Taking advantage of the interaction between the sulfoxide and heme cofactor to develop indoleamine 2, 3-dioxygenase 1 inhibitors.

Taking advantage of key interactions between sulfoxide and heme cofactor, we used the sulfoxide as the anchor functional group to develop two series of indoleamine 2, 3-dioxygenase 1 (IDO1) inhibitors: 2-benzylsulfinylbenzoxazoles (series 1) and 2-phenylsulfinylbenzoxazoles (series 2). In vitro enzymatic screening shows that both series can inhibit the activity of IDO1 in low micromolar (series 1) or nanomolar (series 2) levels. They also show inhibitory selectivity between IDO1 and tryptophan 2, 3-dioxygenase 2. Interestingly, although series 1 is less potent IDO1 inhibitors of these two series, it exhibited stronger inhibitory activity toward kynurenine production in interferon-γ stimulated BxPC-3 cells. Enzyme kinetics and binding studies demonstrated that 2-sulfinylbenzoxazoles are non-competitive inhibitors of tryptophan, and they interact with the ferrous form of heme. These results demonstrated 2-sulfinylbenzoxazoles as type II IDO1 inhibitors. Furthermore, molecular docking studies supports the sulfoxide being of the key functional group that interacts with the heme cofactor. Compound 22 (series 1) can inhibit NO production in a concentration dependent manner in lipopolysaccharides (LPS) stimulated RAW264.7 cells, and can relieve pulmonary edema and lung injury in LPS induced mouse acute lung injury models.

Generation of allogeneic CAR-NKT cells from hematopoietic stem and progenitor cells using a clinically guided culture method.

Cancer immunotherapy with autologous chimeric antigen receptor (CAR) T cells faces challenges in manufacturing and patient selection that could be avoided by using 'off-the-shelf' products, such as allogeneic CAR natural killer T (AlloCAR-NKT) cells. Previously, we reported a system for differentiating human hematopoietic stem and progenitor cells into AlloCAR-NKT cells, but the use of three-dimensional culture and xenogeneic feeders precluded its clinical application. Here we describe a clinically guided method to differentiate and expand IL-15-enhanced AlloCAR-NKT cells with high yield and purity. We generated AlloCAR-NKT cells targeting seven cancers and, in a multiple myeloma model, demonstrated their antitumor efficacy, expansion and persistence. The cells also selectively depleted immunosuppressive cells in the tumor microenviroment and antagonized tumor immune evasion via triple targeting of CAR, TCR and NK receptors. They exhibited a stable hypoimmunogenic phenotype associated with epigenetic and signaling regulation and did not induce detectable graft versus host disease or cytokine release syndrome. These properties of AlloCAR-NKT cells support their potential for clinical translation.

Associations Between Physical Activity and the Incidence of Cerebrovascular Disease or All-Cause Mortality Among 146,742 Older Adults: A 13-Year Prospective Cohort Study.

OBJECTIVES: Although studies have indicated that physical activity (PA) is related to cardiovascular disease, the specific association between PA and incident cerebrovascular disease (CBVD) remains uncertain. The current study aimed to investigate the associations between PA levels and the CBVD incidence or all-cause mortality. DESIGN: Prospective cohort study. SETTINGS AND PARTICIPANTS: Older participants (age >60 years) from the UK Biobank. METHODS: The baseline PA was classified as total, light, moderate, and vigorous PA based on the metabolic equivalent-minutes per week (MET-min/wk) and considered as exposures, whereas CBVD incidence and all-cause mortality were considered as the outcomes. Cox proportional hazards were used to calculate the hazard ratios (HRs) and 95% CIs for the influence of the association between PA and CBVD incidence and all-cause mortality. RESULTS: A total of 146,742 participants aged 60 years and older were included. During a median follow-up period of 13.5 years (interquartile range of 12.8-14.2), 9338 older individuals developed CBVD and 3033 death were recorded (including 767 CBVD-related deaths). High volumes of PA were consistently associated with lower risks of CBVD and all-cause mortality. The lowest risk of CBVD incidence was observed at 2001-2500 MET-min/wk of total PA (HR 0.61, 95% CI 0.53-0.70), and the lowest risk of all-cause mortality was observed at 2501-5000 MET-min/wk (HR 0.52, 95% CI 0.43-0.63) in older adults. Total PA at 2001-2500 MET-min/wk significantly reduced the CBVD incidence in older women (HR 0.57, 95% CI 0.46-0.71), which was more pronounced than that in older men (HR for 2001-2500 MET-min/wk: 0.64, 95% CI 0.50-0.77). CONCLUSIONS AND IMPLICATIONS: Total PA at 2001-2500 MET-min/wk significantly reduced the risk of incident CBVD and all-cause mortality in adults aged >60 years, although the extents of risk reduction vary in men and women.

Effects of Nanowire Doping on Plasmon-Enhanced N2 Dissociation.

Doping a transition metal element into plasmonic systems can tune the optical properties of the system, which will potentially facilitate the plasmon-enhanced catalytic process. In this study, we applied the linear-response time-dependent density functional theory (LR-TDDFT) method with real-time electron dynamics and mean-field Ehrenfest dynamics methods to computationally investigate the effects of doping silver nanowires on plasmon-enhanced N2 dissociation. We calculated the absorption spectra for different doped systems, applied an external electric field to the system, and performed mean-field Ehrenfest dynamics to examine how plasmonic excitation will affect the N2 activation or dissociation. In addition, we also studied how the transition metal dopant affects the system's electronic structure and potential energy surface.

The spatial spillover effects of clean energy consumption and production on sustainable economic development in China.

The massive consumption of fossil energy has resulted in high CO2 emissions, posing a formidable challenge to global sustainable economic development (SED). As countries endeavor to shift from fossil to clean energy sources to achieve SED, research on the impact of clean energy is scarce, and quantitative analysis is lacking. This study measured China's SED and used a spatial econometric model to examine the impact of clean energy consumption and production on SED across 30 provinces in China from 2008 to 2020. Results show that (1) China's SED exhibits significant positive spatial autocorrelation characteristics, forming a "point-to-area" development pattern. (2) Clean energy consumption, production, and consumption structure all contribute to the promotion of SED in the region and have positive spatial spillover effects. (3) A considerable regional disparity exists in the spatial impact of clean energy on SED. The eastern and central regions have significant positive spatial spillover effects, whereas the western region is opposite. Notably, the estimated coefficient of the spatial Durbin model is relatively small, reflecting China's ongoing transition to clean energy and its limited role in promoting economic sustainability. Joint efforts and differentiated policies are essential to develop clean energy and sustainable economic.

Analysis on status quo and related factors of delays in diagnosis and treatment of breast cancer in Ningxia Hui Autonomous Region.

This study aimed to explore factors contributing to the delays in the diagnosis and treatment of breast cancer (BC) in Ningxia Hui Autonomous Region. We conducted a cohort analysis of 1012 patients with BC diagnosed at the General Hospital of Ningxia Medical University between January 2018 and December 2019. Sociodemographic data were collected through questionnaires, and clinical data were gathered and analyzed from relevant databases. Furthermore, observations were made regarding delays in the diagnosis and treatment of BC, followed by an analysis of the correlations between patient delay and both sociological factors within the population and clinical factors specific to patients with BC. Subsequently, the factors associated with patient delay and system delay were examined using Cox regression analysis, along with the inclusion of neoadjuvant therapy. In the prevention and treatment of BC in Ningxia, the patient delay rate was 33.20%, the diagnosis delay rate was 17.89%, the treatment delay rate was 0.0099% and the system delay rate was 41.60%. There was a higher proportion of patient delay and system delay in aged patients (age ≥ 61 years) with rural registered permanent residence (RPR), multiple clinical symptoms (such as nipple spillage, axillary abnormalities, etc), a T4 tumor size classification, and the initial use of neoadjuvant therapy. Besides, significant positive correlations were observed between patient delay and system delay time with BC stage. Patients aged 51 to 60 and those with molecular types (Limanal1B: ki-67 > 14%, Limanal1B: HER-2 positive) were prone to patient delay, whereas molecular characteristics influenced system delay, unrelated to sociodemographic factors. The study identifies significant age, residency, and tumor molecular subtype correlations with diagnostic and treatment delays in Ningxia's patients with BC, predominantly affecting women aged 41 to 60, especially urban dwellers. These findings underscore the need for targeted interventions to reduce delays and improve BC care in this region.

Chelerythrine induces apoptosis and ferroptosis through Nrf2 in ovarian cancer cells.

Ovarian cancer is a prevalent malignancy in the female reproductive system, representing a significantly fatal and incurable tumor. Chelerythrine (CHE), a natural benzopyridine alkaloid, has demonstrated a broad spectrum of anticancer activities. Nevertheless, the ovarian cancer inhibitory impact of CHE remains unclear. In this study, we investigated the cytotoxic mechanism and potential targets of CHE on in vitro cultures of A2780 and SKOV3 cells derived from ovarian cancer. Additionally, in vivo experiments were conducted to confirm the suppressive impact of CHE on tumor growth in nude mice. The findings revealed that CHE impeded the growth of A2780 and SKOV3 cells in a concentration-time-dependent manner and significantly suppressed the development of tumors in nude mice. CHE elevated the level of oxidative stress in tumor cells, prompted cell cycle halt in the S phase, and increased their mitochondrial membrane potential. Western blotting results demonstrated that CHE could modulate the expression of proteins associated with apoptotic and ferroptosis processes in A2780 and SKOV3 cells. Nrf2 was verified to be an upstream key target mediating the inhibitory impact of CHE on ovarian cancer cells. In summary, CHE exerts its anti-cancer effects on ovarian cancer by modulating Nrf2, inhibiting cellular proliferation, and promoting apoptosis and ferroptosis.

Detection of lung cancer through SERS analysis of serum.

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Omics-based approaches for discovering active ingredients and regulating gut microbiota of Actinidia arguta exosome-like nanoparticles.

Exosome-like nanoparticles (ELNs) are novel naturally occurring plant ultrastructures and contain unique bioactive components. However, the potential applications and biological functions of plant ELNs, especially in the context of health promotion and disease prevention, remain largely unexplored. This study aimed to explore the biological activities and functional mechanisms of Actinidia arguta-derived exosome-like nanoparticles (AAELNs). We reported the development of AAELNs, which possess particle sizes of 157.8 nm and a negative surface charge of -23.07 mV, uptaking by RAW264.7 cells, and reduction of oxidative stress by decreasing the activity of GSH-Px and T-SOD and increasing the content of MDA. Through the use of high-throughput sequencing technology, 12 known miRNA families and 23 additional miRNAs were identified in AAELNs, GO and KEGG term enrichment analysis revealed the potential of AAELNs-miRNAs in modulating neural-relevant behaviors. Additionally, LC-MS/MS analysis detected a total of 32 major lipid classes, 430 lipid subclasses, and 1345 proteins in AAELNs. Furthermore, in vivo fluorescence disappearance and in vitro fermentation experiments demonstrated that AAELNs were able to enter the colon and improve the microbial structure. These findings suggest that AAELNs could serve as nanoshuttles in food, potentially offering health-enhancing properties.

Emergency Department Trauma Activation Fees by Payer Type.

This cross-sectional study examines the wide variations in prices of emergency medical services at US hospitals.

A Novel Induction-Type Pressure Sensor based on Magneto-Stress Impedance and Magnetoelastic Coupling Effect for Monitoring Hand Rehabilitation.

Visualization of training effectiveness is critical to patients' confidence and eventual rehabilitation. Here, an innovative magnetoinductive pressure sensor is proposed for monitoring hand rehabilitation in stroke hemiplegic patients. It couples the giant magneto and stress-impedance effects of a square spiral amorphous wire with the giant magnetoelastic effect of a polymer magnet (NdFeB@PDMS). The addition of the magnetoelastic layer results in a sensitivity improvement of 178%, a wide sensing range (up to 1 MPa), fast response/recovery times (40 ms), and excellent mechanical robustness (over 15 000 cycles). Further integration with an LC oscillation circuit enables frequency adjustment into the MHz range resulting in a sensitivity of 6.6% kPa-1 and outstanding linearity (R2 =  0.99717) over a stress range of up to 100 kPa. When attached to a commercial split-fingerboard, the sensor is capable of dynamically monitoring the force in each finger, providing a reading of the rehabilitation process. Unlike conventional inductive sensors, the sensor is based on an inductive force-responsive material (amorphous wire), which significantly boosts the sensitivity. The approach also demonstrates the potential of magnetoelasticity in static pressure sensing, which is highly sensitive to dynamic pressure only through electromagnetic induction. This makes it more suitable for long-term and continuous human health monitoring.

Quantum simulation of conical intersections.

We explore the simulation of conical intersections (CIs) on quantum devices, setting the groundwork for potential applications in nonadiabatic quantum dynamics within molecular systems. The intersecting potential energy surfaces of H3+ are computed from a variance-based contracted quantum eigensolver. We show how the CIs can be correctly described on quantum devices using wavefunctions generated by the anti-Hermitian contracted Schrödinger equation ansatz, which is a unitary transformation of wavefunctions that preserves the topography of CIs. A hybrid quantum-classical procedure is used to locate the seam of CIs. Additionally, we discuss the quantum implementation of the adiabatic to diabatic transformation and its relation to the geometric phase effect. Results on noisy intermediate-scale quantum devices showcase the potential of quantum computers in dealing with problems in nonadiabatic chemistry.

Periampullary cancer and neurological interactions: current understanding and future research directions.

Periampullary cancer is a malignant tumor occurring around the ampullary region of the liver and pancreas, encompassing a variety of tissue types and sharing numerous biological characteristics, including interactions with the nervous system. The nervous system plays a crucial role in regulating organ development, maintaining physiological equilibrium, and ensuring life process plasticity, a role that is equally pivotal in oncology. Investigations into nerve-tumor interactions have unveiled their key part in controlling cancer progression, inhibiting anti-tumor immune responses, facilitating invasion and metastasis, and triggering neuropathic pain. Despite many mechanisms by which nerve fibers contribute to cancer advancement still being incompletely understood, the growing emphasis on the significance of nerves within the tumor microenvironment in recent years has set the stage for the development of groundbreaking therapies. This includes combining current neuroactive medications with established therapeutic protocols. This review centers on the mechanisms of Periampullary cancer's interactions with nerves, the influence of various types of nerve innervation on cancer evolution, and outlines the horizons for ongoing and forthcoming research.

Hazel Leaf Polyphenol Extract Alleviated Cisplatin-Induced Acute Kidney Injury by Reducing Ferroptosis through Inhibiting Hippo Signaling.

Derived from hazelnuts, hazel leaf has been utilized in traditional folk medicine for centuries in countries such as Portugal, Sweden, and Iran. In our previous investigations, we conducted a preliminary assessment of the hazel leaf polyphenol extract (referred to as ZP) and identified nine compounds, such as kaempferol and chlorogenic acid, in its composition. ZP has shown promising properties as an antioxidant and anti-inflammatory agent. Our research has revealed that ZP has protective effects against cisplatin-induced acute kidney injury (AKI). We conducted a comprehensive examination of both the pathological and ultrastructural aspects and found that ZP effectively ameliorated renal tissue lesions and mitigated mitochondrial damage. Moreover, ZP significantly suppressed malondialdehyde levels while increasing glutathione and catalase concentrations in the kidneys of AKI-induced mice. ZP decreased the number of apoptotic cells and decreased pro-apoptotic protein expression in the kidneys of mice and human renal tubular epithelial cells (HK-2). Furthermore, treatment with ZP increased the levels of proteins marking anti-ferroptosis, such as GPX4, FTH1, and FSP1, in experiments both in vivo and in vitro. We elucidated the underlying mechanisms of ZP's actions, revealing its inhibitory effect on Yap phosphorylation and its regulation of Lats expression, which exert a protective influence on the kidneys. Furthermore, we found that inhibiting the Hippo pathway compromised ZP's nephroprotective effects in both in vitro and in vivo studies. In summary, this research shows that ZP exhibits renoprotective properties, effectively reducing oxidative damage, apoptosis, and ferroptosis in the kidneys by targeting the Hippo pathway.

Construction of Functional Phenolic Resin and Carbon Hollow Spheres by Manipulating Structural Inhomogeneity.

Hollow carbonaceous spheres are extraordinarily attractive for their unique structural features and wide applications in various fields. Herein, a facile and effective synthesis methodology based on the extended Stöber process for construction of phenolic resin hollow spheres has been presented. Combined with a series of characterization techniques, the synthesis process was systematically investigated, and a possible synthesis mechanism was proposed. It is revealed that the structural inhomogeneity of the polymer product achieved by using dodecylamine and alkane is responsible for the formation of hollow architecture, which depends on spontaneous selective dissolution during the synthesis process. Different metal-doped carbonaceous hollow spheres can be obtained by introducing corresponding precursors into the synthetic system and meeting requirements of different application fields. This work presented a novel synthesis strategy of hollow carbonaceous spheres, which is significant for building a new platform of advanced functional carbon-based composites.

Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019.

With the issue of ozone (O3) pollution having increasingly gained visibility and prominence in China, the Chinese government explored various policies to mitigate O3 pollution. In some provinces and cities, diurnal regulations of O3 precursor were implemented, such as shifting O3 precursor emission processes to nighttime and offering preferential refueling at night. However, the effectiveness of these policies remains unverified, and their impact on the O3 generation process requires further elucidation. In this study, we utilized a regional climate and air quality model (WRF-Chem, v4.5) to test three scenarios aimed at exploring the impact of diurnal industry emission variation of O3 precursors on O3 formation. Significant O3 variations were observed mainly in urban areas. Shifting volatile organic compounds (VOCs) to nighttime have slight decreased daytime O3 levels while moving nitrogen oxides (NOx) to nighttime elevates O3 levels. Simultaneously moving both to nighttime showed combined effects. Process analysis indicates that the diurnal variation in O3 was mainly attributed to chemical process and vertical mixing in urban areas, while advection becomes more important in non-urban areas, contributing to the changes in O3 and O3 precursors levels through regional transportation. Further photochemical analysis reveals that the O3 photochemical production in urban areas was affected by reduced daytime O3 precursors emissions. Specifically, decreasing VOCs lowered the daytime O3 production by reducing the ROx radicals (ROx = HO + HO˙2 + RO˙2), whereas decreasing NOx promoted the daytime O3 production by weakening ROx radical loss. Our results demonstrate that diurnal regulation of O3 precursors will disrupt the ROx radical and O3 formation in local areas, resulting in a change in O3 concentration and atmospheric oxidation capacity, which should be considered in formulating new relevant policies.

Research on sustainable green building space design model integrating IoT technology.

"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT's impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed.

Temperature-Controlled Selective Formation of Silver Nanoclusters and Their Transformation to the Same Product.

Herein, two atomically precise silver nanoclusters, Ag54 and Ag33, directed by inner anion templates (CrO4 2- and/or Cl-), are initially isolated as a mixed phase from identical reactants across a wide temperature range (20-80 °C). Interestingly, fine-tuning the reaction temperature can realize pure phase synthesis of the two nanoclusters; that is, a metastable Ag54 is kinetically formed at a low temperature (20 °C), whereas such a system is steered towards a thermodynamically stable Ag33 at a relatively high temperature (80 °C). Electrospray ionization mass spectrometry illustrates that the stability of Ag33 is superior to that of Ag54, which is further supported by density functional theory calculations. Importantly, the difference in structural stability can influence the pathway of 1,4-bis(pyrid-4-yl)benzene induced transformation reaction starting from Ag54 and Ag33. The former undergoes a dramatic breakage-reorganization process to form an Ag31 dimer (Ag31), while the same product can be also achieved from the latter following a noninvasive ligand exchange process. Both the Ag54 and Ag33 have the potential for further remote laser ignition applications. This work not only demonstrates how temperature controls the isolation of a specific phase, but also sheds light on the structural transformation pathway of nanoclusters with different stability.

DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning.

Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://deepseq2drug.cs.cityu.edu.hk.

ResDeepSurv: A Survival Model for Deep Neural Networks Based on Residual Blocks and Self-attention Mechanism.

Survival analysis, as a widely used method for analyzing and predicting the timing of event occurrence, plays a crucial role in the medicine field. Medical professionals utilize survival models to gain insight into the effects of patient covariates on the disease, and the correlation with the effectiveness of different treatment strategies. This knowledge is essential for the development of treatment plans and the enhancement of treatment approaches. Conventional survival models, such as the Cox proportional hazards model, require a significant amount of feature engineering or prior knowledge to facilitate personalized modeling. To address these limitations, we propose a novel residual-based self-attention deep neural network for survival modeling, called ResDeepSurv, which combines the benefits of neural networks and the Cox proportional hazards regression model. The model proposed in our study simulates the distribution of survival time and the correlation between covariates and outcomes, but does not impose strict assumptions on the basic distribution of survival data. This approach effectively accounts for both linear and nonlinear risk functions in survival data analysis. The performance of our model in analyzing survival data with various risk functions is on par with or even superior to that of other existing survival analysis methods. Furthermore, we validate the superior performance of our model in comparison to currently existing methods by evaluating multiple publicly available clinical datasets. Through this study, we prove the effectiveness of our proposed model in survival analysis, providing a promising alternative to traditional approaches. The application of deep learning techniques and the ability to capture complex relationships between covariates and survival outcomes without relying on extensive feature engineering make our model a valuable tool for personalized medicine and decision-making in clinical practice.