiety" and the nodes of PMPU weakened across the time. While, correlations between "loneliness", "goal planning", "positive thinking", "affect control" and PMPU nodes strengthened. The relationship between "Stress_N" and PMPU initially increasing before decreasing. Longitudinal cross-lagged network analysis revealed that "negative life consequence" and "craving" in PMPU strongly predicted protective/risk factors, while "hope," "affect control," and "core self-evaluation" were most susceptible to prediction. The findings highlight the significant role of "core self-evaluation" and "stress_N" in the development of adolescents' PMPU and the negative results of PMPU. Additionally, the changes in the network over time suggest that the factors influencing PMPU evolve, with various protective/risk factors gaining or losing significance at different stages. The results of CLPN emphasize the negative outcome of PMPU. Therefore, targeting interventions on the internalized symptoms may help alleviate the severity of PMPU among adolescents.

A GATE Monte Carlo study on ICRP110 phantoms for BNCT dosimetry evaluation

Boron Neutron Capture Therapy (BNCT) is attracting renewed attention due to advancements in compact proton accelerators for the production of neutron beams, and new BNCT facilities are being planned all around the world. A key aspect in BNCT treatments will be patient dosimetry, particularly given the complex radiation field created by neutron interactions with biological tissues. This study aimed at developing a prototype of BNCT dosimetry workflow based on Monte Carlo (MC) simulations for the GATE toolkit. Investigating the feasibility of performing voxel-level dosimetry through full MC transport in terms of simulation time and statistical uncertainties, the ICRP110 male and female adult voxelized phantoms were used to model the human body, adding the possibility to set in their organs user-defined concentrations of 10B. Irradiation simulations of the head district with two monoenergetic neutron beams and with a realistic clinical neutron spectrum were carried out. The absorbed dose matrices for each simulation, assuming both no 10B and then a systemic distribution of 15 ppm, were scored separating the contributions from 7Li, alpha particles, protons and photons. Results showed the expected increase, in presence of 10B distribution, of the 7Li and alpha average dose components in organs of interest of the head, such as brain, reaching in it about 1.3 and 2.3 fGy/evt, respectively, in presence of 15 ppm of 10B. The present prototype of dosimetric workflow, whose macros and files are freely shared for interested users and developers, will serve as a basis for future studies aiming at simulating similar BNCT scenarios with larger statistics, for example by exploiting high computing resources, to verify the obtained results with lower statistical uncertainties and possibly optimize the workflow to reduce simulation times while ensuring suitable dosimetric accuracy.

Events-Affect-Personality: A Daily Diary Investigation of the Mediating Effects of Affect on the Events-Personality Relationship

Our 10-day diary investigation anchored in dynamic personality theories, such as Whole Trait Theory examined (a) whether within-person variability in two broad personality traits Extraversion and Neuroticism is consistently predicted by daily events, (b) whether positive and negative affect, respectively partly mediate this relationship and (c) the lagged relationships between events, and next day variations in affect and personality. Results revealed that personality exhibited significant within-person variability, that positive and negative affect partly mediate the relationship between events and personality, affect accounting for up to 60% of the effects of events on personality. Additionally, we identified that event-affect congruency was accountable for larger effects compared to event-affect non-congruency.

Incorporation of Epstein-Barr viral variation implicates significance of Latent Membrane Protein 1 in survival prediction and prognostic subgrouping in Burkitt lymphoma

Although Epstein-Barr virus (EBV) plays a role in Burkitt lymphoma (BL) tumorigenesis, it is unclear if EBV genetic variation impacts clinical outcomes. From 130 publicly available whole-genome tumor sequences of EBV-positive BL patients, we used least absolute shrinkage and selection operator (LASSO) regression and Bayesian variable selection models within a Cox proportional hazards framework to select the top EBV variants, putative driver genes, and clinical features associated with patient survival time. These features were incorporated into survival prediction and prognostic subgrouping models. Our model yielded 22 EBV variants, including seven in latent membrane protein 1 (LMP1), as most associated with patient survival time. Using the top EBV variants, driver genes, and clinical features, we defined three prognostic subgroups that demonstrated differential survival rates, laying the foundation for incorporating EBV variants such as those in LMP1 as predictive biomarker candidates in future studies.

The relationship between radiological OA severity or body weight and outcomes following a structured education and exercise therapy program (GLA:D®) for people with knee osteoarthritis

BACKGROUND

Clinicians may presume people with higher bodyweight or greater OA severity do not respond to exercise therapy for knee osteoarthritis (OA), but few studies have examined this.

OBJECTIVE

To examine the relationship between radiographical OA severity or bodyweight and pain and functional outcomes following a structured education and exercise therapy program (Good Life with OsteoArthritis from Denmark: GLA:D®).

METHODS

33 participants with knee OA were assessed at baseline and week 8 following GLA:D®. Outcomes were pain (Visual analogue scale (VAS) 0-100), Knee Injury and Osteoarthritis Outcome Score-12 (KOOS-12 total), 40 m-fast-paced walk and 30-s chair stand. Multilevel models were used to define the severity of OA in medial, lateral and patellofemoral compartments using the Kellgren-Lawrence (KL) system and to examine the relationship between compartment severity, bodyweight and outcomes.

RESULTS

No meaningful relationships between bodyweight and response to GLA:D® were found for any outcome measures. Greater medial OA compartment severity was related to less improvement in pain, KOOS-12 and chair stand repetitions. However, all levels of lateral compartment severity had similar improvements, and greater patellofemoral compartment severity was related to more improvement for KOOS-12 and pain.

CONCLUSION

Bodyweight may have little influence on a person's response to a structured education and exercise therapy program. While people with greater medial compartment severity were less likely to improve following the program, OA severity in the PF and lateral compartments was not a barrier to improvement.

Which should be targeted first? The comorbidity of sleep disturbances and anxiety symptoms among adolescents: Cross-sectional and longitudinal network analyses

BACKGROUND

The comorbidity of sleep disturbances and anxiety is well-established, but which symptoms to prioritize for intervention remains unclear. Academic stress, a key factor worsening both, is common among adolescents facing growing academic demands. Understanding how it affects both comorbidities from a symptomatology perspective is crucial for developing targeted interventions.

METHODS

The longitudinal survey of 34,494 adolescents was conducted twice (Mage = 12.89 [1.76] years). Adolescents were categorized into three groups based on academic stress scores' mean ± 1 standard deviation. Then, we examined the moderating effect of academic stress on the relationship between sleep disturbances and anxiety symptoms longitudinally by comparing groups' network structures, where nodes represent symptoms, and edge thickness reflects the associations' strength.

RESULTS

In the cross-sectional network, at both time points, the key bridge symptom is "sleep quality" (bridge Expected Influence (bEI) at T1 = 0.21; bEI at T2 = 0.20). In the longitudinal network, "irritability" (bEI = 0.21) and "sleep quality" (bEI = 0.21) are key bridge symptoms in the whole sample, with "sleep quality" (bEI = 0.36) most prominent in the high stress group. In the low stress group, it's "difficulty initiating sleep" (bEI = 0.14). Additionally, compared to the low group, adolescents with high academic stress possess more tightly connected relationships (edge weights ≥0.05) not only within single disorder symptom networks but also between two disorder symptom networks (number of edges: 2 vs 34).

CONCLUSION

These findings highlight the moderating role of academic stress, offering insights for targeted interventions to improve adolescent mental health.

Rethinking statistical approaches for serological data analysis for viral surveillance

A robust serological surveillance system for zoonotic pathogens is imperative for both early detection and advancing knowledge of emerging diseases. A statistical analysis plan that is aligned to research and epidemiological goals requires a purposeful choice among alternative methods for differentiating seronegative from seropositive samples, estimating seroprevalence, and estimating risk factors associated with seropositivity. The common standard deviation-based cutoff (e.g., 3sd) approach is simple to implement and understand, but fails to appropriately propagate uncertainty in serostatus assignments to any risk factor analysis. Methods such as Gaussian mixture models, which jointly estimate serostatus, risk factors, and their uncertainty, can alleviate the dichotomy created by the cutoff approach. Yet, because of a lack of empirical guidance of method performance, it remains difficult to choose a robust analysis method for a given serological dataset. Here we examine the performance of both cutoff and clustering approaches using simulated datasets that represent the epidemiological, biological, and immunological data generation process. We focus on understudied pathogens for which validated serological assays do not exist, as is common in emerging viruses in wildlife. We quantify coverage (the proportion of time 95 % confidence intervals contain the true value) and bias (systematic differences between true values and model point estimates) of model estimates for individual serostatus assignments, population seroprevalence, and regression coefficients for serostatus risk factors. In nearly all scenarios, Bayesian mixture models provide the highest coverage and lowest bias. Only with very low seroprevalence (∼ < 3 %) and large differences in signal between seronegative and seropositive individuals will a cutoff provide low bias and near-nominal coverage. Given poor coverage of risk factor regression coefficients, we advise against using a cutoff approach for quantifying determinants of seropositivity.

The Association of Mental Health, Asthma Control and Acute Care Visits Among Rural Adolescents with Poorly Controlled Asthma

Anxiety and depressive symptoms are associated with asthma-related acute care utilization. Few studies include rural adolescents. Asthma control may be the mechanism by which mental health affects acute care. This study explored associations between generalized anxiety, asthma-related anxiety, depressive symptoms, and acute care visits, and tested if asthma control mediates these associations among 197 rural adolescents with asthma. Data analysis included descriptive statistics and regression. Controlling for age, sex and race/ethnicity, asthma-related anxiety was associated with higher odds of acute care visits (OR = 2.09, 95% CI [1.42, 3.07]). Asthma control mediated this relationship: one unit increase in anxiety, on average, increased the odds of having any acute care visit by 5%. Generalized anxiety and depressive symptoms were not associated with acute care visits. Helping adolescents reduce their concerns regarding asthma while improving their self-management skill may potentially to reduce acute care among rural adolescents.

Smartphone application-delivered cognitive behavioural therapy for insomnia with telephone support for insomnia disorder compared to a waitlist control: a randomised clinical trial

Although there have been promising findings for smartphone application (app)-delivered cognitive behavioural therapy for insomnia (CBT-I), previous trials have not screened participants rigorously for insomnia disorder and used therapist support. Based on the above, we aimed to examine the effects of smartphone app-delivered CBT-I with telephone support against a waitlist (WL) in a sample with insomnia disorder. A total of 64 participants with insomnia disorder were randomised to smartphone app-delivered CBT-I (n = 32) or a WL (n = 32). Smartphone app-delivered CBT-I consisted of six weekly smartphone app modules with 15 min of telephone support per week. At pre- and post-treatment, and the 3-month follow-up, we assessed insomnia symptoms and associated correlates and consequences. At post-treatment, we also assessed measures related to adherence (therapist support, exercise/module completion), self-rated perception of treatment content, activity, and adverse events. CBT-I significantly outperformed the WL with large effects on the primary outcome (d = 2.26) and was significantly different on most of the secondary outcomes with medium to large effects. CBT-I also resulted in a significantly larger proportion of treatment remitters (CBT-I: 64.5-77.4%, WL: 6.5-6.9%) and responders (CBT-I: 77.4-90.3%, WL: 19.4-24.1%) at post-treatment and follow-up, compared to the WL. Treatment was associated with high satisfaction, high adherence, low attrition, and few treatment-impeding adverse events. Based on the medium to large effects of smartphone app-delivered CBT-I with telephone support, this trial highlights the potential of delivering CBT-I exclusively through an app with therapist telephone support for high efficacy, satisfaction, and adherence.

Fast aberration correction in 3D transcranial photoacoustic computed tomography via a learning-based image reconstruction method

Transcranial photoacoustic computed tomography (PACT) holds significant potential as a neuroimaging modality. However, compensating for skull-induced aberrations in reconstructed images remains a challenge. Although optimization-based image reconstruction methods (OBRMs) can account for the relevant wave physics, they are computationally demanding and generally require accurate estimates of the skull's viscoelastic parameters. To circumvent these issues, a learning-based image reconstruction method was investigated for three-dimensional (3D) transcranial PACT. The method was systematically assessed in virtual imaging studies that involved stochastic 3D numerical head phantoms and applied to experimental data acquired by use of a physical head phantom that involved a human skull. The results demonstrated that the learning-based method yielded accurate images and exhibited robustness to errors in the assumed skull properties, while substantially reducing computational times compared to an OBRM. To the best of our knowledge, this is the first demonstration of a learned image reconstruction method for 3D transcranial PACT.

The interplay between COVID-19 and heart disease: Unravelling a complex connection

The intersection of coronavirus (COVID-19) and heart disease has emerged as a critical nexus in the landscape of global health. Individuals with heart disease face elevated risks when infected with Severe Acute Respiratory-Syndrome Coronavirus-type-2 (SARS-CoV-2) leading to COVID-19. The virus can directly affect the heart, resulting in myocarditis, arrhythmias, and heart failure, even in individuals without prior medical cardiac history. Therefore, tools identifying patients with cardiac infestation and predicting disease severity are of utmost importance. This study's unbiased stratification of clinical and immunological parameters of 134 SARS-CoV-2 positive patients revealed clusters of course-severity within the established WHO ordinal severity-scale leading to its summary (SWOSS) into three categories, A-C. PE and SWOSS-C were significantly associated with reduced survival of COVID-19 patients. The previously introduced CD8/Treg/monocyte-ratio which hints at a dysfunctional antiviral immunity associated with poor prognosis could be verified in this larger study population. However, the number of circulating CD14 + HLA-DR+ monocytes represented the most significant predictor for myocardial damage indicated by PE. We used all available data for an unbiased examination of associations and predictions by machine learning algorithms: Predictive markers for PE can be obtained in clinic and may serve as prognostic features. Among numerous parameters, C-reactive protein (CRP) was the most important in determining the presence of PE and SWOSS-category. Prediction of survival was most relevantly influenced by SWOSS-category underlining the benefit of this condensed classification for clinical practice. All AI-revealed prognostic features serve as promising starting-point to gain further understanding of the interplay between COVID-19 and heart disease.

Ultrasound-based radiomics and machine learning for enhanced diagnosis of knee osteoarthritis: Evaluation of diagnostic accuracy, sensitivity, specificity, and predictive value

Purpose

To evaluate the usefulness of radiomics features extracted from ultrasonographic images in diagnosing and predicting the severity of knee osteoarthritis (OA).

Methods

In this single-center, prospective, observational study, radiomics features were extracted from standing radiographs and ultrasonographic images of knees of patients aged 40-85 years with primary medial OA and without OA. Analysis was conducted using LIFEx software (version 7.2.n), ANOVA, and LASSO regression. The diagnostic accuracy of three different models, including a statistical model incorporating background factors and machine learning models, was evaluated.

Results

Among 491 limbs analyzed, 318 were OA and 173 were non-OA cases. The mean age was 72.7 (±8.7) and 62.6 (±11.3) years in the OA and non-OA groups, respectively. The OA group included 81 (25.5 %) men and 237 (74.5 %) women, whereas the non-OA group included 73 men (42.2 %) and 100 (57.8 %) women. A statistical model using the cutoff value of MORPHOLOGICAL_SurfaceToVolumeRatio (IBSI:2PR5) achieved a specificity of 0.98 and sensitivity of 0.47. Machine learning diagnostic models (Model 2) demonstrated areas under the curve (AUCs) of 0.88 (discriminant analysis) and 0.87 (logistic regression), with sensitivities of 0.80 and 0.81 and specificities of 0.82 and 0.80, respectively. For severity prediction, the statistical model using MORPHOLOGICAL_SurfaceToVolumeRatio (IBSI:2PR5) showed sensitivity and specificity values of 0.78 and 0.86, respectively, whereas machine learning models achieved an AUC of 0.92, sensitivity of 0.81, and specificity of 0.85 for severity prediction.

Conclusion

The use of radiomics features in diagnosing knee OA shows potential as a supportive tool for enhancing clinicians' decision-making.

Identification and Validation of Ferritinophagy-Related Biomarkers in Periodontitis

OBJECTIVE

While ferritinophagy is believed to play a significant role in the development of periodontitis, the exact mechanisms remain unclear. This study aimed to investigate the biomarkers associated with ferritinophagy in periodontitis using transcriptomic data.

METHODS

Two periodontitis-related datasets from Gene Expression Omnibus, GSE10334, and GSE16134, served as the training and validation cohorts, respectively. Additionally, 36 ferritinophagy-related genes (FRGs) were obtained from the GeneCards database. We compared the expression differences of FRGs between the periodontitis and control groups, identifying the different FRGs as candidates. Weighted gene coexpression network analysis (WGCNA) was applied to capture the key modules and modular genes related to periodontitis, utilizing the candidate FRG scores as trait. Then we intersected these with key module genes to identify differentially expressed FRGs. Hub genes were filtered using a protein-protein interaction network. Ultimately, biomarkers were acquired through machine learning, receiver operating characteristic curves, and expression levels. In addition, biomarker-associated immune cells and functional pathways were analysed to predict the upstream regulatory molecules.

RESULTS

In total, 18 candidate FRGs showed significant differences between the periodontitis and control groups, and from the protein-protein interaction network, eight hub genes were identified among the 175 differentially expressed FRGs by analysing 1096 differentially expressed genes and 4479 key modular genes. Eventually, ALDH2, diazepam binding inhibitor, HMGCR, OXCT1, and ACAT2 were identified as potential biomarkers through machine learning algorithms, receiver operating characteristic curve analysis, and gene expression assessments. In addition, resting dendritic cells, mast cells, and follicular helper T cells were positively correlated with the five biomarkers (Cor > 0.3 and P < .05). All five biomarkers are involved in the translation initiation pathway, including transcription factors like KLF5 and microRNAs such as hsa-miR-495-3p and hsa-miR-27a-3p. Reverse transcription-quantitative polymerase chain reaction analysis showed that all biomarkers were expressed at low levels in the periodontitis group. However, the differences in expression levels for OXCT1 and ACAT2 between groups were not statistically significant.

CONCLUSIONS

A total of five ferritinophagy-related biomarkers - ALDH2, diazepam binding inhibitor, HMGCR, OXCT1, and ACAT2 - were screened to explore new treatment options for periodontitis.

Estimating the Impact of Adjuvant Treatment With Nivolumab on Long-Term Survivorship Rates Compared With Surveillance in Muscle Invasive Urothelial Carcinoma: Mixture Cure Modeling Analyses of Disease-Free Survival From the Phase 3 CheckMate 274 Trial

BACKGROUND

Curative potential of adjuvant nivolumab was compared with radical resection only among patients at high risk of recurrence following radical surgery of muscle-invasive urothelial carcinoma (MIUC).

METHODS

Using patient-level disease-free survival (DFS) data from CheckMate 274 (n = 709, minimum follow-up, 31.6 months), we applied mixture cure models (MCMs) to the adjuvant nivolumab (NIVO) and placebo (PBO) arms of the intention-to-treat (ITT) population and tumor PD-L1 expression ≥1% subpopulation. DFS was derived for hypothetical "cured" and "uncured" subgroups. DFS for the cured subgroup was estimated using WHO background mortality rates matched to trial demographic characteristics. Uncured DFS was modeled using parametric distributions and characterized with cure fractions by maximum-likelihood methods. Model selection considered clinical plausibility, visual comparisons of model fit, and goodness-of-fit statistics.

RESULTS

MCM analysis demonstrated that almost all uncured patients experience recurrence or death within 5 years. Clinically plausible models estimated higher cure fractions in tumor PD-L1 ≥1% subgroup for patients treated with NIVO (PD-L1 ≥1%: 59.1%-61.0% vs ITT: 43.1%-45.1%), and highly similar cure fractions for patients receiving PBO irrespective of their PD-L1 expression (PD-L1 ≥1%: 35.9%-36.4% vs ITT: 36.4%-37.0%). Projected 10-year mean DFS was 4.38 to 4.47 years for NIVO and 3.61 to 3.64 years for PBO in the ITT population, and 5.54 to 5.65 years for NIVO and 3.54 to 3.57 years for PBO in the PD-L1 ≥1% subpopulation.

CONCLUSIONS

Adjuvant NIVO for high-risk MIUC was associated with a higher cure fraction than PBO in the ITT and PD-L1 ≥1% populations. Results align with reported survival from the trial and highlight clinical outcomes of interest. CheckMate 274 ClinicalTrials.gov identifier, NCT02632409.

Cotton production areas are at high risk of invasion by Amrasca biguttula (Ishida) (Cicadellidae: Hemiptera): potential distribution under climate change

BACKGROUND

The cotton jassid, Amrasca biguttula, a dangerous and polyphagous pest, has recently invaded the Middle East, Africa and South America, raising concerns about the future of cotton and other food crops including okra, eggplant and potato. However, its potential distribution remains largely unknown, posing a challenge in developing effective phytosanitary strategies. We used an ensemble model of six machine-learning algorithms including random forest, maxent, support vector machines, classification and regression tree, generalized linear model and boosted regression trees to forecast the potential distribution of A. biguttula in the present and future using presence records of the pest and bioclimatic predictors. The accuracy of these algorithms was assessed based on the area under the curve (AUC), correlation (COR), deviance and true skill statistic (TSS).

RESULTS

All algorithms showed good performance in forecasting the distribution of A. biguttula (AUC ≥ 0.91, COR ≥ 0.72, TSS ≥ 0.77 and deviance ≤ 0.65). Mean temperature of wettest quarter, mean temperature of driest quarter and precipitation of the wettest month were the key variables that contributed to predicting A. biguttula occurrence. Projection of the model showed that cotton production areas in Asia, sub-Saharan Africa, and South America are at threat of invasion by A. biguttula under the current climatic scenario. Additionally, range expansion for A. biguttula is projected in the future in sub-Saharan Africa, South America and China, indicating a suitable ecological niche for A. biguttula to thrive.

CONCLUSION

Our results provide early warning and decision-making information that can guide efforts to prevent this pest's further spread and invasion into new areas. © 2025 Society of Chemical Industry.

Integrated transcriptomics of multiple sclerosis peripheral blood mononuclear cells explored potential biomarkers for the disease

Background

Despite their importance, blood RNAs have not been comprehensively studied as potential diagnostic markers for multiple sclerosis (MS). Herein, by the integration of GSE21942 and GSE203241 microarray profiles of peripheral blood mononuclear cells, this study explored potential biomarkers for the disease.

Methods

After identification of differentially expressed genes (DEGs), functional enrichment analyses were performed, and PPI and miRNA-mRNA regulatory networks were constructed. After implementing weighted gene co-expression network analysis (WGCNA) and discovering MS-specific modules, the converging results of differential expression analysis and WGCNA were subjected to machine learning methods. Lastly, the diagnostic performance of the prominent genes was evaluated by receiver operating characteristic (ROC) analysis.

Results

COPG1, RPN1, and KDM3B were initially highlighted as potential biomarkers based on their acceptable diagnostic efficacy in the integrated data, as well as in both GSE141804 and GSE146383 datasets as external validation sets. However, given that they were downregulated in the integrated data while they were upregulated in the validation sets, they could not be considered as potential biomarkers for the disease. In addition to this inconsistency, evaluating their diagnostic performance in other external datasets (GSE247181, GSE59085, and GSE17393) did not reveal their diagnostic efficacy.

Conclusions

This study could not unveil promising blood biomarkers for MS, possibly due to a small sample size and unaccounted confounding factors. Considering PBMCs and blood specimens as valuable sources for the identification of biomarkers, further transcriptomic analyses are needed to discover potential biomarkers for the disease.

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models

A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical literature to correct such disparities by removing race as a predictor (i.e., race-free models). Alternatively, a variety of machine learning methods have been proposed to constrain differences in model predictions between racial groups. In this work, we compare these approaches for equitable stroke risk prediction. We begin by proposing a discrete-time, neural network-based time-to-event model that incorporates a parity constraint designed to make predictions more similar between groups. Using harmonized data from Framingham Offspring, MESA, and ARIC studies, we develop both parity-constrained and unconstrained stroke risk prediction models, then compare their performance with race-free models in a held-out test set and a secondary validation set (REGARDS). Our evaluation includes both intra-group and inter-group performance metrics for right-censored time to event outcomes. Results illustrate a fundamental trade-off in which parity-constrained models must sacrifice intra-group calibration to improve inter-group discrimination performance, while the race-free models strike a balance between the two. Consequently, the choice of model must depend on the potential benefits and harms associated with the intended clinical use. All models as well as code implementing our approach are available in a public repository. More broadly, these results provide a roadmap for development of equitable clinical risk prediction models and illustrate both merits and limitations of a race-free approach.

Supporting young siblings of children with intellectual disabilities and/or visual impairments with the serious game 'Broodles': A mixed methods randomized controlled trial

BACKGROUND

Siblings of children with neurodevelopmental conditions experience conflicting emotions and have an increased risk of mental health problems. Several sibling interventions have been developed, but few are readily available, leaving many siblings unsupported. Therefore, the free, online, self-administered sibling serious game 'Broodles' was developed. This study assessed its social validity and effectiveness in promoting quality of life, and inter- and intrapersonal factors in siblings (6-9 years) of children with intellectual disabilities and/or visual impairments.

METHODS

A mixed methods, waitlist control group, randomized controlled trial was conducted. In total, 107 Dutch or Belgian parent-child dyads completed questionnaires at three timepoints (baseline, one-month post-test, two-month follow-up). The intervention group also completed post-test interviews. Effects were assessed using multilevel modelling, and thematic analysis was applied to the evaluations.

RESULTS

Significant, weak interaction effects (R² = .03-.06) were found on sibling negative adjustment, but only in those who completed ≥ 75 % of the game and followed the study timeline. Regardless of group, (very) small, significant improvements over time were found on several outcomes (R² = .01-.06). 'Broodles' was experienced as fun (80 %) and helpful (79 %). Perceived learning outcomes included the themes: 'sibling awareness and validation', 'emotions and needs', 'coping with emotions and situations' and 'family interactions'.

CONCLUSION

Although quantitative data showed small effects, qualitative data revealed a variety of learning outcomes which can contribute to siblings' resilience, and prevention of mental health problems. To unlock its full potential, future studies should examine if additional family-targeted components can enhance the intervention's impact.

Peripheral blood RNA biomarkers can predict lesion severity in degenerative cervical myelopathy

JOURNAL/nrgr/04.03/01300535-202506000-00027/figure1/v/2024-08-05T133530Z/r/image-tiff Degenerative cervical myelopathy is a common cause of spinal cord injury, with longer symptom duration and higher myelopathy severity indicating a worse prognosis. While numerous studies have investigated serological biomarkers for acute spinal cord injury, few studies have explored such biomarkers for diagnosing degenerative cervical myelopathy. This study involved 30 patients with degenerative cervical myelopathy (51.3 ± 7.3 years old, 12 women and 18 men), seven healthy controls (25.7 ± 1.7 years old, one woman and six men), and nine patients with cervical spondylotic radiculopathy (51.9 ± 8.6 years old, three women and six men). Analysis of blood samples from the three groups showed clear differences in transcriptomic characteristics. Enrichment analysis identified 128 differentially expressed genes that were enriched in patients with neurological disabilities. Using least absolute shrinkage and selection operator analysis, we constructed a five-gene model (TBCD, TPM2, PNKD, EIF4G2, and AP5Z1) to diagnose degenerative cervical myelopathy with an accuracy of 93.5%. One-gene models (TCAP and SDHA) identified mild and severe degenerative cervical myelopathy with accuracies of 83.3% and 76.7%, respectively. Signatures of two immune cell types (memory B cells and memory-activated CD4+ T cells) predicted levels of lesions in degenerative cervical myelopathy with 80% accuracy. Our results suggest that peripheral blood RNA biomarkers could be used to predict lesion severity in degenerative cervical myelopathy.

An Optimized Framework of QSM Mask Generation Using Deep Learning: QSMmask-Net

Quantitative susceptibility mapping (QSM) provides the spatial distribution of magnetic susceptibility within tissues through sequential steps: phase unwrapping and echo combination, mask generation, background field removal, and dipole inversion. Accurate mask generation is crucial, as masks excluding regions outside the brain and without holes are necessary to minimize errors and streaking artifacts during QSM reconstruction. Variations in susceptibility values can arise from different mask generation methods, highlighting the importance of optimizing mask creation. In this study, we propose QSMmask-net, a deep neural network-based method for generating precise QSM masks. QSMmask-net achieved the highest Dice score compared to other mask generation methods. Mean susceptibility values using QSMmask-net masks showed the lowest differences from manual masks (ground truth) in simulations and healthy controls (no significant difference, p > 0.05). Linear regression analysis confirmed a strong correlation with manual masks for hemorrhagic lesions (slope = 0.9814 ± 0.007, intercept = 0.0031 ± 0.001, R2 = 0.9992, p < 0.05). We have demonstrated that mask generation methods can affect the susceptibility value estimations. QSMmask-net reduces the labor required for mask generation while providing mask quality comparable to manual methods. The proposed method enables users without specialized expertise to create optimized masks, potentially broadening QSM applicability efficiently.

The Relevance of Hypersexuality and Impulsivity in Different Groups of Treatment-Seekers With and Without (Exclusive) Pedophilia

Hypersexuality and impulsivity are regarded as risk factors for sexual offending against children. Studies exploring these factors in undetected men who offended or are at risk of offending are rare. This study aims to investigate hypersexuality and impulsivity in treatment-seeking men with and without a diagnosis of (exclusive) pedophilia who committed child sexual abuse (CSA), consumed child sexual abuse images (CSAI), or feel at risk of offending sexually. Data were obtained from three child abuse prevention projects in Bamberg, Germany. We employed self-report (BIS-11, HBI), objective measures (TSO), and risk assessment tools (STABLE-2007). We computed Bayesian ordinal logit and binomial generalized linear models to explore differences between groups and to predict lifetime CSA and CSAI. Hypersexuality scores were particularly pronounced in patients with exclusive and non-exclusive pedophilia. Patients without pedophilia scored similarly to nonclinical samples. Impulsivity measures did not consistently differ between groups. We could not predict lifetime CSA and CSAI using impulsivity and hypersexuality measures. Sexual rather than general impulsivity seems to be an issue in men with pedophilia. The motivation to offend in patients without pedophilia is discussed.

Identification of Periodontal Disease Diagnostic Markers Via Data Cross-Validation

INTRODUCTION AND AIMS

Periodontitis is a globally prevalent disease that is clinically diagnosed when the periodontal tissues are pathologically affected. Therefore, it is vital to identify novel periodontitis-associated biomarkers that will aid in diagnosing or treating potential patients with periodontitis.

METHODS

The GSE16134 and GSE10334 datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes between periodontitis and healthy samples. Single-sample gene set enrichment analysis was performed to identify significantly involved signalling pathways. Weighted gene correlation network analysis was used to identify key molecular modules. Hub genes were screened using key genes to construct a diagnosis and prediction model of periodontitis. Microenvironment cell population-counter was used to analyse immune cell infiltration patterns in periodontal diseases.

RESULTS

Single-sample gene set enrichment analysis revealed that periodontitis involves the PI3K/AKT/mTOR signalling pathway and associated module genes (667 genes). Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the module genes revealed that periodontitis involves the type I interferon, rhythmic process, and response to type I interferon signalling pathways. GSEA identified 21 core genes associated with periodontitis and classified them into two clusters, A and B. Genomics of Drug Sensitivity in Cancer analysis revealed that AKT.inhibitor.VIII had high drug sensitivity in the cluster A subtype. Monocytes and myeloid dendritic cell infiltration were enriched in the cluster A subtype, whereas natural killer T cell infiltration was enriched in the cluster B subtype.

CONCLUSION

The pathway and gene modules identified in this study may help comprehensively diagnose periodontitis and provide a novel method for evaluating new treatments.

CLINICAL RELEVANCE

Our results are beneficial for classifying periodontitis subtypes and treatment using targeted medicine.

Bayesian spatio-temporal modeling of severe acute respiratory syndrome in Brazil: A comparative analysis across pre-, during, and post-COVID-19 eras

This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome (SARS) across the diverse health regions of Brazil from 2016 to 2024. Leveraging extensive datasets that include SARS cases, climate data, hospitalization records, and COVID-19 vaccination information, our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset. The analysis reveals significant variations in the incidence of SARS cases over time, particularly during and between the distinct eras of pre-COVID-19, during, and post-COVID-19. Our modeling approach accommodates explanatory variables such as humidity, temperature, and COVID-19 vaccine doses, providing a comprehensive understanding of the factors influencing SARS dynamics. Our modeling revealed unique temporal trends in SARS cases for each region, resembling neighborhood patterns. Low temperature and high humidity were linked to decreased cases, while in the COVID-19 era, temperature and vaccination coverage played significant roles. The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil, offering a foundation for targeted public health interventions and preparedness strategies.

Interventions for SARS-CoV-2 prevention among Jailed adults: A network-based modeling analysis

Background

Airborne pathogens present challenges in settings like jails or prisons with a high density of contacts. The state of Georgia has the highest percentage of its citizens under correctional supervision in the United States. Yet, it had slow COVID vaccine uptake among jail residents, requiring prevention also using non-pharmaceutical interventions. Using a network-based SARS-CoV-2 transmission model parameterized with data from the Fulton County Jail, this study investigates the impact of three SARS-CoV-2 prevention strategies: vaccination, contact tracing and quarantining, and jail release to reduce jail population density.

Methods

Social contact networks were simulated at two different overlapping network layers: cell and block. Cell-level contacts represented shared confined sleeping space, whereas block-level contacts represented shared socialization space. Contact tracing and quarantining were simulated at the cell-level or both cell- and block-levels, hereafter referred to as all-level. A reference scenario and nine intervention scenarios were simulated three hundred times to estimate the median and interquartile range (IQR) of the outcome measures. Each scenario simulated a 185-day period to measure the prolonged effects of the interventions amid a potential COVID outbreak in the jail. The cumulative incidence, number of infections averted (NIA), and percentage of infections averted (PIA) were calculated comparing interventions against a base scenario without them. For the seven scenarios involving contact tracing and quarantining, total quarantines over the simulation and the number of quarantines per day were calculated to determine the quarantine requirements. Sensitivity analyses compared the impact of jointly varying vaccination rates and contact tracing rates.

Results

Cell-level contact tracing alone was an ineffective intervention (3.2% PIA), but its impact increased in combination with other interventions (i.e., vaccination or increased jail release rate). The other intervention strategies each produced a PIA over 10%, with the jail release scenario producing a PIA of nearly 20% despite only resulting in a 13% reduction in the jail population. The all-level contact tracing only scenario was effective at both 50% and 100% of contacts traced, but feasibility would be limited without a reduction in the jail population.

Conclusions

Implementing a combination intervention approach could substantially reduce the morbidity from COVID-19 and future respiratory viruses in this jail setting while providing secondary protection to the community.

Hierarchical Bayesian linear mixed model to estimate variability in the thermal inactivation parameters for Listeria species

A total of 476 D-values associated with 76 strains of Listeria spp. in liquid media were obtained from 27 scientific articles. Meta-analysis was carried out using hierarchical Bayesian models to assess variability, predict the D-, and estimate the zT-, and zpH-values for Listeria spp. across a range of temperatures (55-70 °C) and pH values (3-8 units). Different hypotheses regarding variability between strains or studies in each model correspond to different hierarchical assumptions about the inactivation parameters. The models produced were compared based on their predictive ability, Bayes-R2 and widely applicable information criterion (WAIC) values. A hierarchical model that considered random effects due to both strain and study effects on the thermal inactivation parameters was determined to be the "best" model and was subsequently used to estimate the posterior distributions for the D-, zT-, and zpH-values. The variability introduced in the parameters due to differences between studies was higher than that of variability between strains. The parameters estimated using the model for different strains of Listeria species may be applicable for processing aqueous foods such as milk and liquid products such as sauces and gravies across a temperature range of 55-70 °C and pH values of 3-8 units.

Exploring the Remote Administration of a Performance-Based Functional Assessment

Performance-based assessments of instrumental activities of daily living (IADL) can detect subtle functional impairments better than self-reported questionnaires. While most performance-based IADL assessments were developed for in-person administration, remote administration could increase access to vulnerable older adults. This study compared in-person and remote administration of IADL tasks from the Performance Assessment of Self-Care Skills. Community-dwelling older adults completed tasks (shopping, checkbook balancing, and medication management) at baseline (in-person) and follow-up (either in-person or remote, with modifications) two years later. Scores between the two follow-up groups, change in scores from baseline to follow-up, and differential item functioning (DIF) between the two administration methods at follow-up were examined. There were no differences in scores between methods of administration, but remote tasks took longer, and one item had significant DIF (ps < .01). Clinicians found remote administration acceptable and feasible. With minor adaptations, remote administration of the three tasks was supported. Further validation research is needed.

Microplastic uptake with food increases risk-taking of a wide-spread decomposer, the common pill bug Armadillidium vulgare

Exposure to microplastics (MPs) i.e., plastic fragments between 1 μm and 1 mm in diameter causing growing concern for wildlife and humanity. It is now evident that MPs can accumulate in soil, freshwater, seawater and the atmosphere; thus, living organisms are directly or indirectly exposed to these significant ecological stressors globally. Studies on the physiological effects of MPs in wildlife are emerging, yet, to date, only a handful of studies with a special focus on how MPs affect animal behaviour are available, and there is even less research on how different components of among- and within-individual behavioural variation are affected by MPs. The main goal of this study was to investigate how prolonged exposure (6 weeks) to 10 μm spherical polystyrene microplastics in food (24.85 particles/mg) influences individual variation in risk-taking behaviour in a widespread decomposer, the common pill bug Armadillidium vulgare. Our results indicate a strong MP effect on different levels of behavioural variation: (i) individual mean risk-taking increased, while (ii) a correlation between mean risk-taking and residual within-individual risk-taking variation emerged (risk-takers became less predictable) in the MP treated group. These findings underscore the intricate effects of MPs on individual behavioural variation, with potentially far-reaching ecological and evolutionary consequences given their pervasive presence in both terrestrial and aquatic ecosystems. The negative impacts of these changes are widespread; in our study, MP exposure may increase the susceptibility of A. vulgare to predation, potentially contributing to population decline.

Associations between pathophysiological traits and symptom development in retrospective analysis of V30M and V122I transthyretin amyloidosis

Background

The Val30Met (V30M) and Val122Ile (V122I) transthyretin (TTR) mutations often beget hereditary amyloid transthyretin amyloidosis (hATTR). Since symptoms are progressively debilitating and potentially fatal if untreated, low survival rates result from late diagnoses of hATTR patients. This retrospective analysis of microarray and biobank data helped establish clinical biomarkers for early hATTR detection.

Methods

In a Portuguese sample of V30M carriers (n = 183), gene profiling identified dysregulated immune markers. Among African Americans (AA) and Hispanic/Latinx Americans (HA) from the Mount Sinai BioMe Biobank (n = 28,718), a case-control style Phenome-Wide Association Study (PheWAS; odds ratio [95% confidence interval]) of V122I for phenotypic and echocardiogram traits (β coefficients [95 % CI]) determined gene pleiotropy.

Results

Among V30M profiles, 96 (52.4%) were symptomatic, expressing upregulated neutrophil activity (p < 10-16), IL-6/JAK/STAT3 signaling (p < 10-3), and downregulated CD4+T cell expression (p = 0.009), compared to their asymptomatic counterparts. In BioMe, 562 (2.0%) were V122I carriers, demonstrating associations with heart failure (1.71 [1.23-2.39]; p = 0.0014), amyloidosis (20.79 [8.42-51.31]; p = 4.67 × 10-11), secondary/extrinsic cardiomyopathies (17.73 [7.25-43.37]; p = 2.97 × 10-10), peripheral nerve disorders (4.14 [2.42-7.09]; p = 2.26 × 10-7), primary angle-closure glaucoma (8.03 [3.15-20.46]; p = 1.27 × 10-5), malignant neoplasm of the female breast (4.48 [2.23-9.00]; p = 2.48 × 10-5), fracture of tibia and fibula (8.42 [3.25-21.89]; p = 1.19 × 10-5), and Carpal tunnel syndrome (2.62 [1.68-4.11]; p = 2.44 × 10-5). Echocardiographic presentations included higher LVEDV (15.87 [9.63-22.10]; p = 6.04 × 10-7) and LA length (1.52 [0.69-2.35]; p = 3.31 × 10-4). Race-stratified associations identified that AA presented more severe cardiac abnormalities than HA.

Conclusions

This study identified inflammatory biomarkers upregulated in symptomatic V30M carriers and phenotypic/echocardiographic traits associated with V122I, representing comorbidities of hATTR pathology. Such markers can provide the basis for future improvements in diagnostic regimes to deliver early therapies.

Prevalence Estimation Using a Depression Screening Tool in the National Health and Nutrition Examination Survey: Comparison of Different Cutoffs

OBJECTIVES

The National Health and Nutrition Examination Survey (NHANES) in the US relies on the depression screening tool PHQ-9 to assess depressive symptoms in the general population. For prevalence estimation, PHQ-9s imperfect diagnostic accuracy can be modeled with a Bayesian Latent Class Model. We investigate the impact of different cutoffs on prevalence estimation.

METHODS

We used data from the 16-th wave of the National Health and Nutrition Examination Survey (NHANES). We assessed the joint posterior distribution to asssess the prevalence of major depression as well as sensitivity and specificity of the PHQ-9 at cutoffs 5 to 15. We also assessed the impact of weakly and strongly informative prevalence priors.

RESULTS

Data from 9693 participants of the NHANES Wave 2019-2020 were analyzed. Under weakly informative prevalence priors, prevalence estimates ranged from 16.0% (95% CrI: 0.3%-87.8%) when using a cut-off of 5% to 3.9% (0.2%-12.7%) at 13. More informative prevalence priors led to narrower credible intervals, but the observed data was still in accordance with a wide range of possible MDD prevalence estimates.

CONCLUSIONS

Regardless of the cutoff and the prevalence prior chosen, prevalence estimation of major depressive disorders in the NHANES based on the PHQ-9 is imprecise.

Network meta-analysis based ranking of dry off interventions to cure or prevent intramammary infections in dairy cows

This study aimed to rank dry off interventions for the prevention of new intramammary infections (IMI) and the cure of existing IMI in quarters of dry cows using two network meta-analyses. Randomized controlled trials reported in 137 papers were assessed for inclusion eligibility. Network meta-analyses were performed separately for the incidence risk of IMI and cure risk of IMI. For cure of IMI, 29 trials with 10 dry off interventions were included. Both selective and blanket dry cow therapy, either in combination with an internal teat sealant or as a singular intervention, resulted in a better cure risk compared with the non-antimicrobial interventions. No differences were observed between the antimicrobial based interventions. For the incidence risk of IMI, 54 trials were included, representing 18 dry off interventions. The incidence risk of IMI was similar for the various selective dry cow treatments when antimicrobials were administered together with an internal teat sealant, either at quarter or cow level. Also, they did not differ from the evaluated blanket dry cow treatment interventions or when an internal teat sealant was applied alone. Selective dry cow therapy with internal teat sealant is therefore likely a suitable intervention option to simultaneously maintain a low incidence risk of IMI and a high cure risk of IMI, all the while lowering the antimicrobial use in dairy herds. Circumstances in the herd, including the distribution and prevalence of mastitis pathogens, should be evaluated before results are utilized in dairy practice given the heterogeneity of included studies.

Identification and validation of m7G-related genes related to macrophage immunity in acute myocardial infarction through comprehensive bioinformatics analysis

BACKGROUND

Acute myocardial infarction (AMI) is a fatal disease related to immune cell activation; however, the pathological molecular mechanisms associated with AMI and immunity remain unclear. This study aims to explore m7G-related hub genes associated with immune cell characteristics in AMI through the bioinformatics method.

METHODS

Transcriptome sequencing data downloaded from GSE59867 (GPL6244) were used to screen m7G-related differentially expressed genes (DEGs) between AMI and non-AMI controls. Abnormal immune cell characteristics was analyzed by single-sample gene set enrichment analysis (ssGSEA) algorithm. Hub genes were screened from m7G-related DEGs by the support vector machine recursive feature elimination (SVM-RFE) algorithm and random forest tree model. The association of hub genes with immune cell types was analyzed by GSEA and Spearman correlation analysis. A mouse AMI model and hypoxia-stimulated macrophage model were established to verified the function of CYFIP1 on macropahges.

RESULTS

We identified significant differences in 21 types of immune cells and 13 m7G-related DEGs between AMI and non-AMI controls. m7G-related DEGs were enriched in nucleoside nuclear catabolism, RNA modification and translation regulation, the HIF-1 signaling pathway, etc. 111 AMI samples were divided into three clusters based on the cluster analysis of m7G-related DEG expression profiles, and immune cell types were significantly different in the three clusters. Four hub genes including CYFIP1, EIF4E2, IFIT5, and NCBP3 were screened and positively or negatively correlated with AMI. ROC curve verified the efficiency of the 4 hub genes in the diagnosis prediction models of AMI. CYFIP1 had the best prediction efficiency of than other 3 hub genes. GESA enrichment and Spearman correlation analysis found that hub genes were associated with inflammation and immune, especially CYFIP1 had a strong statistical relationship with macrophages, Monocyte, etc. By experiments, we found that CYFIP1 was upregulated in AMI patients and animal models, and knockdown of CYFIP1 inhibited hypoxia-mediated macrophage inflammatory response.

CONCLUSION

m7G-related hub genes are associated with immune cell characteristics in AMI, among which CYFIP1 may play a key role in the regulatory network of macrophage immune response.

Alemtuzumab Exposure and T Lymphocyte Depletion: A Population Pharmacokinetic-Pharmacodynamic Model of Alemtuzumab Induction Therapy for Kidney Transplantation

Alemtuzumab is a T cell-depleting monoclonal antibody that is used for the prevention of kidney transplant rejection. The duration of lymphodepletion after the current standard induction therapy dose is likely longer than necessary, resulting in prolonged T cell lymphopenia with the associated risk of infections. Here, the interplay between alemtuzumab exposure and T cell dynamics was quantitatively evaluated, and the influence of different doses on T cell recovery was investigated. A population pharmacokinetic-pharmacodynamic model describing the interplay between 30 mg alemtuzumab induction therapy and T cell dynamics in kidney transplantation was developed using NONMEM, using pharmacodynamic data from the Triton study (NCT02057965). The developed model was used to perform an exposure-response analysis and investigate dose optimization with model-derived simulations. In total, 418 peripheral blood T cell measurements from 61 adult kidney transplant recipients were included for model development. A single-compartment turnover Emax model with a first-order T cell influx with feedback and a first-order T cell efflux with parallel alemtuzumab-stimulated T cell removal best described the data. Higher alemtuzumab exposure was associated with lower individual-predicted T cells 4 weeks after administration and longer T cell recovery (> 200 cells/μL). In the simulations, a fixed dose of 15 mg improved median recovery times by 19 days as compared to the standard 30 mg dose without influencing early T cell depletion. A population pharmacokinetic-pharmacodynamic model adequately described T cell dynamics after alemtuzumab induction therapy in kidney transplant recipients. This model can be used to inform future dose-optimization studies of alemtuzumab in different clinical settings.

A supervised learning approach to estimating IRT models in small samples

Existing estimators of parameters of item response theory (IRT) models exploit the likelihood function. In small samples, however, the IRT likelihood oftentimes contains little informative value, potentially resulting in biased and/or unstable parameter estimates and large standard errors. To facilitate small-sample IRT estimation, we introduce a novel approach that does not rely on the likelihood. Our estimation approach derives features from response data and then maps the features to item parameters using a neural network (NN). We describe and evaluate our approach for the three-parameter logistic model; however, it is applicable to any model with an item characteristic curve. Three types of NNs are developed, supporting the obtainment of both point estimates and confidence intervals for IRT model parameters. The results of a simulation study demonstrate that these NNs perform better than Bayesian estimation using Markov chain Monte Carlo methods in terms of the quality of the point estimates and confidence intervals while also being much faster. These properties facilitate (1) pretesting items in a real-time testing environment, (2) pretesting more items and (3) pretesting items only in a secured environment to eradicate possible compromise of new items in online testing.

Depressive symptoms changes in the new-onset stroke patients: A cross-lagged panel network analysis

BACKGROUND

Each year, there are approximately 10.3 million new stroke cases worldwide, with 2 million occurring in China. Post-stroke depression (PSD) is a common complication that negatively affects rehabilitation outcomes and increases long-term mortality.

OBJECTIVE

This study used network analysis to investigate the cross-sectional and longitudinal networks between depressive symptoms in new-onset stroke patients with PSD, aiming to identify the key symptoms and predictive relationships among distinct symptoms during the acute phase and 6 months after the stroke.

METHODS

This longitudinal descriptive study collected data from October 2022 to December 2023, including eligible new-onset stroke patients. Depressive symptoms were assessed using the CES-D scale, and network analysis was used to analyze the interactions between symptoms.

RESULTS

613 participants completed the data collection. The study found that D3 (Felt sadness) emerged as the central depressive symptom at both baseline and follow-up (EI value = 1.215 and 1.168, respectively). In the longitudinal network analysis, D7 (Sleep quality) displayed the strongest out-Expected Influence (value = 1.728), while D4 (Everything was an effort) showed the strongest in-Expected Influence (value = 1.322).

LIMITATIONS

The self-report measure is adopted for all depressive symptoms in the study, and there may be some deviation.

CONCLUSION

These symptom-level associations at cross-sectional and longitudinal networks extend our understanding of PSD symptoms in new-onset stroke patients by pointing to specific key depressive symptoms that may aggravate PSD. Recognizing these symptoms is imperative for the development of targeted interventions and treatments aimed at addressing PSD in new-onset stroke patients.

Single-cell sequencing and spatial transcriptomics reveal FAS+ T cell and autophagy-related signatures predicting chemoimmunotherapy response in diffuse large B-cell lymphoma patients

Current subtyping methods of diffuse large B-cell lymphoma (DLBCL) could not satisfy the clinical demands for risk assessment and prognostic prediction. We aimed to investigate the prognostic effect of autophagy-related genes (ARGs) in DLBCL. Transcriptomic data of 1,409 DLBCL patients, 531 healthy controls (HCs), and single-cell sequencing data of 4 DLBCL were included. Validation involved spatial transcriptomics from 10 DLBCL patients and 110 DLBCL proteomic data from a local cohort. We identified 153 differentially expressed ARGs between DLBCL patients (n=48) and HCs (n=531), classifying 414 DLBCL patients into two subtypes based on autophagy heterogeneity. Subtype I, characterized by upregulated T regulatory (Treg) cells (P<0.0001) and T follicular helper (Tfh) cells (P=0.0012), showed a superior prognosis (P=0.035). Eight prognostic ARGs were selected to construct an autophagy-related model, dividing patients into low- and high-risk groups. Kaplan-Meier survival analysis revealed significantly better outcomes for the low-risk group in both the discovery (P<0.0001) and validation cohorts (P=0.0041). High-risk patients exhibited elevated IDO1 (P=0.042) and LAG3 (P<0.001) levels. Among the eight signature proteins, higher FAS was further verified to indicate a better prognosis in the local cohort (n=110) using antibody array (P=0.0083). FAS was primarily expressed in T cells such as Treg and Tfh cells and was elevated in non-progressive disease patients. FAS-positive T cells showed increased interferon-gamma (normalized enrichment score (NES)=2.196, FDR<0.0001) and alpha (NES=1.836, FDR<0.01) response activities. We constructed an autophagy-related model and identified FAS as a prognostic biomarker. FAS+ Treg and Tfh cell-enriched TME indicated a favorable prognosis.