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BackgroundCurrent online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions. ObjectiveWe aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China. MethodsThis study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT. ResultsThe training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values. ConclusionsOur prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China.

BackgroundRisk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. ObjectiveThis study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. MethodsA dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. ResultsThe three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. ConclusionsThe novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

PURPOSE: The OlympiA randomized phase III trial compared 1 year of olaparib (OL) or placebo (PL) as adjuvant therapy in patients with germline BRCA1/2, high-risk human epidermal growth factor receptor 2-negative early breast cancer after completing (neo)adjuvant chemotherapy ([N]ACT), surgery, and radiotherapy. The patient-reported outcome primary hypothesis was that OL-treated patients may experience greater fatigue during treatment. METHODS: Data were collected before random assignment, and at 6, 12, 18, and 24 months. The primary end point was fatigue, measured with the Functional Assessment of Chronic Illness Therapy-Fatigue scale. Secondary end points, assessed with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, Core 30 item, included nausea and vomiting (NV), diarrhea, and multiple functional domains. Scores were compared between treatment groups using mixed model for repeated measures. Two-sided P values

Infection, autoimmunity, and cancer are principal human health challenges of the 21st century. Often regarded as distinct ends of the immunological spectrum, recent studies hint at potential overlap between these diseases. For example, inflammation can be pathogenic in infection and autoimmunity. T resident memory (TRM) cells can be beneficial in infection and cancer. However, these findings are limited by size and scope; exact immunological factors shared across diseases remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune/post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation and increased humoral immunity and that they resemble TRM cells. Our results suggest NKG2A+ biases as a cross-disease factor of protection, supporting suggestions of immunological overlap between infection, autoimmunity, and cancer.

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.

Abstract Neurons rely heavily on high mitochondrial metabolism to provide sufficient energy for proper development. However, it remains unclear how neurons maintain high oxidative phosphorylation (OXPHOS) during development. Mitophagy plays a pivotal role in maintaining mitochondrial quality and quantity. We herein describe that G protein-coupled receptor 50 (GPR50) is a novel mitophagy receptor, which harbors the LC3-interacting region (LIR) and is required in mitophagy under stress conditions. Although it does not localize in mitochondria under normal culturing conditions, GPR50 is recruited to the depolarized mitochondrial membrane upon mitophagy stress, which marks the mitochondrial portion and recruits the assembling autophagosomes, eventually facilitating the mitochondrial fragments to be engulfed by the autophagosomes. Mutations Δ502-505 and T532A attenuate GPR50-mediated mitophagy by disrupting the binding of GPR50 to LC3 and the mitochondrial recruitment of GPR50. Deficiency of GPR50 causes the accumulation of damaged mitochondria and disrupts OXPHOS, resulting in insufficient ATP production and excessive ROS generation, eventually impairing neuronal development. GPR50-deficient mice exhibit impaired social recognition, which is rescued by prenatal treatment with mitoQ, a mitochondrially antioxidant. The present study identifies GPR50 as a novel mitophagy receptor that is required to maintain mitochondrial OXPHOS in developing neurons.

Infection, autoimmunity, and cancer are the principal human health challenges of the 21st century and major contributors to human death and disease. Often regarded as distinct ends of the immunological spectrum, recent studies have hinted there may be more overlap between these diseases than appears. For example, pathogenic inflammation has been demonstrated as conserved between infection and autoimmune settings. T resident memory (TRM) cells have been highlighted as beneficial for infection and cancer. However, these findings are limited by patient number and disease scope; exact immunological factors shared across disease remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune and post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation, increased humoral immunity, and resemble TRM cells. Our results suggest that an NKG2A+ bias is a pan-disease immunological factor of protection and thus supports recent suggestions that there is immunological overlap between infection, autoimmunity, and cancer. Our findings underscore the promotion of an NKG2A+ biased response as a putative therapeutic strategy.

Flexible graphite film (FGF), as a traditional interface heat dissipation material, has high anisotropy. It is a challenge to enhance both in-plane and through-plane thermal conductivity of FGF. For this reason, the effects of oxygen content, layer spacing, density and particle size on the in-plane and through-plane thermal conductivity of FGF were studied by both molecular simulation and experimental investigation. The simulation results indicate that the ways to improve the thermal conductivity of FGF include reducing oxygen content and layer spacing, increasing the density and matching the size of graphite sheets. The FGF prepared from room temperature exfoliated graphite (RTFGF) has a wide range of adjustable density (1.3–2.0 g/cm3) and thickness (50–400 μm). The thermal conductivity of the RTFGF is significantly improved after heat treatment owing to reduced oxygen content and layer spacing, which is consistent with the simulation results. Moreover, RTFGF with both high in-plane (518 W⋅m−1⋅K−1) and through-plane (7.2 W⋅m−1⋅K−1) thermal conductivity can be obtained by particle size matching of graphite.

Objective To investigate the effect of inhibiting the expression of RNA component of mitochondrial RNA processing endoribonuclease(RMRP) on the proliferation and invasion of human lung cancer cell line A549 in order to provide evidence to support the research on NSCLC mechanism. Methods A total of 122 cases of patients who underwent surgical treatment were selected in Jiaozuo People′s Hospital from March 2016 to March 2021. In the same period, 50 healthy people from physical examination center were selected as the control group. RT-qPCR was used to detect the expressions of RMRP in blood and tissues. Human lung cancer cell line A549 was cultured and divided into si-RMRP group, siRNA-NC group and blank group.RT-qPCR, CCK-8 and Transwell assays were used to detect the expression of RMRP, proliferation activity and cell counting of invasive cells. Results The relative expression level of RMRP in the blood of NSCLC patients was significantly higher than that of control group (P＜0.001). The relative expression level of RMRP in NSCLC tissues was significantly higher than the adjacent tissues (P＜0.001). The relative expression level of RMRP in blood and tissues of patients with poorly differentiated, lymph node metastasis and TNM stage Ⅲ compared with moderate to highly differentiated, no lymph node metastasis and TNM stage Ⅰ-Ⅱ were significantly increased (P＜0.05). The relative expression level of RMRP in the cells of the si-RMRP group was lower than that of blank and the siRNA-NC group (P＜0.001). Compared with the blank group and the siRNA-NC group, the absorbance (A) value of cells at 24, 48, 72 and 96 h in the si-RMRP group was decreased (P＜0.05). The number of invasion cells in the si-RMRP group was lower than that in the blank group and the siRNA-NC group (F=27.765, P＜0.001). Conclusions The relative expression levels of RMRP in blood and tissues of NSCLC patients are increased. Down-regulation of the expression of RMRP gene in A549 cells can inhibit cell proliferation and reduce cell invasion.

Acute kidney disease (AKD) is a critical transitional period between acute kidney injury and chronic kidney disease. The incidence of AKD following acute kidney injury is approximately 33.6%, and it can occur without identifiable preceding acute kidney injury. The development of AKD is associated with increased risks of chronic kidney disease, dialysis, and mortality. Biomarkers and subphenotypes are promising tools to predict prognosis in AKD. The complex clinical situations in patients with AKD necessitate a comprehensive and structured approach, termed “KAMPS” (kidney function check, advocacy, medications, pressure, sick day protocols). We introduce “MAND-MASS,” an acronym devised to summarize the reconciliation of medications during episodes of acute illness, as a critical component of the sick day protocols at AKD. A multidisciplinary team care, consisting of nephrologists, pharmacists, dietitians, health educators, and nurses, is an optimal model to achieve the care bundle in KAMPS. Although the evidence for patients with AKD is still lacking, several potential pharmacological agents may improve outcomes, including but not limited to angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, mineralocorticoid receptor antagonists, sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide 1 receptor agonists. In conclusion, accurate prognosis prediction and effective treatment for AKD are critical yet unmet clinical needs. Future studies are urgently needed to improve patient care in this complex and rapidly evolving field.

Background: The significant rebound of influenza A (H1N1) virus activity, particularly among children, with rapidly growing number of hospitalized cases is of major concern in the post-COVID-19 era. The present study was performed to establish a prediction model of severe case in pediatric patients hospitalized with H1N1 infection during the post-COVID-19 era. Methods: This is a multicenter retrospective study across nine public tertiary hospitals in Yunnan, China, recruiting pediatric H1N1 inpatients hospitalized at five of these centers between February 1 and July 1, 2023, into the development dataset. Screening of 40 variables including demographic information, clinical features, and laboratory parameters were performed utilizing Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression to determine independent risk factors of severe H1N1 infection, thus constructing a prediction nomogram. Receiver operating characteristic (ROC) curve, calibration curve, as well as decision curve analysis (DCA) were employed to evaluate the model's performance. Data from four independent cohorts comprised of pediatric H1N1 inpatients from another four hospitals between July 25 and October 31, 2023, were utilized to externally validate this nomogram. Results: The development dataset included 527 subjects, 122 (23.1 %) of whom developed severe H1N1 infection. The external validation dataset included 352 subjects, 72 (20.5 %) of whom were eventually confirmed as severe H1N1 infection. The LASSO regression identified 19 candidate predictors, with logistic regression further narrowing down to 11 independent risk factors, including underlying conditions, prematurity, fever duration, wheezing, poor appetite, leukocyte count, neutrophil-lymphocyte ratio (NLR), erythrocyte sedimentation rate (ESR), lactate dehydrogenase (LDH), interleukin-10 (IL-10), and tumor necrosis factor-α (TNF-α). By integrating these 11 factors, a predictive nomogram was established. In terms of prediction of severe H1N1 infection, excellent discriminative capacity, favorable accuracy, and satisfactory clinical usefulness of this model were internally and externally validated via ROC curve, calibration curve, and DCA, respectively. Conclusion: Our study successfully established and validated a novel nomogram model integrating underlying conditions, prematurity, fever duration, wheezing, poor appetite, leukocyte count, NLR, ESR, LDH, IL-10, and TNF-α. This nomogram can effectively predict the occurrence of serious case in pediatric H1N1 inpatients during the post-COVID-19 era, facilitating the early recognition and more efficient clinical management of such patients.

Abstract Cat states, as an important resource in the study of macroscopic quantum superposition and quantum information applications, have garnered widespread attention. To date, preparing large-sized optical cat states has remained challenging. We demonstrate that, by utilizing interaction-free measurement and the quantum Zeno effect, even a fragile quantum microscopic system can deterministically control and become entangled with strong light fields, thereby generating large-amplitude optical cat states. During the entire preparation process, our method ensures that the microscopic system functions within a weak field environment, so that its quantum property can be protected. Furthermore, we show that the preparation of cat states is possible even when the quantum microsystem suffers from significant photon loss, provided that optical losses from classical devices are kept low, which implies that the fidelity of the cat state can be enhanced by improvements to and the perfection of the classical optical system.

Abstract Background The rebound of influenza A (H1N1) infection in post-COVID-19 era recently attracted enormous attention due the rapidly increased number of pediatric hospitalizations and the changed characteristics compared to classical H1N1 infection in pre-COVID-19 era. This study aimed to evaluate the clinical characteristics and severity of children hospitalized with H1N1 infection during post-COVID-19 period, and to construct a novel prediction model for severe H1N1 infection. Methods A total of 757 pediatric H1N1 inpatients from nine tertiary public hospitals in Yunnan and Shanghai, China, were retrospectively included, of which 431 patients diagnosed between February 2023 and July 2023 were divided into post-COVID-19 group, while the remaining 326 patients diagnosed between November 2018 and April 2019 were divided into pre-COVID-19 group. A 1:1 propensity-score matching (PSM) was adopted to balance demographic differences between pre- and post-COVID-19 groups, and then compared the severity across these two groups based on clinical and laboratory indicators. Additionally, a subgroup analysis in the original post-COVID-19 group (without PSM) was performed to investigate the independent risk factors for severe H1N1 infection in post-COIVD-19 era. Specifically, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select candidate predictors, and logistic regression was used to further identify independent risk factors, thus establishing a prediction model. Receiver operating characteristic (ROC) curve and calibration curve were utilized to assess discriminative capability and accuracy of the model, while decision curve analysis (DCA) was used to determine the clinical usefulness of the model. Results After PSM, the post-COVID-19 group showed longer fever duration, higher fever peak, more frequent cough and seizures, as well as higher levels of C-reactive protein (CRP), interleukin 6 (IL-6), IL-10, creatine kinase-MB (CK-MB) and fibrinogen, higher mechanical ventilation rate, longer length of hospital stay (LOS), as well as higher proportion of severe H1N1 infection (all P

An unbalanced protocol recognition method based on the improved Residual U-Net was proposed to solve the challenge of network security posed by the increasing network attacks with the continuous development of the Internet.In the captured network traffic, a small proportion is constituted by malicious traffic, typically utilizing minority protocols.However, existing protocol recognition methods struggle to accurately identify these minority protocols when the class distribution of the protocol data is imbalanced.To address this issue, an unbalanced protocol recognition method was proposed, which utilized the improved Residual U-Net, incorporating a novel activation function and the Squeeze-and-Excitation Networks (SE-Net) to enhance the feature extraction capability.The loss function employed in the proposed model was the weighted Dice loss function.In cases where the recognition accuracies of the minority protocols were low, the loss function value would be high.Consequently, the optimization direction of the model would be dominated by the minority protocols, resulting in improved recognition accuracies for them.During the protocol recognition process, the network flow was extracted from the network traffic and preprocessed to convert it into a one-dimensional matrix.Subsequently, the protocol recognition model extracted the features of the protocol data, and the Softmax classifier predicted the protocol types.Experimental results demonstrate that the proposed protocol recognition model achieves more accurate recognition of the minority protocols compared to the comparison model, while also improving the recognition accuracies of the majority protocols.