Prognostic and clinicopathological significance of systemic immune-inflammation index in upper tract urothelial carcinoma: a meta-analysis of 3911 patients.

Background: Systemic immune-inflammation index (SII), a novel prognostic indicator, is being more commonly utilized in different types of cancer. This research project involved combining information from previously published studies to examine how pre-treatment SII can predict outcomes in individuals with upper tract urothelial carcinoma (UTUC). Further examination of the correlation between SII and clinical and pathological features in UTUC. Methods: We thoroughly chose pertinent articles from various databases including PubMed, Embase, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI), WanFang database, and Chinese Scientific Journal Database (VIP) until March 10, 2022.The data collected was analyzed using Stata 17.0 software (Stat Corp, College Station, TX). Subsequently, the impact of SII on the survival outcomes of UTUC patients was evaluated by combining HRs with 95% confidence intervals. Results: Six included studies were finally confirmed, including 3911 UTUC patients in seven cohorts. The results showed that high SII before treatment predicted poor overall survival (HR =1.87, 95%CI 1.20-2.92, p=0.005), cancer specific survival (HR=2.70, 95%CI 1.47-4.96, P=0.001), and recurrence-free survival (HR =1.52, 95%CI 1.12-2.07, P=0.007). And the elevated SII may be related to LVI (present vs. absent) (OR=0.83, 95% CI=0.71-0.97, p=0.018), pT stage (pT ≥3 vs. < 3) (OR=1.82, 95% CI=1.21-2.72, p=0.004), and pN stage (N+ vs. N0) (OR=3.27, 95% CI=1.60-6.71, p=0.001). Conclusion: A comprehensive analysis of all included articles in this study showed that higher pretreatment SII was related to poorer survival outcomes and adverse pathological features independently. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022316333.

Multiple extreme overdose of antipsychotics with minimal side effects: A case report.

Key Clinical Message: Antipsychotic drug treatment is a commonly used therapeutic strategy in the field of psychiatry. Rational and standardized use of antipsychotics is crucial in clinical practice, and excessive use of antipsychotics may lead to severe toxic reactions. Thus, attention should be given to the monitoring of drug concentration and examination of organ function. Abstract: Excessive use of antipsychotics can cause a variety of adverse effects, including dysfunction of the liver and other organs. Liver cytochrome P450 (CYP450) enzymes play an important role in the metabolism of antipsychotics, and metabolizer types of CYP450 enzymes may influence the therapeutic effects. In this case report, we introduced a 52-year-old woman with a 23-year history of schizophrenia who took excessive doses of multiple antipsychotics and other herbal preparations for nearly 2 years, with poor response to treatment and minor side reactions to the antipsychotics. Pharmacogenomic examination showed that this patient was a CYP1A2 ultra-rapid metabolizer. The examination and treatment of this patient may provide a reference for the management of similar cases with poor response to an alarming tolerance for antipsychotics.

Machine learning models for predicting thermal desorption remediation of soils contaminated with polycyclic aromatic hydrocarbons.

Among various remediation methods for organic-contaminated soil, thermal desorption stands out due to its broad treatment range and high efficiency. Nonetheless, analyzing the contribution of factors in complex soil remediation systems and deducing the results under multiple conditions are challenging, given the complexities arising from diverse soil properties, heating conditions, and contaminant types. Machine learning (ML) methods serve as a powerful analytical tool that can extract meaningful insights from datasets and reveal hidden relationships. Due to insufficient research on soil thermal desorption for remediation of organic sites using ML methods, this study took organic pollutants represented by polycyclic aromatic hydrocarbons (PAHs) as the research object and sorted out a comprehensive data set containing >700 data points on the thermal desorption of soil contaminated with PAHs from published literature. Several ML models, including artificial neural network (ANN), random forest (RF), and support vector regression (SVR), were applied. Model optimization and regression fitting centered on soil remediation efficiency, with feature importance analysis conducted on soil and contaminant properties and heating conditions. This approach enabled the quantitative evaluation and prediction of thermal desorption remediation effects on soil contaminated with PAHs. Results indicated that ML models, particularly the RF model (R2 = 0.90), exhibited high accuracy in predicting remediation efficiency. The hierarchical significance of the features within the RF model is elucidated as follows: heating conditions account for 52 %, contaminant properties for 28 %, and soil properties for 20 % of the model's predictive power. A comprehensive analysis suggests that practical applications should emphasize heating conditions for efficient soil remediation. This research provides a crucial reference for optimizing and implementing thermal desorption in the quest for more efficient and reliable soil remediation strategies.

Real-world retrospective study of prostate-specific antigen and safety assessment with darolutamide plus androgen deprivation therapy for metastasis hormone-sensitive prostate cancer.

Background: ARASENS has demonstrated the efficacy and safety for darolutamide (DARO) with androgen deprivation therapy (ADT) plus docetaxel in metastasis hormone-sensitive prostate cancer (mHSPC). There is a lack of reports for DARO with ADT in mHSPC though the regimen is used in clinical from time to time. Moreover, recent studies have supported the importance of early and rapid prostate-specific antigen (PSA) reduction, which correlates with reduced disease progression and improved survival in patients with mHSPC. This study aims to evaluate PSA reduction as a primary endpoint for DARO with ADT in the treatment of mHSPC and to evaluate the real-world short-term PSA control of DARO with ADT from two leading medical centers in China. Methods: We retrospectively reviewed the clinical records of patients with mHSPC receiving ADT and DARO (600 mg, b.i.d.). The collection of data spanned from March 1, 2022, to July 31, 2023. The main observation indicators were PSA level and drug-related adverse events (AE) after medication. PSA levels were closely monitored prior to treatment initiation and at 2-week intervals, as well as at 1, 3, and 6 months after the initiation of treatment. We also conducted an analysis to determine the proportion of patients achieving a PSA reduction of 50% or more (PSA50) and 90% or more (PSA90) as well as the percentage of patients with a notable decrease in PSA level to 0.2 ng/mL and PSA nadir of ≤0.02 ng/mL. Results: Fifty-one patients were included in the study, with a median age of 73 years. At diagnosis of HSPC, the majority of patients had a Gleason score ≥8 (n=40, 78.40%) and a median baseline PSA level of 88 ng/mL. Approximately 45.1% (n=23) of patients had a Charlson Comorbidity Index over 1 and were receiving one or more nontumor-related treatments. The median follow-up time was 9.3 months (range, 1.16-15.8 months). The median reductions in PSA levels compared to baseline were 84.37%, 91.48%, 94.67% and 99.81% at 2 weeks, 1 month, 3 months and 6 months after administration of DARO with ADT, respectively. The median time to PSA50, PSA90, significant PSA reduction (PSA <0.2 ng/mL), and PSA nadir (PSA <0.02 ng/mL) was 0.97, 1.27, 1.98, and 2.08 months, respectively. AE mainly included fatigue (two patients) and arm pain (one patient), all of which were grade I or II AE. No grade III or AE were observed. Conclusions: For treating prostate cancer, DARO with ADT has good early efficacy, demonstrating prompt and substantial control of PSA levels, with a favorable safety profile.

Identification of Anoikis-Related Genes in Spinal Cord Injury: Bioinformatics and Experimental Validation.

Spinal cord injury (SCI) is a serious disease without effective therapeutic strategies. To identify the potential treatments for SCI, it is extremely important to explore the underlying mechanism. Current studies demonstrate that anoikis might play an important role in SCI. In this study, we aimed to identify the key anoikis-related genes (ARGs) providing therapeutic targets for SCI. The mRNA expression matrix of GSE45006 was downloaded from the Gene Expression Omnibus (GEO) database, and the ARGs were downloaded from the Molecular Signatures Database (MSigDB database). Then, the potential differentially expressed ARGs were identified. Next, correlation analysis, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) analysis were employed for the differentially expressed ARGs. Moreover, miRNA-gene networks were constructed by the hub ARGs. Finally, RNA expression of the top ten hub ARGs was validated in the SCI cell model and rat SCI model. A total of 27 common differentially expressed ARGs were identified at different time points (1, 3, 7, and 14 days) following SCI. The GO and KEGG enrichment analysis of these ARGs indicated several enriched terms related to proliferation, cell cycle, and apoptotic process. The PPI results revealed that most of the ARGs interacted with each other. Ten hub ARGs were further screened, and all the 10 genes were validated in the SCI cell model. In the rat model, only seven genes were validated eventually. We identified 27 differentially expressed ARGs of the SCI through bioinformatic analysis. Seven real hub ARGs (CCND1, FN1, IGF1, MYC, STAT3, TGFB1, and TP53) were identified eventually. These results may expand our understanding of SCI and contribute to the exploration of potential SCI targets.

Construction of functional neural network tissue combining CBD-NT3-modified linear-ordered collagen scaffold and TrkC-modified iPSC-derived neural stem cells for spinal cord injury repair.

Induced pluripotent stem cells (iPSCs) can be personalized and differentiated into neural stem cells (NSCs), thereby effectively providing a source of transplanted cells for spinal cord injury (SCI). To further improve the repair efficiency of SCI, we designed a functional neural network tissue based on TrkC-modified iPSC-derived NSCs and a CBD-NT3-modified linear-ordered collagen scaffold (LOCS). We confirmed that transplantation of this tissue regenerated neurons and synapses, improved the microenvironment of the injured area, enhanced remodeling of the extracellular matrix, and promoted functional recovery of the hind limbs in a rat SCI model with complete transection. RNA sequencing and metabolomic analyses also confirmed the repair effect of this tissue from multiple perspectives and revealed its potential mechanism for treating SCI. Together, we constructed a functional neural network tissue using human iPSCs-derived NSCs as seed cells based on the interaction of receptors and ligands for the first time. This tissue can effectively improve the therapeutic effect of SCI, thus confirming the feasibility of human iPSCs-derived NSCs and LOCS for SCI repair and providing a valuable direction for SCI research.

Liquid Metal Composites-Enabled Real-Time Hand Gesture Recognizer with Superior Recognition Speed and Accuracy.

Prosthetic hands play a vital role in restoring forearm functionality for patients who have suffered hand loss or deformity. The hand gesture intention recognition system serves as a critical component within the prosthetic hand system. However, accurately and swiftly identifying hand gesture intentions remains a challenge in existing approaches. Here, a real-time motion intention recognition system utilizing liquid metal composite sensor bracelets is proposed. The sensor bracelet detects pressure signals generated by forearm muscle movements to recognize hand gesture intent. Leveraging the remarkable pressure sensitivity of liquid metal composites and the efficient classifier based on the optimized recognition algorithm, this system achieves an average offline and real-time recognition accuracy of 98.2% and 92.04%, respectively, with an average recognition speed of 0.364 s. Thus, this wearable system shows advantages in superior recognition speed and accuracy. Furthermore, this system finds applications in master-slave control of prosthetic hands in unmanned scenarios, such as electrically powered operations, space exploration, and telemedicine. The proposed system promises significant advances in next-generation intent-controlled prosthetic hands and robots.

Combination of hyperthermia and intravesical chemotherapy for the treatment of pT1 stage bladder cancer: A retrospectively clinical study.

PURPOSE: To evaluate the efficiency and safety of combined local bladder hyperthermia and intravesical chemotherapy (IVC) for the treatment of patients with pT1 stage bladder cancer. METHOD: A total of 189 patients with pT1 who underwent transurethral resection of bladder cancer (TURBT) were retrospectively reviewed. After TURBT, the patients with low-grade urothelial carcinoma (UC) were treated with either an IVC with pirarubicin (THP) protocol or chemo-thermotherapy (CHT) with THP protocol, whereas patients with high-grade UC were treated with either an intravesical immunotherapy (IVI) with bacillus Calmette-Guerin (BCG) protocol or CHT protocol, patients' characteristics, tumor biological features, and follow-up data were analyzed and compared between CHT and IVC group in low-grade UC, CHT, and IVI group in high-grade UC, respectively. RESULTS: The median follow-up time was 24 months. In patients with low-grade UC, the median recurrence free survival (RFS) interval and costs of treatment in CHT group were significantly higher than those in IVC group (p = .01, p < .001, respectively), CHT was associated with higher RFS compared with IVC by Kaplan-Meier analysis, and three patients in IVC group upgraded to high grade when tumor recurred, whereas no cases were found upgraded in CHT group, p = .38. In patients with high-grade UC, tumor recurrence rates at 12 (p = .004) and 24 months (p = .004) after TURBT, rate of complications (p = .04)-especially for hematuresis (p = .03) and irritation symptoms (p = .04)-the median costs of treatment (p < .001) in CHT group were significantly lower than those in IVI group, RFS interval, health-related quality of life) at 12 and 24 months after TURBT in CHT group was significantly higher than those in IVI group (p < .001, p = .002, and p < .001, respectively), and CHT was associated with higher RFS compared with IVI by Kaplan-Meier analysis. The rate of patients upstaged to pT2 in CHT group seemed lower than that in IVI group, but there was no significantly statistical difference (14.3% vs. 24%, p = .58). CONCLUSION: CHT has a beneficial prophylactic effect in patients with pT1 bladder cancer, especially in patients with high-grade UC, which is much more effective and safer than BCG, meanwhile it costs less compared with BCG.

Fish-Wearable Piezoelectric Nanogenerator for Dual-Modal Energy Scavenging from Fish-Tailing.

Aiming to develop a self-powered bioelectric tag for fish behavioral studies, here we present a fish-wearable piezoelectric nanogenerator (FWPNG) that can simultaneously harvest the strain energy and the flow impact energy caused by fish-tailing. The FWPNG is fabricated by transferring a 2 µm-thick Nb0.02-Pb(Zr0.6Ti0.4)O3 (PZT) layer from a silicon substrate to a spin-coated polyimide film via a novel zinc oxide (ZnO) release process. The open-circuit voltage of the strain energy harvester reaches 2.3 V under a strain of 1% at an ultra-low frequency of 1 Hz, and output voltage of the impact energy harvester reaches a 0.3 V under a pressure of 82.6 kPa at 1 Hz, which is in good agreement with our theoretical analysis. As a proof-of-concept demonstration, an event-driven underwater acoustic transmitter is developed by utilizing the FWPNG as a trigger switch. Acoustic transmission occurs when the amplitude of fish-tailing is larger than a preset threshold. The dual-modal FWPNG device shows the potential application in self-powered biotags for animal behavioral studies and ocean explorations.

Classification related to immunogenic cell death predicts prognosis, immune microenvironment characteristics, and response to immunotherapy in lower-grade gliomas.

Background: Immunogenic cell death (ICD) is a form of cell death that elicits immune responses against the antigens found in dead or dying tumor cells. Growing evidence implies that ICD plays a significant role in triggering antitumor immunity. The prognosis for glioma remains poor despite many biomarkers being reported, and identifying ICD-related biomarkers is imminent for better-personalized management in patients with lower-grade glioma (LGG). Materials and methods: We identified ICD-related differentially expressed genes (DEGs) by comparing gene expression profiles obtained across Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) cohorts. On the foundation of ICD-related DEGs, two ICD-related clusters were identified through consensus clustering. Then, survival analysis, functional enrichment analysis, somatic mutation analysis, and immune characteristics analysis were performed in the two ICD-related subtypes. Additionally, we developed and validated a risk assessment signature for LGG patients. Finally, we selected one gene (EIF2AK3) from the above risk model for experimental validation. Results: 32 ICD-related DEGs were screened, dividing the LGG samples from the TCGA database into two distinct subtypes. The ICD-high subgroup showed worse overall survival (OS), greater immune infiltration, more active immune response process, and higher expression levels of HLA genes than the ICD-low subgroup. Additionally, nine ICD-related DEGs were identified to build the prognostic signature, which was highly correlated with the tumor-immune microenvironment and could unambiguously be taken as an independent prognostic factor and further verified in an external dataset. The experimental results indicated that EIF2AK3 expression was higher in tumors than paracancerous tissues, and high-expression EIF2AK3 was enriched in WHO III and IV gliomas by qPCR and IHC, and Knockdown of EIF2AK3 suppressed cell viability and mobility in glioma cells. Conclusion: We established novel ICD-related subtypes and risk signature for LGG, which may be beneficial to improving clinical outcome prediction and guiding individualized immunotherapy.

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry.

De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly developing fields with lead candidate discovery in the drug development process. First, artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This article explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.

Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations.

As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions.

Leveraging a gene signature associated with disulfidptosis identified by machine learning to forecast clinical outcomes, immunological heterogeneities, and potential therapeutic targets within lower-grade glioma.

Background: Disulfidptosis, a newly defined type of programmed cell death, has emerged as a significant regulatory process in the development and advancement of malignant tumors, such as lower-grade glioma (LGG). Nevertheless, the precise biological mechanisms behind disulfidptosis in LGG are yet to be revealed, considering the limited research conducted in this field. Methods: We obtained LGG data from the TCGA and CGGA databases and performed comprehensive weighted co-expression network analysis, single-sample gene set enrichment analysis, and transcriptome differential expression analyses. We discovered nine genes associated with disulfidptosis by employing machine learning methods like Cox regression, LASSO regression, and SVM-RFE. These were later used to build a predictive model for patients with LGG. To confirm the expression level, functional role, and impact on disulfidptosis of ABI3, the pivotal gene of the model, validation experiments were carried out in vitro. Results: The developed prognostic model successfully categorized LGG patients into two distinct risk groups: high and low. There was a noticeable difference in the time the groups survived, which was statistically significant. The model's predictive accuracy was substantiated through two independent external validation cohorts. Additional evaluations of the immune microenvironment and the potential for immunotherapy indicated that this risk classification could function as a practical roadmap for LGG treatment using immune-based therapies. Cellular experiments demonstrated that suppressing the crucial ABI3 gene in the predictive model significantly reduced the migratory and invasive abilities of both SHG44 and U251 cell lines while also triggering cytoskeletal retraction and increased cell pseudopodia. Conclusion: The research suggests that the prognostic pattern relying on genes linked to disulfidptosis can provide valuable insights into the clinical outcomes, tumor characteristics, and immune alterations in patients with LGG. This could pave the way for early interventions and suggests that ABI3 might be a potential therapeutic target for disulfidptosis.

Liquid Metal-Tailored PEDOT:PSS for Noncontact Flexible Electronics with High Spatial Resolution.

Electric field-based noncontact flexible electronics (EF-NFEs) allow people to communicate with intelligent devices through noncontact human-machine interactions, but current EF-NFEs with limited detections (usually <20 cm) distance often lack a high spatial resolution. Here, we report a versatile material for preparing EF-NFE devices with a high spatial resolution to realize everyday human activity detection. Eutectic gallium-indium alloy (EGaIn) was introduced into poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) chains to fabricate this material, named Ga-PP. The introduction of EGaIn successfully regulates the intra- and interchain interactions of PEDOT chains and thus increases the π-electron accumulation on Ga-PP chains, which facilitates improvement of the electron storage of Ga-PP and its noncontact sensing ability. The water solubility of the obtained Ga-PP can reach approximately 15 mg/mL, comparable to that of commercial PEDOT:PSS, thus making Ga-PP suitable for various design strategies to prepare EF-NFE devices. We demonstrate that a conductive textile with a noncontact sensing ability can be achieved by immersing a commercial silk fabric into a Ga-PP solution for 5 min. With a detection distance exceeding 1 m, the prepared Ga-PP-based conductive textile (Ga-PP-CT) possesses outstanding noncontact sensing sensitivity, showing advantages in tracing the locations of signal sources and distinguishing motion states. Surprisingly, even when placed in water, Ga-PP-CT can be used to monitor the movement signals of athletes in different sporting events and output specific noncontact response signals for different sports. Intriguingly, the Ga-PP solution itself can be used to construct noncontact sensing conductive circuits, displaying the potential to be incorporated into smart electronics.

The values of systemic immune-inflammation index and neutrophil-lymphocyte ratio in predicting testicular germ cell tumors: A retrospective clinical study.

Purpose: To determine whether complete blood count (CBC) based inflammatory parameters can be used as markers predicting testicular germ cell tumors (TGCT). Material and methods: Between 2013 to 2018 the data of 58 patients with testicular TGCT undergoing radical orchiectomy and 54 malignancy-free healthy men were retrospectively analyzed as tumor group and control group. Patient baseline characteristics including age, pathological stage and pre-surgery CBC based inflammatory parameters including neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), systemic immune-inflammation index (SII), lymphocyte ratio (LR), neutrophil ratio (NR), mean platelet volume (MPV) and red cell distribution width (RDW) were analyzed and compared between tumor group and control group. Receiver operating characteristic (ROC) curve were used analyzing data with significantly difference to assess the discriminative ability of the markers for TGCT, area under the curve (AUC), cut-off value, sensitivity and specificity were calculated. The binary logistic regression model was used to evaluate the association between significant inflammatory markers and risk of TGCT. Results: Mean age of the tumor and control group was 41.1 ± 15.36 and 44.89 ± 9.2 years, respectively. Mean NLR, SII and RDW were significantly higher in tumor group compared with control group with P=0.005, P=0.001 and P=0.016, respectively; there were no significantly differences of age, PLR, LMR, LR, NR, MPV and RDW between groups. The ROC curve for NLR, SII and RDW was plotted in the diagnosis of TGCT and tumor progression, the cut-off value for NLR, SII and RDW were found as 3.38 (AUC: 0.704, sensitivity=51.4%, specificity=88.6%, P=0.003), 881.24 (AUC: 0.725, sensitivity=45.7%, specificity=91.4%, P=0.001) and 0.14 (AUC: 0.63, sensitivity=28.6%, specificity=97%, P=0.063), respectively. Patients were divided into two groups according to the threshold values, respectively. By using the multivariable logistic regression models, NLR ≥ 3.38 (OR, 5.86; 95% CI, 1.67-20.65, P=0.006) and SII ≥ 881.24 (OR, 4.89; 95% CI, 1.48-15.32, P=0.009) were independent risk factors predicting TGCT. Significantly statistical difference of pathological stage was also found between groups with respect to NLR cut-off values (P=0.034) and SII cut-off values (P=0.049). Combined the data together, NLR and SII both exhibited good differential diagnosis potential which could be used as markers predicting the TGCT. Conclusion: As the CBC based inflammation parameters, both NLR and SII could be used as effective tumor markers predicting the TGCT, and higher NLR and SII are associated with higher pathological stage. In addition, SII is a more powerful tool among these two inflammatory markers.

The role of lncRNA just proximal to XIST (JPX) in human disease phenotypes and RNA methylation: The novel biomarker and therapeutic target potential.

Increasing evidence suggests that long non-coding RNAs (lncRNAs) are closely related to the initialization and development of human diseases. lncRNA just proximal to XIST (JPX), as a newly identified lncRNA, has been reported to be aberrantly expressed and associated with pathophysiological traits in numerous diseases, particularly cancers. More importantly, JPX has been proven to play important roles in various biological functions, including cell proliferation, migration, invasion, apoptosis, chemoresistance, and differentiation. In addition, we discuss the diverse molecular mechanisms and correlation with RNA methylation of JPX in several cancers. In this Review, we summarize current studies on JPX's roles in diseases and its potential application as a biomarker for both diagnoses and prognoses and a therapeutic target in human diseases.

Integrated analysis of inflammatory response subtype-related signature to predict clinical outcomes, immune status and drug targets in lower-grade glioma.

Background: The inflammatory response in the tumor immune microenvironment has implications for the progression and prognosis in glioma. However, few inflammatory response-related biomarkers for lower-grade glioma (LGG) prognosis and immune infiltration have been identified. We aimed to construct and identify the prognostic value of an inflammatory response-related signature, immune infiltration, and drug targets for LGG. Methods: The transcriptomic and clinical data of LGG samples and 200 inflammatory response genes were obtained from public databases. The LGG samples were separated into two inflammatory response-related subtypes based on differentially expressed inflammatory response genes between LGG and normal brain tissue. Next, inflammatory response-related genes (IRRGs) were determined through a difference analysis between the aforementioned two subtypes. An inflammatory response-related prognostic model was constructed using IRRGs by using univariate Cox regression and Lasso regression analyses and validated in an external database (CGGA database). ssGSEA and ESTIMATE algorithms were conducted to evaluate immune infiltration. Additionally, we performed integrated analyses to investigate the correlation between the prognostic signature and N 6-methyladenosine mRNA status, stemness index, and drug sensitivity. We finally selected MSR1 from the prognostic signature for further experimental validation. Results: A total of nine IRRGs were identified to construct the prognostic signature for LGG. LGG patients in the high-risk group presented significantly reduced overall survival than those in the low-risk group. An ROC analysis confirmed the predictive power of the prognostic model. Multivariate analyses identified the risk score as an independent predictor for the overall survival. ssGSEA revealed that the immune status was definitely disparate between two risk subgroups, and immune checkpoints such as PD-1, PD-L1, and CTLA4 were significantly expressed higher in the high-risk group. The risk score was strongly correlated with tumor stemness and m6A. The expression levels of the genes in the signature were significantly associated with the sensitivity of tumor cells to anti-tumor drugs. Finally, the knockdown of MSR1 suppressed LGG cell migration, invasion, epithelial-mesenchymal transition, and proliferation. Conclusion: The study constructed a novel signature composed of nine IRRGs to predict the prognosis, potential drug targets, and impact immune infiltration status in LGG, which hold promise for screening prognostic biomarkers and guiding immunotherapy for LGG.

A practical and economical method for frontal sinus reconstruction after frontal craniotomy: A single-center experience with 140 patients.

Background: Frontal sinus exposure is a common consequence of frontal craniotomy. Cerebrospinal fluid leakage and infection are the major postoperative complications that may occur as a result of the open frontal sinus. The successful filling of the open frontal sinus provides an approach to prevent significant complications caused by frontal sinus exposure. Objective: This article describes a new technique to reconstruct the exposed frontal sinus cavity with the combined application of gelatin sponge and a vascularized pericranial flap. Methods: A total of 140 patients underwent frontal sinus reconstruction using gelfoam and vascularized pericranial flaps from 2016 to 2021. Gelatin sponge was used to fill the frontal sinus, and a vascularized pericranial flap was used to cover the frontal sinus when the bone flap was retracted. Results: Postoperative cerebrospinal fluid leakage and infection did not occur in any patient. Conclusion: Our results validated the effectiveness of our technique in the prevention of exposed frontal sinus-related postoperative complications.

Cancer associated fibroblasts secreted exosomal miR-1290 contributes to prostate cancer cell growth and metastasis via targeting GSK3ß.

Cancer-associated fibroblasts (CAFs) play crucial roles in mediating tumor growth and metastasis via transferring exosomes to neighboring cells, whereas the mechanisms by which CAFs regulate the tumorgenesis of prostate cancer (PC) remain largely unknown. In this study, CAFs and normal fibroblasts (NFs) were isolated from PC tissues and adjacent normal tissues, respectively. Exosomes (NFs-Exo and CAFs-Exo) were then isolated from the supernatant of NFs and CAFs. Next, the differentially expressed miRNAs (DEMs) between NFs-Exo and CAFs-Exo were identified using RNA-sequencing. Cell viability, migration and invasion were detected with CCK-8 and Transwell assays. Protein expression was measured with western blot. We found that CAFs-Exo remarkably enhanced PC cell migration, invasion, stemness, epithelial-mesenchymal transition (EMT) and metastasis. Significantly, miR-1290 level was upregulated in CAFs-Exo compared to NFs-Exo. In addition, CAFs could transfer exosomes to PC cells, resulting in a marked increase of miR-1290 level in cells. Moreover, exosomal miR-1290 could inhibit GSK3ß/ß-catenin signaling by binding with the downstream target GSK3ß mRNA. Meanwhile, miR-1290 antagomir notably reversed the effects of CAFs-Exo on PC cells through activating GSK3ß/ß-catenin signaling. Collectively, exosomal miR-1290 from CAFs could promote PC cell growth and metastasis via inhibiting GSK3ß/ß-catenin signaling, suggesting that miR-1290 may serve as potential therapeutic target for the treatment of PC.

The Values of Systemic Immune-Inflammation Index and Neutrophil-Lymphocyte Ratio in Predicting Biochemical Recurrence in Patients With Localized Prostate Cancer After Radical Prostatectomy.

Purpose: To investigate the association between preoperative systemic immune-inflammation index (SII) and neutrophil-lymphocyte ratio (NLR) and oncological outcomes in localized prostate cancer (PCa) patients after radical prostatectomy (RP). Methods: Between January 2014 and December 2019, 291 patients with pathologically confirmed localized PCa who underwent RP were included in this study. The threshold values of SII and NLR for biochemical recurrence (BCR) were calculated according to Youden's index based on the receiver operating characteristic (ROC) curve, then the patients were divided into two groups by the threshold values of SII and NLR, and the clinicopathological outcomes were analyzed and compared between groups, respectively. The binary logistic regression model was used to evaluate the association between SII, NLR, and pathological outcomes including Gleason score (GS) and pathological T (pT) stage. Kaplan-Meier curves and univariable and multivariable Cox regression models were used to determine the association between high SII, high NLR, and BCR-free survival, respectively. Results: The median follow-up time was 48 months (IQR 36-62), and 114 (39.18%) patients developed BCR. The AUC of SII for BCR was 0.813 (P < 0.001), with a threshold value of 528.54, a sensitivity of 72.9%, and a specificity of 76.3%; the AUC of NLR for BCR was 0.824 (P < 0.001), with a threshold value of 2.62, a sensitivity of 71.2%, and a specificity of 81.6%. Patients were divided into two groups according to the threshold values of SII and NLR, respectively. Patients in the high SII group had higher tPSA, GS, pT stage, and BCR rate than patients in the low SII group (P = 0.004, 0.04, 0.007, and <0.001, respectively), and patients in the high NLR group had higher tPSA, GS, pT stage, and BCR rate than patients in the low NLR group (P = 0.04, 0.02, 0.006, and <0.001, respectively). Multivariable logistic regression analysis revealed that high SII was significantly correlated with adverse pathological outcomes of GS (HR, 1.656; 95% CI, 1.00-2.742, P = 0.042) and pT stage (HR, 1.478; 95% CI, 0.972-3.64, P = 0.028); there was no association between high NLR and pathological events. Kaplan-Meier analysis showed significantly poorer BCR-free survival in patients with high SII or high NLR (P < 0.001 and <0.001, respectively). By using the multivariable Cox regression model, high SII (HR, 4.521; 95% CI, 2.262-9.037, P < 0.001) and high NLR (HR, 4.787; 95% CI, 2.339-9.798, P < 0.001) were both significant predictors of BCR after RP. Conclusion: High SII was significantly related to unfavorable clinicopathological outcomes. High preoperative SII and NLR were related to higher BCR rate in localized PCa after RP, and they were all independent risk factors associated with shorter BCR-free survival. These two factors might provide promising and inexpensive methods for predicting clinical outcomes in patients with RP.