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Background The features and survival of stage IV breast cancer patients with different metastatic sites are poorly understood. This study aims to examine the clinicopathological features and survival of stage IV breast cancer patients according to different metastatic sites. Methods Using the Surveillance, Epidemiology, and End Results database, we restricted our study population to stage IV breast cancer patients diagnosed between 2010 to 2015. The clinicopathological features were examined by chi-square tests. Breast cancer-specific survival (BCSS) and overall survival (OS) were compared among patients with different metastatic sites by the Kaplan-Meier method with log-rank test. Univariable and multivariable analyses were also performed using the Cox proportional hazard model to identify statistically significant prognostic factors. Results A total of 18,322 patients were identified for survival analysis. Bone-only metastasis accounted for 39.80% of patients, followed by multiple metastasis (33.07%), lung metastasis (10.94%), liver metastasis (7.34%), other metastasis (7.34%), and brain metastasis (1.51%). The Kaplan-Meier plots showed that patients with bone metastasis had the best survival, while patients with brain metastasis had the worst survival in both BCSS and OS ( p  < 0.001, for both). Multivariable analyses showed that age, race, marital status, grade, tumor subtype, tumor size, surgery of primary cancer, and a history of radiotherapy or chemotherapy were independent prognostic factors. Conclusion Stage IV breast cancer patients have different clinicopathological characteristics and survival outcomes according to different metastatic sites. Patients with bone metastasis have the best prognosis, and brain metastasis is the most aggressive subgroup.

Cancer is one of the most important public health problems in the world. The curative effect of traditional surgery, radiotherapy and chemotherapy is limited and has inevitable side effects. As a potential target for tumor therapy, few studies have comprehensively analyzed the role of CALR in cancers. Therefore, by using GeneCards, UALCAN, GEPIA, Kaplan-Meier Plotter, COSMIC, Regulome Explorer, String, GeneMANIA and TIMER databases, we collected and analyzed relevant data to conduct in-depth bioinformatics research on the CALR expression in Pan-cancer to assess the possibility of CALR as a potential therapeutic target and survival biomarker. We studied the CALR expression in normal human tissues and various tumors of different stages, and found that CALR expression was associated with relapse free survival (RFS). We verified the expression of CALR in breast cancer cell lines by vitro experiments. Mutations of CALR were widely present in tumors. CALR interacted with different genes and various proteins. In tumors, a variety of immune cells are closely related to CALR. In conclusion, CALR can be used as a biomarker for predicting prognosis and a potential target for tumor molecular and immunotherapy.

Background Triple Negative Breast cancer (TNBC) is incurable cancer with higher rates of relapse and shorter overall survival compared with other subtypes of breast cancer. Cellular retinoic acid binding protein 2 (CRABP2) belongs to fatty acid binding protein (FABP) family which binds with all-trans retinoic acid (RA). Previous studies from the database have reported the patients with high expression of CRABP2 showed different prognosis in ER + and ER − breast cancer. However, its biological role and exact mechanism in breast cancer remain unknown. This aim of this study was to explore how CRABP2 regulated invasion and metastasis based on the estrogen receptor-α (herein called ER) status in breast cancer. Methods Immunohistochemical staining method was used to analyze the expression of CRABP2 in human breast cancer tissues. Lentivirus vector-based shRNA technique was used to test the functional relevance of CRABP2 knockdown in breast tumors. Tail vein injection model was used to examine the lung metastasis. Co-immunoprecipitation, Western blotting, immunofluorescence, and quantitative reverse transcription polymerase chain reaction (RT-qPCR) were conducted to investigate the underlying mechanism that influenced the ER to the regulation of CRABP2 to Lats1. Results We observed that knockdown of CRABP2 promotes EMT, invasion and metastasis of ER + breast cancer cells in vitro and in vivo, whereas overexpression of CRABP2 yields the reverse results. In ER + mammary cancer cells, the interaction of CRABP2 and Lats1 suppress the ubiquitination of Lats1 to activate Hippo pathway to inhibit the invasion and metastasis of ER + mammary cancer. However, in ER − mammary cancer cells, the interaction of CRABP2 and Lats1 promote the ubiquitination of Lats1 to inactivate Hippo pathway to promote the invasion and metastasis of ER − mammary cancer. Conclusions Our findings indicate that CRABP2 can suppress invasion and metastasis of ER + breast cancer and promote invasion and metastasis of ER − breast cancer by regulating the stability of Lats1 in vitro and in vivo, and it provides new ideas for breast cancer therapy.

Background Circulating cell-free RNA (cfRNA) is gaining recognition as an effective biomarker for the early detection of preeclampsia (PE). However, the current methods for selecting disease-specific biomarkers are often inefficient and typically one-dimensional. Purpose This study introduces a Pyramid Scene Parsing Network (PSPNet) model to predict PE, aiming to improve early risk assessment using cfRNA profiles. Methods The theoretical maximum Preeclamptic Risk Index (PRI) of patients clinically diagnosed with PE is defined as “1”, and the control group (NP) is defined as “0”, referred to as the clinical PRI. A data preprocessing algorithm was used to screen relevant cfRNA indicators for PE. The cfRNA expression profiles were obtained from the Gene Expression Omnibus (GSE192902), consisting of 180 normal pregnancies (NP) and 69 preeclamptic (PE) samples, collected at two gestational time points: ≤ 12 weeks and 13–20 weeks. Based on the differences in cfRNA expression profiles, the Calculated Ground Truth values of the NP and PE groups in the sequencing data were acquired (Calculated PRI). The differential algorithm was embedded in the PSPNet neural network and the network was then trained using the generated dataset. Subsequently, the real-world sequencing dataset was used to validate and optimize the network, ultimately outputting the PRI values of the healthy control group and the PE group (PSPNet-based PRI). The model’s predictive ability for PE was evaluated by comparing the fit between Calculated PRI (Calculated Ground Truth) and PSPNet-based PRI. Results The mean absolute error (MAE) between the Calculated Ground Truth the PSPNet-based PRI was 0.0178 for cfRNA data sampled at ≤ 12 gws and 0.0195 for data sampled at 13–20 gws. For cfRNA data sequenced at ≤ 12 gws and 13–20 gws, the corresponding loss values, maximum absolute errors, peak-to-valley error values, mean absolute errors, and average prediction times per sample were 0.0178 (0.0195). Conclusions The present PSPNet model is reliable and fast for cfRNA-based PE prediction and its PRI output allows for continuous PE risk monitoring, introducing an innovative and effective method for early PE prediction. This model enables timely interventions and better management of pregnancy complications, particularly benefiting densely populated developing countries with high PE incidence and limited access to routine prenatal care.

The Aphis gossypii is a major threat of cotton worldwide due to its short life cycle and rapid reproduction. Chemical control is the primary method used to manage the cotton aphid, which has significant environmental impacts. Therefore, prioritizing eco-friendly alternatives is essential for managing the cotton aphid. The ladybird, Hippodamia variegata , is a predominant predator of the cotton aphid. Its performance in cotton plantation is directly linked to chemical communication, where volatile compounds emitted from aphid-infested plants play important roles in successful predation. Here, we comprehensively studied the chemical interaction between the pest, natural enemy and host plants by analyzing the volatile profiles of aphid-infested cotton plants using gas chromatography-mass spectrometry (GC-MS). We then utilized the identified volatile compounds in electrophysiological recording (EAG) and behavioral assays. Through behavioral tests, we initially demonstrated the clear preference of both larvae and adults of H. variegata for aphid-infested plants. Subsequently, 13 compounds, namely α-pinene, cis -3-hexenyl acetate, 4-ethyl-1-octyn-3-ol, β-ocimene, dodecane, E-β-farnesene, decanal, methyl salicylate, β-caryophyllene, α-humulene, farnesol, DMNT, and TMTT were identified from aphid-infested plants. All these compounds were electrophysiologically active and induced detectable EAG responses in larvae and adults. Y-tube olfactometer assays indicated that, with few exceptions for larvae, all identified chemicals were attractive to H. variegata , particularly at the highest tested concentration (100 mg/ml). The outcomes of this study establish a practical foundation for developing attractants for H. variegata and open avenues for potential advancements in aphid management strategies by understanding the details of chemical communication at a tritrophic level.

R-loops are prevalent in mammalian genomes and involved in many fundamental cellular processes. Depletion of BRCA2 leads to aberrant R-loop accumulation, contributing to genome instability. Here, we show that ZFP281 cooperates with BRCA2 in preventing R-loop accumulation to facilitate DNA replication in embryonic stem cells. ZFP281 depletion reduces PCNA levels on chromatin and impairs DNA replication. Mechanistically, we demonstrate that ZFP281 can interact with BRCA2, and that BRCA2 is enriched at G/C-rich promoters and requires both ZFP281 and PRC2 for its proper recruitment to the bivalent chromatin at the genome-wide scale. Furthermore, depletion of ZFP281 or BRCA2 leads to accumulation of R-loops over the bivalent regions, and compromises activation of the developmental genes by retinoic acid during stem cell differentiation. In summary, our results reveal that ZFP281 recruits BRCA2 to the bivalent chromatin regions to ensure proper progression of DNA replication through preventing persistent R-loops. R-loops are prevalent in mammalian genomes and involved in many fundamental cellular processes. Here, Wang et al. report that ZFP281 cooperates with BRCA2 in preventing R-loop accumulation to facilitate DNA replication in embryonic stem cells.

Wind energy is contributing to more and more portions in the world energy market. However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines. In particular, large wind turbines are expensive, with less tolerance for system performance degradations, unscheduled system shut downs, and even system damages caused by various malfunctions or faults occurring in system components such as rotor blades, hydraulic systems, generator, electronic control units, electric systems, sensors, and so forth. As a result, there is a high demand to improve the operation reliability, availability, and productivity of wind turbine systems. It is thus paramount to detect and identify any kinds of abnormalities as early as possible, predict potential faults and the remaining useful life of the components, and implement resilient control and management for minimizing performance degradation and economic cost, and avoiding dangerous situations. During the last 20 years, interesting and intensive research results were reported on fault diagnosis, prognosis, and resilient control techniques for wind turbine systems. This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade. Finally, an overlook on the future development of the fault diagnosis, prognosis, and resilient control techniques for wind turbine systems is presented.

Local anesthetics are used clinically for the control of postoperative pain management. This study aimed to develop chitosan (CS) with genipin (GP) hydrogels as the hydrophilic lipid shell loaded poly(ε-caprolactone) (PC) nanocapsules as the hydrophobic polymeric core composites (CS-GP/PC) to deliver bupivacaine (BPV) for the prolongation of anesthesia and pain relief. The swelling ratio, in vitro degradation, and rheological properties enhancement of CS-GP/PC polymeric hydrogel. The incorporation of PC nanocapsules into CS-GP hydrogels was confirmed by SEM, FTIR, and XRD analysis. Scanning electron microscopy results demonstrated that the CS-GP hydrogels and CS-GP/PC polymeric hydrogels have a porous structure, the pore dimensions being non-uniform with diameters between 25 and 300 μm. The in vitro drug release profile of CS-GP/PC polymeric hydrogel has been achieved 99.2 ± 1.12% of BPV drug release in 36 h. Cellular viability was evaluated using the CCK-8 test on 3T3 fibroblast cells revealed that the obtained CS-GP/PC polymeric hydrogel with BPV exhibited no obvious cytotoxicity. The CS-GP/PC polymeric hydrogel loaded with BPV showed significant improvement in pain response compared to the control group animals for at least 7 days. When compared with BPV solution, CS-GP hydrogel and CS-GP/PC polymeric hydrogel improved the skin permeation of BPV 3-fold and 5-fold in 24 h, respectively. In vitro and in vivo results pointed out PC nanocapsules loaded CS-GP hydrogel can act as effective drug carriers, thus prolonging and enhancing the anesthetic effect of BPV. Histopathological results demonstrated the excellent biodegradability and biocompatibility of the BPV-loaded CS-GP/PC polymeric hydrogel system on 7, 14, and 21 days without neurotoxicity. HIGHLIGHTS Preparation and characterization of CS-GP/PC polymeric hydrogel system. BPV-loaded CS-GP/PC exhibited prolonged in vitro release in PBS solution. Cytotoxicity of BPV-loaded CS-GP/PC polymeric hydrogel against fibroblast (3T3) cells. Development of CS-GP/PC a promising skin drug-delivery system for local anesthetic BPV.

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients’ responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients’ EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients’ outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%–100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.