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.

Identification of collagen genes related to immune infiltration and epithelial-mesenchymal transition in glioma.

BACKGROUND: Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression. METHODS: We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assays were used to measure the cell migration and invasion capacity. Western blotting was used to test the expression of epithelial-mesenchymal transition (EMT) related markers. RESULTS: We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the EMT process of glioma. Finally, taking COL3A1 as a further research object, the results showed that knockdown of COL3A1 significantly inhibited the migration, invasion, and EMT process of SHG44 and A172 cells. CONCLUSIONS: In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.

METTL7B is a novel prognostic biomarker of lower-grade glioma based on pan-cancer analysis.

Methyltransferase-like 7B (METTL7B) is a member of the methyltransferase-like protein family that plays an important role in the development and progression of tumors. However, its prognostic value and the correlation of METTL7B expression and tumor immunity in some cancers remain unclear. By analyzing online data, we found that METTL7B is abnormally overexpressed in multiple human tumors and plays an important role in the overall survival (OS) of patients with 8 cancer types and disease-free survival (DFS) of patients with 5 cancer types. Remarkably, METTL7B expression was positively correlated with the OS and DFS of patients with lower-grade glioma (LGG). In addition, a positive correlation between METTL7B expression and immune cell infiltration in LGG was observed. Moreover, we identified a strong correlation between METTL7B expression and immune checkpoint gene expression in kidney chromophobe (KICH), LGG and pheochromocytoma and paraganglioma (PCPG). Furthermore, METTL7B was involved in the extracellular matrix (ECM) and immune-related pathways in LGGs. Finally, in vitro experiments showed that knockdown of METTL7B inhibited the growth, migration, invasion and the epithelial-mesenchymal transition (EMT) of LGG cells. METTL7B expression potentially represents a novel prognostic biomarker due to its significant association with immune cell infiltration in LGG.

Quantum Chemistry in the Age of Quantum Computing.

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.

Diagnostic and Predictive Values of Inflammatory Factors in Pathology and Survival of Patients Undergoing Total Cystectomy.

BACKGROUND: Inflammation and tumorigenesis are related. We conducted this study to evaluate whether inflammatory factors (IFs) have a diagnostic value for pathology and a predictive value for survival and recurrence in bladder cancer patients undergoing total cystectomy. METHODS: The patients who were diagnosed with bladder cancer and underwent total cystectomy in our center from 2014 to 2020 were enrolled. The values of neutrophil to lymphocyte ratio (NLR), derived neutrophil to lymphocyte ratio (dNLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and systemic immune-inflammation index (SII) were calculated by blood routine test results before operation. The AUC of ROC was calculated to judge the diagnostic value of the IFs in pathology and their corresponding cut-off values. For overall survival (OS) and recurrence-free survival (RFS), the above IFs were grouped according to the cut-off value. The differences between different groups were analyzed by the Kaplan-Meier curves, and the predictive value of these IFs was determined by the Cox proportional hazards regression model. RESULTS: A total of 79 patients were enrolled. All IFs had no diagnostic value for the pathological grade, tumor T stage, and systemic metastasis. Only NLR (AUC = 0.706, cut-off value = 3.12, sensitivity = 75.00%, specificity = 70.00%, P = 0.014), dNLR (AUC = 0.700, cut-off value = 2.49, sensitivity = 66.67%, specificity = 76.67%, P = 0.015), and SII (AUC = 0.704, cut-off value = 463.56, sensitivity = 100.00%, specificity = 40.00%, P = 0.004) had a diagnostic value for lymph node metastasis. The median follow-up time was 31 months, and there was no significant difference in OS between the two groups for all IFs. For RFS, Kaplan-Meier suggested PLR might be predictive when the cut-off value was 266.70 (P = 0.044), but the subsequent Cox proportional hazards regression analysis showed that all IFs had no predictive value for OS and RFS. CONCLUSIONS: We found that in patients undergoing total cystectomy preoperative NLR, dNLR and SII had a diagnostic value for lymph node metastasis, while all these five IFs had no predictive value for OS and RFS. However, this conclusion needs to be further verified by large-scale studies in the future.

Precursor-Transformation Strategy Preparation of CuPx Nanodots-Decorated CoP3 Nanowires Hybrid Catalysts for Boosting pH-Universal Electrocatalytic Hydrogen Evolution.

The development of earth-abundant, low cost, and versatile electrocatalysts for producing hydrogen from water electrolysis is still challenging. Herein, based on high hydrogen evolution reaction (HER) activity of transition metal phosphides, a CoP3 nanowire decorated with copper phosphides (denoted as CuPx ) nanodots structures synthesized through a simple and easily scalable precursor-transformation strategy is reported as a highly efficient HER catalyst. By decorating with CuPx nanodots, the optimized CoP3 nanowires electrode exhibits excellent catalytic activity and long-term durability for HER in alkaline conditions, achieving a low overpotential of 49.5 mV at a geometrical catalytic current density of 10 mA cm-2 with a small Tafel slope of 58.0 mV dec-1 , while also performing quite well in neutral and acidic media. Moreover, its overall performance exceeds most of the reported state-of-the-art catalysts, especially under high current density of 100 mA cm-2 , demonstrating its potential as a promising versatile pH universal electrocatalyst for efficient water electrolysis. These results indicate that the incorporation of earth-abundant stable element copper can significantly enhance catalytic activity, which widens the application range of copper and provides a new path for design and selection of HER catalysts.

Amorphous Sn/Crystalline SnS2 Nanosheets via In Situ Electrochemical Reduction Methodology for Highly Efficient Ambient N2 Fixation.

Electrochemical nitrogen reduction reaction (NRR) as a new strategy for synthesizing ammonia has attracted ever-growing attention, due to its renewability, flexibility, and sustainability. However, the lack of efficient electrocatalysts has hampered the development of such reactions. Herein, a series of amorphous Sn/crystalline SnS2 (Sn/SnS2 ) nanosheets by an L-cysteine-based hydrothermal process, followed by in situ electrochemical reduction, are synthesized. The amount of reduced amorphous Sn can be adjusted by selecting electrolytes with different pH values. The optimized Sn/SnS2 catalyst can achieve a high ammonia yield of 23.8 µg h-1 mg-1 , outperforming most reported noble-metal NRR electrocatalysts. According to the electrochemical tests, the conversion of SnS2 to an amorphous Sn phase leads to the substantial increase of its catalytic activity, while the amorphous Sn is identified as the active phase. These results provide a guideline for a rational design of low-cost and highly active Sn-based catalysts thus paving a wider path for NRR.

Fingerprint-Inspired Flexible Tactile Sensor for Accurately Discerning Surface Texture.

Inspired by the epidermal-dermal and outer microstructures of the human fingerprint, a novel flexible sensor device is designed to improve haptic perception and surface texture recognition, which is consisted of single-walled carbon nanotubes, polyethylene, and polydimethylsiloxane with interlocked and outer micropyramid arrays. The sensor shows high pressure sensitivity (-3.26 kPa-1 in the pressure range of 0-300 Pa), and it can detect the shear force changes induced by the dynamic interaction between the outer micropyramid structure on the sensor and the tested material surface, and the minimum dimension of the microstripe that can be discerned is as low as 15 µm × 15 µm (interval × width). To demonstrate the texture discrimination capability, the sensors are tested for accurately discerning various surface textures, such as the textures of different fabrics, Braille characters, the inverted pyramid patterns, which will have great potential in robot skins and haptic perception, etc.

Wearable Sweatband Sensor Platform Based on Gold Nanodendrite Array as Efficient Solid Contact of Ion-Selective Electrode.

As chemical sensors are in great demand for portable and wearable analytical applications, it is highly desirable to develop an all-solid-state ion-selective electrode (ISE) and reference electrode (RE) platform with simplicity and stability. Here we propose a wearable sensor platform with a new type of all-solid-state ISE based on a gold nanodendrite (AuND) array electrode as the solid contact and a poly(vinyl acetate)/inorganic salt (PVA/KCl) membrane-coated all-solid-state RE. A simple and controllable method was developed to fabricate the AuNDs on a microwell array patterned chip by one-step electrodeposition without additional processing. For the first time, the AuND electrodes with different real surface area and double layer capacitance were developed as solid contact of the Na+-ISE to investigate the relationship between performance of the ISE and surface area. As-prepared AuND-ISE with larger surface area (∼7.23 cm2) exhibited enhanced potential stability compared to those with smaller surface area (∼1.85 cm2) and to bare Au ISE. Important as the ISE, the PVA/KCl membrane-coated Ag/AgCl RE exhibited highly stable potential even after 3 months' storage. Finally, a wearable sweatband sensor platform was developed for efficient sweat collection and real-time analysis of sweat sodium during indoor exercise. This all-solid-state ISE and RE integrated sensor platform provided a very simple and reliable way to construct diverse portable and wearable devices for healthcare, sports, clinical diagnosis, and environmental analysis applications.

Gross Hematuria Is More Common in Male and Older Patients with Renal Tuberculosis in China: A Single-Center 15-Year Clinical Experience.

OBJECTIVES: This study aimed to investigate the clinical features of renal tuberculosis and identify the age- and gender-related differences. METHODS: A total of 419 patients at the Peking University First Hospital from January 2000 to July 2015 were retrospectively reviewed. Data on demographic characteristics, clinical presentation, complications, laboratory results, radiologic imaging, surgical procedures, and pathology features were collected and compared between genders and 3 different age groups (under 40 years, 41-60, years and over 60 years). RESULTS: The most common local presentations were lower urinary tract symptoms (65.2%), flank pain (37.9%), and gross hematuria (26.3%). Constitutional symptoms were also observed in 38.9% of the patients. Gross hematuria was more common in male patients (32.2%) and older patients (45.5%). Flank pain was more common in female patients (43.6%). Patients younger than 40 years of age had lower frequencies of calcification of the urinary tract (22.2%) and kidney atrophy (4.2%) in CT. In the postoperative pathological reports, atrophy (35.9%) and fibrosis (38.5%) were found to be significantly more common in older patients. CONCLUSIONS: While gross hematuria is more prevalent in older patients and male patients, flank pain is more common in female patients. Radiological and pathological features including calcification of the urinary tract, fibrosis, and kidney atrophy are more common in older patients.

Quantum Computation using Arrays of N Polar Molecules in Pendular States.

We investigate several aspects of realizing quantum computation using entangled polar molecules in pendular states. Quantum algorithms typically start from a product state |00⋯0⟩ and we show that up to a negligible error, the ground states of polar molecule arrays can be considered as the unentangled qubit basis state |00⋯0⟩ . This state can be prepared by simply allowing the system to reach thermal equilibrium at low temperature (<1 mK). We also evaluate entanglement, characterized by concurrence of pendular state qubits in dipole arrays as governed by the external electric field, dipole-dipole coupling and number N of molecules in the array. In the parameter regime that we consider for quantum computing, we find that qubit entanglement is modest, typically no greater than 10-4 , confirming the negligible entanglement in the ground state. We discuss methods for realizing quantum computation in the gate model, measurement-based model, instantaneous quantum polynomial time circuits and the adiabatic model using polar molecules in pendular states.

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.

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.

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.

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.

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.

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.