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PubMed2026

Sex-dependent protective effects of microglial tumor necrosis factor on post-stroke inflammation and myelin injury.

Tumor necrosis factor (TNF) is rapidly induced after ischemic stroke, but its proposed cell-specific and sex-dependent functions during post-stroke inflammation remain insufficiently understood. Here, we investigated the role of microglia-derived TNF in the acute and subacute response to permanent middle cerebral artery occlusion (pMCAO). Tnf expression was transiently upregulated after stroke, becoming significant at 4 h, peaking at 12-24 h, and returning to baseline by 5 days. In situ hybridization confirmed strong Tnf expression in the infarct and peri-infarct regions. Whole-brain transcriptomic profiling showed that global TNF deficiency reshaped the early post-ischemic response, shifting it from microglia-associated phagocytic and wound-healing pathways toward an interferon-related inflammatory signature. To define the specific contribution of microglial TNF, we used inducible Cx3cr1CreER:Tnffl/fl mice. Microglial TNF deletion had no effect on infarct volume in males at 24 h or 5 days after pMCAO, but significantly increased infarct size in females at both time points. In both sexes, brain TNF levels peaked at 24 h and were significantly reduced in Cx3cr1CreER:Tnffl/fl mice, confirming microglia as a major source of early post-ischemic TNF. However, downstream consequences diverged by sex. At 5 days, male Cx3cr1CreER:Tnffl/fl mice showed reduced microglial reactivity and 18 kDa translocator protein (TSPO) signal, with no change in T-cell infiltration, and exhibited increased density of mature oligodendrocytes. In contrast, female Cx3cr1CreER:Tnffl/fl mice displayed enhanced microglial reactivity, increased TSPO binding, higher peri-infarct T-cell infiltration, and reduced oligodendrocyte density and myelin integrity. Together, these findings identify microglial TNF as a sex-dependent regulator of post-stroke inflammation and myelin injury.

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PubMed2026

Molecular evaluation of residual disease following neoadjuvant chemotherapy in triple-negative breast cancer CALGB 40603 (Alliance).

BACKGROUNDDespite therapeutic advances in early-stage triple-negative breast cancer (TNBC), residual disease (RD) following neoadjuvant therapy remains a key predictor of a worse prognosis and obstacle to improving patient outcomes.METHODSTo better characterize RD and identify survival-associated features, we performed comprehensive transcriptomic profiling of 340 pretreatment stage II/III TNBCs and 70 matched posttreatment RD samples from the randomized CALGB 40603 (Alliance) phase II clinical trial. To explore preclinical treatment strategies for RD, patient-derived xenograft (PDX) mouse models mimicking RD were treated with antibody-drug conjugates (ADCs).RESULTSOur study shows prognostic genomic features measured pretreatment may differ from prognostic features measured posttreatment from RD specimens. Patients with a genomic PAM50 subtype of basal-like in RD specimens had a poor survival outcome, and their matching pretreatment tumors were characterized by elevated chromosomal amplifications of oncogenic drivers and significantly reduced B and T cell expression features. Paired analyses of basal-like RD and matched pretreatment tumors revealed further lymphocyte depletion in RD, along with lower expression of MHC class I and interferon signaling, indicating an immune-cold RD microenvironment. Treatment of a basal-like and conventional chemotherapy-resistant PDX model, resembling basal-like RD, with sacituzumab govitecan or trastuzumab deruxtecan produced a marked antitumor response.CONCLUSIONRD biology differs from pretreatment tumors, with basal-like subtype RD following neoadjuvant chemotherapy being immune cold and associated with poor survival. Preclinical modeling suggests this high-risk group may benefit from adjuvant ADC therapy.TRIAL REGISTRATIONClinicalTrials.gov NCT00861705.FUNDINGNIH NCI U10CA180821 (Alliance for Clinical Trials in Oncology), NCI U24CA176171 (Alliance for Clinical Trials in Oncology), NCI UG1CA233373 (Alliance for Clinical Trials in Oncology), NCI Breast SPORE program P50-CA058223; Susan G. Komen SAC-160074; Breast Cancer Research Foundation BCRF-23-127; NIH NCI R01-CA229409; UNC LCCC Triple Negative Breast Cancer Center.

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PubMed2026

PPRC1 is a prognostic biomarker and key regulator of mitochondrial oxidative phosphorylation in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) remains an incurable haematological malignancy, underscoring the need for novel prognostic biomarkers and therapeutic targets. This study aimed to investigate the clinical and biological significance of peroxisome proliferator-activated receptor gamma coactivator-related protein 1 (PPRC1) in MM. METHODS: Expression and clinical data were obtained from public databases and an independent local cohort. Kaplan-Meier and Cox regression analyses were performed to evaluate prognostic value. Differential expression analysis, pathway enrichment analysis and single-cell RNA-seq data analysis were used to explore biological functions. PPRC1 was silenced in MM cell lines using siRNA to assess its effects on cell survival and oxidative phosphorylation. RESULTS: PPRC1 was significantly upregulated in MM and was associated with advanced disease stage and poor overall survival. Multivariate Cox analysis identified PPRC1 as an independent prognostic factor. A nomogram incorporating PPRC1 and revised-ISS improved survival prediction. Functional analyses revealed that PPRC1 was positively correlated with oxidative phosphorylation and oncogenic signalling pathways. A potential connection between PPRC1 expression and immune cell infiltration was observed. PPRC1 knockdown inhibited cell proliferation, induced cell cycle arrest and apoptosis and impaired oxidative phosphorylation in MM. CONCLUSIONS: PPRC1 acts as a prognostic biomarker and metabolic regulator in MM by sustaining mitochondrial oxidative phosphorylation. These findings highlight PPRC1 as a potential therapeutic target in MM.

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PubMed2026

Cognitive-metabolic relationship in temporal lobe epilepsy: A systematic review.

OBJECTIVE: To summarize the current literature on neurometabolic dysfunction identified through brain imaging and its cognitive correlates in temporal lobe epilepsy (TLE). BACKGROUND: Cognitive decline contributes to chronic disability in TLE. The pathophysiology of cognitive decline in TLE is poorly understood, limiting therapeutic advances. Characterizing metabolic changes in patients with TLE and cognitive impairment may identify biomarkers and inform new treatment strategies. DESIGN/METHODS: We conducted a systematic review of five major databases, gathering studies published through December 2024, in accordance with PRISMA guidelines. We included all observational studies describing associations between metabolic imaging findings and cognitive measures in TLE. RESULTS: Of 1449 reports, 38 met the inclusion criteria, encompassing 1161 patients with TLE aged 5-66 years. Twenty-two studies applied fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) to assess interictal brain glucose metabolism. Two studies utilized PET with other tracers to assess more specific metabolic aspects. Fourteen studies used proton magnetic resonance spectroscopy (1H-MRS) to quantify local concentrations of brain metabolites. Impairment of verbal memory was consistently associated with left temporal lobe metabolite changes. Non-memory cognitive impairments correlated with changes in glucose metabolism, N-acetylaspartate, and gamma-aminobutyrate in both temporal and extratemporal areas. CONCLUSION: 18F-FDG PET remains the most widely used imaging modality to assess cognitive-metabolic correlates in TLE, while other PET tracers and 1H-MRS are potentially underexplored. Verbal memory impairment correlates robustly with left temporal dysmetabolism. Cognitive impairment in TLE is multifaceted and correlates with measurable changes in metabolism in both temporal and extratemporal regions. While our synthesis was restricted by some methodological limitations, these neurometabolic signatures may hold promise as potential biomarkers for identifying risk of cognitive decline and highlight avenues for future research.

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PubMed2026

Atypical cytokine profiles in people on the autism spectrum: a comprehensive systematic review and meta-analysis including 54 cytokines.

Atypical peripheral blood cytokine concentrations have been shown in autism, but no clear pattern has been observed. This systematic review and meta-analysis summarised current state of findings, expanded the range of cytokines, accounted for study risk of bias, and examined relations between cytokines and autism traits. Literature comparing peripheral blood cytokine in autistic and non-autistic people was systematically searched in Ovid® Embase, MEDLINE and APA PsycINFO, Web of Science™ and Scopus, resulting in 98 studies and 54 cytokines (4236 autistic, 3333 non-autistic controls; age 2 to 65 years) in the meta-analysis. Study risk of bias was assessed using adapted Newcastle-Ottawa Scale. Compared to controls, autistic people had elevated levels of IL1-beta (Hedges' g = 0.620, 95%CI[0.32, 0.92]), IL4 (g = 0.245, 95%CI [0.07, 0.42]), IL6 (g = 0.365, 95%CI [0.011, 0.62]), IL8 (g = 0.384, 95%CI [0.15, 0.62]), IFN-gamma (g = 0.404, 95%CI [0.09, 0.72]), TNF-alpha (g = 0.31, 95%CI [0.11, 0.51]), CXCL1/GRO-α (g = 0.364, 95%CI [0.058, 0.670]) and MIF (g = 0.560, 95%CI [0.14, 0.98]). Over a third of studies were classified as having a high risk of bias; their removal revealed higher IL7 and IL1RA in autism relative to controls. Narrative synthesis produced no strong evidence for an association between cytokine and autism traits among autistic individuals. Altogether, our findings support a predominance of pro-inflammatory cytokines, while also indicating potential modulatory contributions from inhibitory cytokines, which reflect group-level differences between autistic and non-autistic individuals, but not variations of autism traits within the autistic population. However, higher-quality studies with low risk of bias are needed before firm conclusions can be drawn.

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PubMed2026

Interhospital transfer and outcomes after robotic emergency general surgery: a national analysis.

The outcomes of patients transferred to receiving centers who subsequently undergo robotic EGS remain uncharacterized at a national level. We aimed to quantify the association between transfer and outcomes among adults undergoing robotic EGS. We performed a retrospective cohort study of the Nationwide Readmissions Database (2016-2019) including adult nonelective admissions undergoing robotic EGS. Interhospital transfer versus direct admission was the exposure. Survey-weighted logistic regression estimated adjusted odds ratios (aOR) for clinical outcomes; generalized linear models with gamma family and log link estimated adjusted mean ratios (aMR) for length of stay (LOS) and cost. Average marginal effects provided adjusted risks/means and absolute differences. Among 26,869 unweighted robotic EGS admissions, representing an estimated 46,517 admissions nationally, 246 unweighted admissions were interhospital transfers, representing an estimated 444 transfers (1.0%) nationally. Transfers were older, more comorbid, and more severely ill and were treated predominantly at large, teaching hospitals. After adjustment, transfer was associated with a higher risk of postprocedural complications (8.0% vs. 3.5%; aRR 2.26, 95% CI 1.25-3.27), non-home discharge (31.2% vs. 18.9%; aRR 1.65, 95% CI 1.38-1.92), longer LOS (11.49 vs. 5.53 days; AMR 2.08, 95% CI 1.78-2.42), and higher cost ($43,340 vs. $21,821; AMR 1.99, 95% CI 1.68-2.35). The association with postprocedural complications was attenuated after additional adjustment for APR-DRG Severity of Illness, whereas associations with non-home discharge, LOS, and cost persisted. Among patients undergoing robotic EGS, interhospital transfer is independently associated with higher complication burden and greater resource use. Transferred patients represent a small but distinctly high-risk subgroup whose worse outcomes may reflect drivers that extend beyond the choice of surgical approach.

Humans↗

PubMed2026

Apoptosis protein markers in comorbid type 2 diabetes mellitus and depression; relationships with cognitive performance, incident dementia, and white matter hyperintensities.

Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) are reciprocal risk factors, and both elevate dementia risk. Dysregulation of programmed cell death is implicated in T2DM, MDD, and neurodegeneration, but proteomic markers of apoptosis have yet to be studied as dementia predictors in people with T2DM and/or MDD. This study examines apoptosis markers in comorbid T2DM and MDD, and their associations with cognitive, dementia, and neuroimaging outcomes. The retrospective sample (n = 15,765) consisted of UK Biobank participants (MDD only n = 1230; T2DM only n = 3644; comorbid T2DM + MDD n = 721). Individuals with T2DM + MDD comorbidity had poorer cognitive performance, and a higher 15-year dementia incidence (HR = 4.44, 95% CI = [3.23,6.11]). Among 60 apoptosis-related proteins identified by Kyoto Encyclopedia of Genes and Genomes pathway enrichment, 41 were significantly up-regulated in comorbid T2DM + MDD relative to controls, and 4 were higher in the comorbid group than both T2DM alone and MDD alone. Tumor necrosis factor ligand superfamily member 10 (TNFSF10), growth arrest and DNA damage-inducible protein GADD45 beta, tumor necrosis factor ligand superfamily member 6, and RAC-gamma serine/threonine-protein kinase were associated with dementia risk. Nine proteins (e.g. apoptosis-inducing factor 1, mitochondrial, caspase-2, mitogen-activated protein kinase kinase kinase 5, TNFSF10), were associated with white matter hyperintensity volumes in comorbid T2DM + MDD after FDR correction, but none were associated with cognitive performance, atrophy, or white matter microstructural changes. These findings identify peripheral apoptosis markers that were further elevated in comorbid T2DM + MDD compared to either alone, pointing to an important pathophysiological element underlying adverse outcomes in the context of mood and metabolic comorbidity.

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PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

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