Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer.

BACKGROUND: Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear. METHODS: Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy. RESULTS: A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues. CONCLUSIONS: We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

Association between air pollution and male sexual function: A nationwide observational study in China.

This study aimed to explore the associations between air pollution and male sexual function. A total of 5047 male subjects in China were included in this study. The average air pollution exposure (PM2.5, PM10, SO2, CO, NO2, and O3) for the preceding 1, 3, 6, and 12 months before the participants' response was assessed. Male sexual function was evaluated using the International Index of Erectile Function-5 (IIEF-5) and the Premature Ejaculation Diagnostic Tool (PEDT). Generalized linear models were utilized to explore the associations between air pollution and male sexual function. K-prototype algorithm was conducted to identify the association among specific populations. Significant adverse effects on the IIEF-5 score were observed with NO2 exposure during the preceding 1, 3, and 6 months (1 m: ß = -5.26E-05; 3 m: ß = -4.83E-05; 6 m: ß = -4.23E-05, P < 0.05). PM2.5 exposure during the preceding 12 months was found to significantly negatively affect the PEDT after adjusting for confounding variables. Our research indicated negative correlations between air pollutant exposures and male sexual function for the first time. Furthermore, these associations were more pronounced among specific participants who maintain a normal BMI, exhibit extroverted traits, and currently engage in smoking and alcohol consumption.

Prognosis of LSPD versus TIPS for the treatment of esophagogastric variceal bleeding in cirrhosis.

BACKGROUND: This study aimed to compare postoperative complications in patients with esophagogastric variceal bleeding (EVB) who underwent laparoscopic splenectomy combined with pericardial devascularization (LSPD) versus transjugular intrahepatic portosystemic shunt (TIPS) procedures. METHODS: A retrospective collection of medical records was conducted from January 2014 to May 2020 at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. The study included patients from the departments of trauma surgery, interventional radiology, and general surgery who were diagnosed with EVB caused by portal hypertension and treated with LSPD or TIPS. Follow-up data were obtained to assess the occurrence of postoperative complications in both groups. RESULTS: A total of 201 patients were included in the study, with 104 cases in the LSPD group and 97 cases in the TIPS group. There was no significant difference in the 1-year and 3-year post-surgery survival rates between the TIPS and LSPD groups (P = 0.669, 0.066). The 3-year survival rate of Child-Pugh B patients in the LSPD group was higher than TIPS group (P = 0.041). The LSPD group also had a significantly higher rate of freedom from rebleeding at 3-year post-surgery compared to the TIPS group (P = 0.038). Stratified analysis showed no statistically significant difference in the rebleeding rate between the two groups. Furthermore, the LSPD group had a higher rate of freedom from overt hepatic encephalopathy at 1-year and 3-year post-surgery compared to the TIPS group (P = 0.007, < 0.001). The LSPD group also had a lower rate of severe complications at 3-year post-surgery compared to the TIPS group (P = 0.020). CONCLUSION: Compared to TIPS, LSPD does not increase the risk of mortality and rebleeding, while demonstrating fewer complications. In patients classified as Child-Pugh A and B, the use of LSPD for treating EVB is both safe and effective.

Rare Sagittal Compensatory Balance in Neuromuscular Scoliosis Complicated by Hydrocephalus: A Case Report.

Objective: This case report aims to present a rare case of thoracic lordosis and lumbar kyphosis and describe the posterior instrumented scoliosis correction performed. Case presentation: A 59-year-old female presented with low back pain. She had undergone ventriculoperitoneal shunt placement 8 years ago. I scored 76 on the Wechsler Adult Intelligence Scale. MRI of the lumbar spine showed spinal canal stenosis at L3/4, L4/5, and L5/S1. Full spine X-ray revealed thoracic lordosis and lumbar kyphoscoliosis， the coronal imbalance, and the sagittal compensatory balance. In order to avoid the risk of brain swelling and paraplegia, pedicle subtraction osteotomies (PSO) in the L2 and lumbar posterior instrumented scoliosis correction were performed under electroencephalogram and neuro electrophysiological monitoring. Shoulder imbalance was observed 1 year after surgery, but there was no loss of lumbar correction. Conclusion: In future cases of complex spinal deformity, it is important to observe whether there is cerebral ventricular dilatation on MRI before the operation. If severe thoracic lordosis is combined with lumbar scoliosis, over-correcting the lumbar scoliosis should be avoided to prevent shoulder imbalance.

Injectable Hydrogel To Deliver Bone Mesenchymal Stem Cells Preloaded with Azithromycin To Promote Spinal Cord Repair.

Spinal cord injury is a disease that causes severe damage to the central nervous system. Currently, there is no cure for spinal cord injury. Azithromycin is commonly used as an antibiotic, but it can also exert anti-inflammatory effects by down-regulating M1-type macrophage genes and up-regulating M2-type macrophage genes, which may make it effective for treating spinal cord injury. Bone mesenchymal stem cells possess tissue regenerative capabilities that may help promote the repair of the injured spinal cord. In this study, our objective was to explore the potential of promoting repair in the injured spinal cord by delivering bone mesenchymal stem cells that had internalized nanoparticles preloaded with azithromycin. To achieve this objective, we formulated azithromycin into nanoparticles along with a trans-activating transcriptional activator, which should enhance nanoparticle uptake by bone mesenchymal stem cells. These stem cells were then incorporated into an injectable hydrogel. The therapeutic effects of this formulation were analyzed in vitro using a mouse microglial cell line and a human neuroblastoma cell line, as well as in vivo using a rat model of spinal cord injury. The results showed that the formulation exhibited anti-inflammatory and neuroprotective effects in vitro as well as therapeutic effects in vivo. These results highlight the potential of a hydrogel containing bone mesenchymal stem cells preloaded with azithromycin and trans-activating transcriptional activator to mitigate spinal cord injury and promote tissue repair.

Association of cancer history with structural brain aging markers of Alzheimer's disease and related dementias risk.

INTRODUCTION: Cancer survivors are less likely than comparably aged individuals without a cancer history to develop Alzheimer's disease and related dementias (ADRD). METHODS: In the UK Biobank, we investigated associations between cancer history and five structural magnetic resonance imaging (MRI) markers for ADRD risk, using linear mixed-effects models to assess differences in mean values and quantile regression to examine whether associations varied across the distribution of MRI markers. RESULTS: Cancer history was associated with smaller mean hippocampal volume (b = -19 mm3 , 95% CI = -36, -1) and lower mean cortical thickness in the Alzheimer's disease signature region (b = -0.004 mm, 95% CI = -0.007, -0.000). Quantile regressions indicated individuals most vulnerable to ADRD were more affected by cancer history. DISCUSSION: Some brain MRI markers associated with ADRD risk were elevated in adults with a history of cancer. The magnitude of the adverse associations varied across quantiles of neuroimaging markers, and the pattern suggests possible harmful associations for individuals already at high ADRD risk. HIGHLIGHTS: We found no evidence of an inverse association between cancer history and ADRD-related neurodegeneration. Cancer history was associated with smaller mean hippocampal volume and lower mean cortical thickness in the Alzheimer's disease signature region. Quantile regressions indicated individuals most vulnerable to ADRD were more affected by cancer history.

Explainable machine learning model based on clinical factors for predicting the disappearance of indeterminate pulmonary nodules.

BACKGROUND: During lung cancer screening, indeterminate pulmonary nodules (IPNs) are a frequent finding. We aim to predict whether IPNs are resolving or non-resolving to reduce follow-up examinations, using machine learning (ML) models. We incorporated dedicated techniques to enhance prediction explainability. METHODS: In total, 724 IPNs (size 50-500 mm3, 575 participants) from the Dutch-Belgian Randomized Lung Cancer Screening Trial were used. We implemented six ML models and 14 factors to predict nodule disappearance. Random search was applied to determine the optimal hyperparameters on the training set (579 nodules). ML models were trained using 5-fold cross-validation and tested on the test set (145 nodules). Model predictions were evaluated by utilizing the recall, precision, F1 score, and the area under the receiver operating characteristic curve (AUC). The best-performing model was used for three feature importance techniques: mean decrease in impurity (MDI), permutation feature importance (PFI), and SHAPley Additive exPlanations (SHAP). RESULTS: The random forest model outperformed the other ML models with an AUC of 0.865. This model achieved a recall of 0.646, a precision of 0.816, and an F1 score of 0.721. The evaluation of feature importance achieved consistent ranking across all three methods for the most crucial factors. The MDI, PFI, and SHAP methods highlighted volume, maximum diameter, and minimum diameter as the top three factors. However, the remaining factors revealed discrepant ranking across methods. CONCLUSION: ML models effectively predict IPN disappearance using participant demographics and nodule characteristics. Explainable techniques can assist clinicians in developing understandable preliminary assessments.

Augmenting L3MBTL2-induced condensates suppresses tumor growth in osteosarcoma.

Osteosarcoma is a highly aggressive cancer and lacks effective therapeutic targets. We found that L3MBTL2 acts as a tumor suppressor by transcriptionally repressing IFIT2 in osteosarcoma. L3MBTL2 recruits the components of Polycomb repressive complex 1.6 to form condensates via both Pho-binding pockets and polybasic regions within carboxyl-terminal intrinsically disordered regions; the L3MBTL2-induced condensates are required for its tumor suppression. Multi-monoubiquitination of L3MBTL2 by UBE2O results in its proteasomal degradation, and the UBE2O/L3MBTL2 axis was crucial for osteosarcoma growth. There is a reverse correlation between L3MBTL2 and UBE2O in osteosarcoma tissues, and higher UBE2O and lower L3MBTL2 are associated with poorer prognosis in osteosarcoma. Pharmacological blockage of UBE2O by arsenic trioxide can enhance L3MBTL2-induced condensates and consequently suppress osteosarcoma growth. Our findings unveil a crucial biological function of L3MBTL2-induced condensates in mediating tumor suppression, proposing the UBE2O-L3MBTL2 axis as a potential cancer therapeutic target in osteosarcoma.

Case Report: Autoimmune encephalitis and other neurological syndromes with rare neuronal surface antibody in children after hematopoietic stem cell transplantation.

Introduction: Neuronal surface antibody syndromes (NSAS) encompass a growing set of autoimmune neurological disorders, with their predominant clinical presentation being autoimmune encephalitis (AE). The most extensively documented form within NSAS is anti-N-methyl-D-aspartate receptor (NMDAR) autoimmunity. In contrast, other NSAS, such as anti-metabotropic glutamate receptor-5 (mGluR5) autoimmunity, are less common and less comprehensively characterized, particularly in pediatric cases. Case description: In this instance, we present the case of a 7-year-old girl who exhibited abnormal behaviors following hematopoietic stem cell transplantation (HSCT). She received a diagnosis of anti-mGluR5 AE, and her Electroencephalogram (EEG) displayed an increased number of generalized slow waves during wakefulness. Treatment involved intravenous administration of gamma globulin and methylprednisolone, followed by oral prednisone tablets. Levetiracetam was introduced as an antiepileptic therapy during the pulse steroid therapy. Notably, the abnormal behaviors exhibited significant improvement after treatment. Conclusions: To the best of our knowledge, this is the first report of rare pediatric NSAS involving anti-mGluR5 AE following HSCT. Enhancing our understanding and characterization of this condition may facilitate its recognition and treatment in children. Serum antibody testing could enable early identification and treatment of anti-mGluR5 AE.

Association between cancer and dementia risk in the UK Biobank: evidence of diagnostic bias.

Epidemiological studies have identified an inverse association between cancer and dementia. Underlying methodological biases have been postulated, yet no studies have systematically investigated the potential for each source of bias within a single dataset. We used the UK Biobank to compare estimates for the cancer-dementia association using different analytical specifications designed to sequentially address multiple sources of bias, including competing risk of death, selective survival, confounding bias, and diagnostic bias. We included 140,959 UK Biobank participants aged ≥ 55 without dementia before enrollment and with linked primary care data. We used cancer registry data to identify cancer cases prevalent before UK Biobank enrollment and incident cancer diagnosed after enrollment. We used Cox models to evaluate associations of prevalent and incident cancer with all-cause dementia, Alzheimer's disease (AD), and vascular dementia. We used time-varying models to evaluate diagnostic bias. Over a median follow-up of 12.3 years, 3,310 dementia cases were diagnosed. All-site incident cancer was positively associated with all-cause dementia incidence (hazard ratio [HR] = 1.14, 95% CI: 1.02-1.29), but prevalent cancer was not (HR = 1.04, 95% CI: 0.92-1.17). Results were similar for vascular dementia. AD was not associated with prevalent or incident cancer. Dementia diagnosis was substantially elevated in the first year after cancer diagnosis (HR = 1.83, 95% CI: 1.42-2.36), after which the association attenuated to null, suggesting diagnostic bias. Following a cancer diagnosis, health care utilization or cognitive consequences of diagnosis or treatment may increase chance of receiving a dementia diagnosis, creating potential diagnostic bias in electronic health records-based studies.

Diabetes mellitus and risk of breast cancer: a large-scale, prospective, population-based study.

BACKGROUND: The objective of this study was to evaluate associations of diabetes overall, type 1 diabetes (T1D), and type 2 diabetes (T2D) with breast cancer (BCa) risk. METHODS: We included 250,312 women aged 40-69 years between 2006 and 2010 from the UK Biobank cohort. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were calculated for associations of diabetes and its two major types with the time from enrollment to incident BCa. RESULTS: We identified 8182 BCa cases during a median follow-up of 11.1 years. We found no overall association between diabetes and BCa risk (aHR = 1.02, 95% CI = 0.92-1.14). When accounting for diabetes subtype, women with T1D had a higher risk of BCa than women without diabetes (aHR = 1.52, 95% CI = 1.03-2.23). T2D was not associated with BCa risk overall (aHR = 1.00, 95% CI = 0.90-1.12). However, there was a significantly increased risk of BCa in the short time window after T2D diagnosis. CONCLUSIONS: Though we did not find an association between diabetes and BCa risk overall, an increased risk of BCa was observed shortly after T2D diagnosis. In addition, our data suggest that women with T1D may have an increased risk of BCa.

Preparing CT imaging datasets for deep learning in lung nodule analysis: Insights from four well-known datasets.

BACKGROUND: Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering. METHODS: The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order. FINDINGS: The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided. DISCUSSION AND CONCLUSION: Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.

Dysregulation of immune response in PCOS organ system.

Polycystic ovary syndrome (PCOS) is the most common reproductive endocrine disorder affecting women, which can lead to infertility. Infertility, obesity, hirsutism, acne, and irregular menstruation are just a few of the issues that PCOS can be linked to. PCOS has a complicated pathophysiology and a range of clinical symptoms. Chronic low-grade inflammation is one of the features of PCOS. The inflammatory environment involves immune and metabolic disturbances. Numerous organ systems across the body, in addition to the female reproductive system, have been affected by the pathogenic role of immunological dysregulation in PCOS in recent years. Insulin resistance and hyperandrogenism are associated with immune cell dysfunction and cytokine imbalance. More importantly, obesity is also involved in immune dysfunction in PCOS, leading to an inflammatory environment in women with PCOS. Hormone, obesity, and metabolic interactions contribute to the pathogenesis of PCOS. Hormone imbalance may also contribute to the development of autoimmune diseases. The aim of this review is to summarize the pathophysiological role of immune dysregulation in various organ systems of PCOS patients and provide new ideas for systemic treatment of PCOS in the future.

A Nomogram Model for Predicting Prognosis in Spontaneous Intracerebral Hemorrhage Patients.

OBJECTIVES: Intracranial hemorrhage is the second most common stroke subtype following ischemic stroke and usually induces high mortality and disability. Here, we conducted a retrospective study to establish a nomogram clinical prediction model. METHODS: First, the baseline data of patients who presented to our hospital in 2015-2021 were collected and compared (789 patients for the training cohort and 378 patients for the validation cohort). Second, univariate and binary logistic analyses were performed to screen out alternative indicators. Finally, a clinical prediction model by nomogram was established that included such indicators to estimate the prognosis of intracranial hemorrhage patients. RESULTS: Univariate logistic analysis was used to screen several possible impact factors, including hypertension, hematoma volume, Glasgow Coma Scale (GCS) score, intracranial hemorrhage (ICH) score, irregular shape, uneven density, intraventricular hemorrhage (IVH) relation, fibrinogen, D-dimer, low density lipoprotein (LDL), high-density lipoprotein (HDL), creatinine, total protein, hemoglobin (HB), white blood cell (WBC), neutrophil blood cell (NBC), lymphocyte blood cell (LBC), the neutrophil lymphocyte ratio (NLR), surgery, deep venous thrombosis (DVT) or pulmonary embolism (PE) rate, hospital day, and hypertension control. Further binary logistic analysis revealed that ICH score (p = 0.036), GCS score (p = 0.000), irregular shape (p = 0.000), uneven density (p = 0.002), IVH relation (p = 0.014), surgery (p = 0.000) were independent indicators to construct a nomogram clinical prediction model. The C statistic was 0.840. CONCLUSIONS: ICH score, GCS score, irregular shape, uneven density, IVH relation, surgery are easily available indicators to assist neurologists in formulating the most appropriate therapy for every intracranial hemorrhage patient. Further large prospective clinical trials are needed to obtain more integrated and reliable conclusions.

Effect of Thermal Treatment on the Self-Assembly of Wheat Gluten Polypeptide.

Self-assembled fibrillation of wheat gluten is a common phenomenon in the daily production and processing of wheat flour products. The driving forces for its formation and the factors that influence the morphology of fibrils have not been thoroughly investigated. In this study, the effect of three bonding changes (breaking hydrogen bonds, strengthening hydrophobic interactions, and SH-SS exchange reactions) on gluten polypeptide (GP) fibrillation was simulated by adjusting the heating temperature (room temperature (RT), 45 °C, 65 °C, and 95 °C). The results showed that the breakage of hydrogen bonds could induce conformational transitions in GPs and help to excite fibrillation in GPs. Strengthened hydrophobic interactions significantly contributed to the fibrillation of GPs. Covalent crosslinks generated by SH-SS exchange reactions might also promote the fibrillation of GPs. GPs with different degrees of hydrolysis (4.0%, 6.0%, and 10.0%, represented by DH 4, DH 6, and DH 10, respectively) presented different extents of fibrillation, with DH 10 GPs having a higher propensity to fibrillation than DH 4 and DH 6 GPs. The results of Fourier's transform infrared spectroscopy indicated that hydrophobic interactions drive the transition from a random coil and α-helix to a ß-sheet. In addition, hydrophobic interactions also drive the intermolecular polymerization of GPs, resulting in larger molecular weight aggregates. The morphology presented by transmission electron microscopy showed that the greater the DH, the stronger the tendency for the worm-like aggregation of GPs.

RMI2 is a novel prognostic and predictive biomarker for breast cancer.

BACKGROUND: RecQ-mediated genome instability 2 (RMI2) maintains genome stability by promoting DNA damage repair. It has been reported to accelerate the progression of several tumors. However, the functional mechanism of RMI2 in breast cancer remains unclear. METHODS: Gene expression profiles were obtained from TCGA, GTEx, and GEO databases. The expression of RMI2 and its prognostic value in breast cancer was explored. In addition, we calculated pooled standardized mean deviation (SMD) and performed a summary receiver operating characteristic (sROC) curve analysis to further determine RMI2 expression status and diagnostic significance. The functions and related signaling pathways were investigated based on GO and KEGG analyses. The PPI network was constructed by combining the STRING database and Cytoscape software. Subsequently, in vitro assays were conducted to detect the effect of RMI2 on the proliferation and migration of breast cancer cells. RESULTS: The expression of RMI2 was markedly upregulated in breast cancer tissues relative to that in normal tissues. Moreover, pooled SMD further confirmed the overexpression of RMI2 in breast cancer (SMD = 1.29, 95% confidence interval (CI): 1.18-1.41, p = 0.000). The sROC curve analysis result suggested that RMI2 had a relatively high diagnostic ability in breast cancer (AUC = 0.87, 95% CI: 0.84-0.90). High RMI2 expression was associated with poor prognosis. GO and KEGG analyses revealed that RMI2 was closely related to cell adhesion, various enzyme activities, and PI3K/AKT signaling pathway. PPI analysis showed that RMI2 had interactions with proteins involved in DNA damage repair. knockdown of RMI2 remarkably inhibited the proliferation and migration of breast cancer cells, while overexpression of RMI2 exerted the opposite effects. Furthermore, we identified that RMI2 accelerates the proliferation and migration of breast cancer cells via activation of the PI3K/AKT pathway. CONCLUSION: The results suggest that RMI2 is a potential diagnostic and prognostic biomarker associated with cell proliferation and migration, and may be used as a novel therapeutic target for breast cancer in the future.