Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study.

BACKGROUND: A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Academy of Medicine to transform health systems into learning health systems (LHS) holds promise for bringing necessary structural changes to health care, thereby addressing the exclusivity and adoption issues of LC screening. OBJECTIVE: This study aims to realize the LHS vision by designing an equitable, machine learning (ML)-enabled LHS unit for LC screening. It focuses on developing an inclusive and practical LC risk prediction model, suitable for initializing the ML-enabled LHS (ML-LHS) unit. This model aims to empower primary physicians in a clinical research network, linking central hospitals and rural clinics, to routinely deliver risk-based screening for enhancing LC early detection in broader populations. METHODS: We created a standardized data set of health factors from 1397 patients with LC and 1448 control patients, all aged 30 years and older, including both smokers and nonsmokers, from a hospital's electronic medical record system. Initially, a data-centric ML approach was used to create inclusive ML models for risk prediction from all available health factors. Subsequently, a quantitative distribution of LC health factors was used in feature engineering to refine the models into a more practical model with fewer variables. RESULTS: The initial inclusive 250-variable XGBoost model for LC risk prediction achieved performance metrics of 0.86 recall, 0.90 precision, and 0.89 accuracy. Post feature refinement, a practical 29-variable XGBoost model was developed, displaying performance metrics of 0.80 recall, 0.82 precision, and 0.82 accuracy. This model met the criteria for initializing the ML-LHS unit for risk-based, inclusive LC screening within clinical research networks. CONCLUSIONS: This study designed an innovative ML-LHS unit for a clinical research network, aiming to sustainably provide inclusive LC screening to all at-risk populations. It developed an inclusive and practical XGBoost model from hospital electronic medical record data, capable of initializing such an ML-LHS unit for community and rural clinics. The anticipated deployment of this ML-LHS unit is expected to significantly improve LC-screening rates and early detection among broader populations, including those typically overlooked by existing screening guidelines.

Model-based prototype design, establishment and operation of ventilation system for underground gymnasium.

Underground small indoor gymnasiums (USIG) are important public places, it is vital to design and build a very economical and efficient ventilation system for effective closed-loop regulation of temperature and gases concentration at prescribed levels. In the article, the model-based prototype design, establishment and operation were proposed and applied to closed-loop control system of the underground small indoor gymnasiums' ventilation system (USIGVS). First of all, the extended Multiphysics model was developed through feedback connecting the 3D Multiphysics model of air flow rate, temperature, O2 and CO2 concentration with a 0D proportional-integral-derivative (PID) controller via Neumann boundary condition, hence a close-loop USIGVS was constructed for feedback control of temperature and gases concentration in ping-pong USIG. Simultaneously, a cost function sufficiently representing the design requirement was formulated. Then global parameter sensitivity analysis (GPSA) was applied for sensitivity ranking of parameters including geometric parameters of USIGVS and tunable parameters of PID controller. The GPSA proved that sensitivity ordering of the cost function to each parameter was proportional gain (k p ) > derivative gain (k d ) > distance from left inlet to bottom (r) > distance from outlet pipe to bottom (d) > integrative gain (k i ) > distance from upper inlet pipe to left (h), respectively, and the k p , k d and r was the parameter influencing the cost function the most. The optimal parameters determined by both GPSA and response optimization were k p  = 3.17 m4 mol-1 s-1, k d  = 1.49 m4 mol-1, r = 2.04 m, d = 3.12 m, k i  = 0.37 m4 mol-1 s-2 and h = 3.85 m. Finally, the closed-loop USIGVS prototype with optimal parameters was designed and established through real-time simulation. The real-time operation confirmed that the temperature and gases concentrations were robust maintained at prescribed levels with desired dynamic response characteristics and lower power consumption, and the expected requirements were achieved for the design, establishment and operation of closed-loop USIGVS control system prototype.

A new approach for multislice spatiotemporal encoding MRI in a portable low-field system.

PURPOSE: Spatiotemporal encoding (SPEN) MRI offers a unique alternative to address image distortion problems in echo planar acquisition-based techniques, at portable low-field systems that lack multiple receiver coils. However, existing 2-π multislice SPEN schemes fail to keep consistent SNRs and contrasts with different numbers of slice settings. This work proposes a new multislice SPEN scheme (SPENms) to achieve stable quality imaging in portable low-field MRI systems. METHODS: The proposed SPENms includes the insertion of one selective π pulse and one non-selective π pulse, closely arranged together, before the frequency-swept π pulse in the original 2D SPEN sequence. Theoretical simulations and experiments on phantoms and human brains were conducted to validate its SNR and contrast performances under different parameters compared to the existing 2-π multislice SPEN scheme. RESULTS: Both simulations and experiments demonstrate the consistent image quality of SPENms with different scanning parameters and targets, as well as good distortion resistance and scan efficiency. Robust diffusion weighted multislice SPEN images of diagnostic value were also highlighted. CONCLUSION: SPENms provides a robust fast echo planar acquisition approach to obtain multislice 2D images with less distortions, consistent SNRs and contrasts at portable low-field MRI systems.

A Novel Adversarial Example Detection Method Based on Frequency Domain Reconstruction for Image Sensors.

Convolutional neural networks (CNNs) have been extensively used in numerous remote sensing image detection tasks owing to their exceptional performance. Nevertheless, CNNs are often vulnerable to adversarial examples, limiting the uses in different safety-critical scenarios. Recently, how to efficiently detect adversarial examples and improve the robustness of CNNs has drawn considerable focus. The existing adversarial example detection methods require modifying CNNs, which not only affects the model performance but also greatly enhances training cost. With the purpose of solving these problems, this study proposes a detection algorithm for adversarial examples that does not need modification of the CNN models and can simultaneously retain the classification accuracy of normal examples. Specifically, we design a method to detect adversarial examples using frequency domain reconstruction. After converting the input adversarial examples into the frequency domain by Fourier transform, the adversarial disturbance from adversarial attacks can be eliminated by modifying the frequency of the example. The inverse Fourier transform is then used to maximize the recovery of the original example. Firstly, we train a CNN to reconstruct input examples. Then, we insert Fourier transform, convolution operation, and inverse Fourier transform into the features of the input examples to automatically filter out adversarial frequencies. We refer to our proposed method as FDR (frequency domain reconstruction), which removes adversarial interference by converting input samples into frequency and reconstructing them back into the spatial domain to restore the image. In addition, we also introduce gradient masking into the proposed FDR method to enhance the detection accuracy of the model for complex adversarial examples. We conduct extensive experiments on five mainstream adversarial attacks on three benchmark datasets, and the experimental results show that FDR can outperform state-of-the-art solutions in detecting adversarial examples. Additionally, FDR does not require any modifications to the detector and can be integrated with other adversarial example detection methods to be installed in sensing devices to ensure detection safety.

O-GlcNAcylation regulation of RIPK1-dependent apoptosis dictates sensitivity to sunitinib in renal cell carcinoma.

Receptor interacting protein kinase 1 (RIPK1) has emerged as a key regulatory molecule that influences the balance between cell death and cell survival. Under external stress, RIPK1 determines whether a cell undergoes RIPK-dependent apoptosis (RDA) or survives by activating NF-κB signaling. However, the role and mechanisms of RIPK1 on sunitinib sensitivity in renal cell carcinoma (RCC) remain elusive. In this study, we demonstrated that the O-linked ß-N-acetylglucosamine modification (O-GlcNAcylation) of RIPK1 induces sunitinib resistance in RCC by inhibiting RDA. O-GlcNAc transferase (OGT) specifically interacts with RIPK1 through its tetratricopeptide repeats (TPR) domain and facilitates RIPK1 O-GlcNAcylation. The O-GlcNAcylation of RIPK1 at Ser331, Ser440 and Ser669 regulates RIPK1 ubiquitination and the formation of the RIPK1/FADD/Caspase-8 complex, thereby inhibiting sunitinib-induced RDA in RCC. Site-specific depletion of O-GlcNAcylation on RIPK1 affects the formation of the RIPK1/FADD/Caspase 8 complex, leading to increased sunitinib sensitivity in RCC. Our data highlight the significance of aberrant RIPK1 O-GlcNAcylation in the development of sunitinib resistance and indicate that targeting RIPK1 O-GlcNAcylation could be a promising therapeutic strategy for RCC.

Atomic-engineered gradient tunable solid-state metamaterials.

Metamaterial has been captivated a popular notion, offering photonic functionalities beyond the capabilities of natural materials. Its desirable functionality primarily relies on well-controlled conditions such as structural resonance, dispersion, geometry, filling fraction, external actuation, etc. However, its fundamental building blocks-meta-atoms-still rely on naturally occurring substances. Here, we propose and validate the concept of gradient and reversible atomic-engineered metamaterials (GRAM), which represents a platform for continuously tunable solid metaphotonics by atomic manipulation. GRAM consists of an atomic heterogenous interface of amorphous host and noble metals at the bottom, and the top interface was designed to facilitate the reversible movement of foreign atoms. Continuous and reversible changes in GRAM's refractive index and atomic structures are observed in the presence of a thermal field. We achieve multiple optical states of GRAM at varying temperature and time and demonstrate GRAM-based tunable nanophotonic devices in the visible spectrum. Further, high-efficiency and programmable laser raster-scanning patterns can be locally controlled by adjusting power and speed, without any mask-assisted or complex nanofabrication. Our approach casts a distinct, multilevel, and reversible postfabrication recipe to modify a solid material's properties at the atomic scale, opening avenues for optical materials engineering, information storage, display, and encryption, as well as advanced thermal optics and photonics.

Reproducible, Accurate, and Sensitive Food Toxin On-Site Detection with Carbon Nanotube Transistor Biosensors.

Field-effect transistor (FET) biosensors based on nanomaterials are promising in the areas of food safety and early disease diagnosis due to their ultrahigh sensitivity and rapid response. However, most academically developed FET biosensors lack real-world reproducibility and comprehensive methodological validation to meet the standards of regulatory bodies. Here, highly uniform and well-packaged semiconducting carbon nanotube (CNT) FET biosensor chips were developed and assessed for the plug-and-play sensing for the rapid and highly sensitive detection of aflatoxin B1 (AFB1) in real food samples to meet international standards. In order to meet the requirements for reproducibility and stability, a scalable residual-free passivation and packaging process was developed for CNT FET biosensors. Portable detection systems were then constructed for on-site detection. The resulting packaged chips were functionalized with nucleic aptamers to enable highly selective detection of AFB1 in food samples with a detection limit (LOD) of 0.55 fg/mL (standard) for AFB1 and cross-reactivity coefficients to interferences as low as 1.8 × 10-7 in simulated solutions. Utilizing the portable detection system, on-site real food detection was achieved with a rapid response time less than 60 s, and LOD of 0.25 pg/kg (standard) in complex corn sample matrices. Single-blind tests demonstrated the ability of the chips to detect AFB1-positive food with 100% accuracy, using a set of 30 peanut samples. Validation experiments confirmed that the detection range, stability, and repeatability met international standards. This study showcased the accuracy, reliability, and potential practical applications of CNT FET biosensor chips in areas such as food safety and rapid biomedical testing.

A novel nomogram for predicting the recurrence of atrial fibrillation in patients treated with first-time radiofrequency catheter ablation for atrial fibrillation.

Introduction: The purpose of this study was to investigate the predictive factors of atrial fibrillation (AF) recurrence in patients after first-time radiofrequency catheter ablation (RFCA) and to develop a nomogram predictive model that can provide valuable information for determining the ablation strategy. Methods: In total, 500 patients who had received first-time RFCA for AF were retrospectively enrolled in the study. The patients were divided into a training cohort (n = 300) and a validation cohort (n = 200) randomly at a 6:4 ratio. Lasso and multivariate logistic regression analyses were used to screen the predictors for AF recurrence during a 2-year follow-up. The C-index and a calibration plot were used to detect the discriminative ability and calibration of the nomogram. The performance of the nomogram was assessed compared with the APPLE score, CAAP-AF score, and MB-LATER score using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), integrated discrimination index (IDI), and net reclassification index (NRI). Results: A total of 78 patients experienced the recurrence of AF after first-time RFCA in the training cohort. The six strongest predictors for AF recurrence in the training cohort were persistent AF, duration of AF, left atrial diameter (LAD), estimated glomerular filtration rate (eGFR), N-terminal pro-brain natriuretic peptide (NT-proBNP), and autoantibody against M2-muscarinic receptor (anti-M2-R). Based on the above six variables, a nomogram prediction model was constructed with a C-index of 0.862 (95% CI, 0.815-0.909), while the C-index was 0.831 (95% CI, 0.771-0.890) in the validation cohort. DCA showed that this nomogram had greater net benefits compared with other models. Furthermore, the nomogram showed a noticeable improvement in predictive performance, sensitivity, and reclassification for AF recurrence compared with the APPLE score, CAAP-AF score, or MB-LATER score. Conclusion: We established a novel predictive tool for AF recurrence after the first-time RFCA during a 2-year follow-up period that could accurately predict individual AF recurrence.

Contrasting demographic history and mutational load in three threatened whitebark pines (Pinus subsect. Gerardianae): implications for conservation.

Demographic history and mutational load are of paramount importance for the adaptation of the endangered species. However, the effects of population evolutionary history and genetic load on the adaptive potential in endangered conifers remain unclear. Here, using population transcriptome sequencing, whole chloroplast genomes and mitochondrial DNA markers, combined with niche analysis, we determined the demographic history and mutational load for three threatened whitebark pines having different endangered statuses, Pinus bungeana, P. gerardiana and P. squamata. Demographic inference indicated that severe bottlenecks occurred in all three pines at different times, coinciding with periods of major climate and geological changes; in contrast, while P. bungeana experienced a recent population expansion, P. gerardiana and P. squamata maintained small population sizes after bottlenecking. Abundant homozygous-derived variants accumulated in the three pines, particularly in P. squamata, while the species with most heterozygous variants was P. gerardiana. Abundant moderately and few highly deleterious variants accumulated in the pine species that have experienced the most severe demographic bottlenecks (P. gerardiana and P. squamata), most likely because of purging effects. Finally, niche modeling showed that the distribution of P. bungeana might experience a significant expansion in the future, and the species' identified genetic clusters are also supported by differences in the ecological niche. The integration of genomic, demographic and niche data has allowed us to prove that the three threatened pines have contrasting patterns of demographic history and mutational load, which may have important implications in their adaptive potential and thus are also key for informing conservation planning.

iJAZ: the next breakthrough for engineering pest-resistance in plants?

Although transgenic Bacillus thuringiensis (Bt) crops have brought various ecological and socioeconomic benefits, there is evidence suggesting that pests will eventually develop resistance to Bt crops. Thus, additional genes are urgently needed to engineer pest resistance in plants. A recent study by Mo et al. indicates that iJAZ maybe the next breakthrough for engineering pest resistance in plants.

Genetic diversity and population structure of an insect-pollinated and bird-dispersed dioecious tree Magnolia kwangsiensis in a fragmented karst forest landscape.

This study combined population genetics and parentage analysis to obtain foundational data for the conservation of Magnolia kwangsiensis. M. kwangsiensis is a Class I tree species that occurs in two disjunct regions in a biodiversity hotspot in southwest China. We assessed the genetic diversity and structure of this species across its distribution range to support its conservation management. Genetic diversity and population structure of 529 individuals sampled from 14 populations were investigated using seven nuclear simple sequence repeat (nSSR) markers and three chloroplast DNA (cpDNA) fragments. Parentage analysis was used to evaluate the pollen and seed dispersal distances. The nSSR marker analysis revealed a high genetic diversity in M. kwangsiensis, with an average observed (Ho) and expected heterozygosities (He) of 0.726 and 0.687, respectively. The mean and maximum pollen and seed dispersal distances were 66.4 and 95.7 m and 535.4 and 553.8 m, respectively. Our data revealed two distinct genetic groups, consistent with the disjunct geographical distribution of the M. kwangsiensis populations. Both pollen and seed dispersal movements help maintain genetic connectivity among M. kwangsiensis populations, contributing to high levels of genetic diversity. Both genetically differentiated groups corresponding to the two disjunct regions should be recognized as separate conservation units.

A systematic review on aquaculture wastewater: Pollutants, impacts, and treatment technology.

Aquaculture is the major way to solve the global food sacrcity. As the global population increases, the demand for aquaculture increases. Fish feed, drugs and chemicals, and metabolic waste or mortalities of aquatic organisms also increase, eventually resulting in the production of a large amount of aquaculture wastewater. These aquaculture discharges contain a variety of pollutants, such as conventional pollutants, organic compounds, heavy metals, and biological contaminants, inducing occupational hazards and risks, food security, the environment pollution. Proper wastewater treatment technologies are required to remove hazardous pollutants for minimizing their impacts on environmental and human health. Recirculating aquaculture systems, some biological and physicochemical methods have been applied to remove some pollutants from the aquaculture wastewater, but their efficiency in removing pollutants still requires to be further improved for achieving zero-waste discharge and ensuring sustainable aquaculture development. Meanwhile, sound regulation and legislation needs to be established for ensuring the normal operation of aquaculture industries and the standard discharge of wastewater. This review aims to provide comprehensive information of aquaculture wastewater for the researchers and promote the healthy development of aquaculture.

Nonideality in Arrayed Carbon Nanotube Field Effect Transistors Revealed by High-Resolution Transmission Electron Microscopy.

High density and high semiconducting-purity single-walled carbon nanotube array (A-CNT) have recently been demonstrated as promising candidates for high-performance nanoelectronics. Knowledge of the structures and arrangement of CNTs within the arrays and their interfaces to neighboring CNTs, metal contacts, and dielectrics, as the key components of an A-CNT field effect transistor (FET), is essential for device mechanistic understanding and further optimization, particularly considering that the current technologies for the fabrication of A-CNT wafers are mainly laboratory-level solution-based processes. Here, we conduct a systematic investigation into the microstructures of A-CNT FETs mainly via cross-sectional high-resolution transmission electron microscopy and tentatively establish a framework consisting of up to 11 parameters which can be used for structure-side quality evaluation of the A-CNT FETs. The parameter ensemble includes the diameter, length (or terminal), and density distribution of CNTs, radial deformation of CNTs, array alignment defects, surface crystallography facets of contact metal, thickness distribution of high-k dielectrics (HfO2), and the contact ratios for the CNT-CNT, CNT-metal, CNT-dielectric, and CNT-substrate interfaces. Enriched array alignment defects, i.e., bundle, stacking, misorientation, and voids, are observed with a total ratio sometimes up to ∼90% in pristine A-CNTs and even up to ∼95% after the device fabrication process. Thus, they are suggested as the prevalent performance-limiting factors for A-CNT FETs. Complex interfacial structures are observed at the CNT-CNT, CNT-metal contact, and CNT-high-k dielectric interfaces, making the local environment and the property of each component CNT involved in an A-CNT FET distinct from others in terms of the diameters, radial deformation, and interactions with the local surroundings (mainly through van der Waals interactions). The present study suggests further improvements on the fabrication technology of A-CNT wafers and devices and mechanistic investigations into the impacts of complex array alignment defects and interface structures on the electrical performance of A-CNT FETs as well.

IL-1ß mediates Candida tropicalis-induced immunosuppressive function of MDSCs to foster colorectal cancer.

BACKGROUND: There is increasing evidence that gut fungi dysbiosis plays a crucial role in the development and progression of colorectal cancer (CRC). It has been reported that gut fungi exacerbate the severity of CRC by regulating tumor immunity. Our previous studies have shown that the opportunistic pathogenic fungal pathogen, Candida tropicalis (C. tropicalis) promotes CRC progression by enhancing the immunosuppressive function of MDSCs and activating the NLRP3 inflammasome of MDSCs. However, the relationship between IL-1ß produced by NLRP3 inflammasome activation and the immunosuppressive function of MDSCs enhanced by C. tropicalis in CRC remains unclear. METHODS: The TCGA database was used to analyze the relationship between IL-1ß and genes related to immunosuppressive function of MDSCs in human CRC. The expression of IL-1ß in human CRC tissues was detected by immunofluorescence staining. The proteomic analysis was performed on the culture supernatant of C. tropicalis-stimulated MDSCs. The experiments of supplementing and blocking IL-1ß as well as inhibiting the NLRP3 inflammasome activation were conducted. A mouse colon cancer xenograft model was established by using MC38 colon cancer cell line. RESULTS: Analysis of CRC clinical samples showed that the high expression of IL-1ß was closely related to the immunosuppressive function of tumor-infiltrated MDSCs. The results of in vitro experiments revealed that IL-1ß was the most secreted cytokine of MDSCs stimulated by C. tropicalis. In vitro supplementation of IL-1ß further enhanced the immunosuppressive function of C. tropicalis-stimulated MDSCs and NLRP3-IL-1ß axis mediated the immunosuppressive function of MDSCs enhanced by C. tropicalis. Finally, blockade of IL-1ß secreted by MDSCs augmented antitumor immunity and mitigated C. tropicalis-associated colon cancer. CONCLUSIONS: C. tropicalis promotes excessive secretion of IL-1ß from MDSCs via the NLRP3 inflammasome. IL-1ß further enhances the immunosuppressive function of MDSCs to inhibit antitumor immunity, thus promoting the progression of CRC. Therefore, targeting IL-1ß secreted by MDSCs may be a potential immunotherapeutic strategy for the treatment of CRC.

Protein Corona-Directed Cellular Recognition and Uptake of Polyethylene Nanoplastics by Macrophages.

The widespread use of plastic products in daily life has raised concerns about the health hazards associated with nanoplastics (NPs). When exposed, NPs are likely to infiltrate the bloodstream, interact with plasma proteins, and trigger macrophage recognition and clearance. In this study, we focused on establishing a correlation between the unique protein coronal signatures of high-density (HDPE) and low-density (LDPE) polyethylene (PE) NPs with their ultimate impact on macrophage recognition and cytotoxicity. We observed that low-density and high-density lipoprotein receptors (LDLR and SR-B1), facilitated by apolipoproteins, played an essential role in PE-NP recognition. Consequently, PE-NPs activated the caspase-3/GSDME pathway and ultimately led to pyroptosis. Advanced imaging techniques, including label-free scattered light confocal imaging and cryo-soft X-ray transmission microscopy with 3D-tomographic reconstruction (nano-CT), provided powerful insights into visualizing NPs-cell interactions. These findings underscore the potential risks of NPs to macrophages and introduce analytical methods for studying the behavior of NPs in biological systems.

Clinical and genetic analysis of macrophage activation syndrome complicating juvenile idiopathic inflammatory myopathies.

BACKGROUND: Macrophage activation syndrome (MAS) is a serve complication of juvenile idiopathic inflammatory myopathies (JIIMs). This study delineates the clinical manifestations and genetic underpinnings of JIIM-MAS patients. METHODS: We retrospectively analysed clinical and UNC13D gene from JIIM patients admitted to our centre between 2011 and 2021 to identify cases of MAS. Additionally, a literature review summarising reported cases of JIIMs and MAS was performed. RESULTS: Of 773 JIIM patients, 10 (1.3%) were diagnosed with MAS. All patients presented with persistent fever and hyperferritinaemia. Seventy percent of patients met the HLH-2004 criteria, while 90% met the 2016 sJIA-MAS criteria. Most patients received combined treatment of corticosteroids and immunosuppressants. UNC13D gene analysis was performed in six patients. A homozygous pathogenic mutation (c.2588G>A) was detected in one patient with recurrent MAS, and twenty-eight single-nucleotide polymorphisms (SNPs) were detected. Eighty percent of patients exhibiting a consistent combination of ten SNPs compared to JIIM patients without MAS (35%). CONCLUSION: MAS is an early and often overlooked complication of JIIMs. The 2016 sJIA-MAS criteria may facilitate early diagnosis. Combined corticosteroid and immunosuppressant therapy prove effective. An increased prevalence of UNC13D gene polymorphisms was observed in JIIM-MAS patients, highlighting the necessity for further investigations. IMPACT: This study aimed to delineate the clinical manifestations and genetic underpinnings of macrophage activation syndrome (MAS) in ten patients with juvenile idiopathic inflammatory myopathies (JIIMs). MAS has been recognised as a complication of JIIMs. However, only a few case reports provide comprehensive descriptions of MAS in JIIM patients, and there are few reports related to UNC13D mutations in these patients. This article offers single-centre clinical insights to enhance the identification and management of MAS in JIIM patients, while also highlighting the potential association between MAS occurrence and UNC13D gene polymorphisms.

A novel hybrid adaptive differential evolution for global optimization.

Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants.

Clinical Therapy: HAIC Combined with Tyrosine Kinase Inhibitors and Programmed Cell Death Protein-1 Inhibitors versus HAIC Alone for Unresectable Hepatocellular Carcinoma.

Purpose: The majority of new diagnoses of hepatocellular carcinoma (HCC) still pertain to unresectable cases. Currently, the combination therapy of tyrosine kinase inhibitors (TKIs) and programmed cell death protein-1 (PD-1) inhibitors has become the mainstream treatment. According to multiple clinical guidelines, it is strongly advised to consider local therapy as the primary treatment choice for uHCC. This research was conducted to examine the safety and effectiveness of combining hepatic arterial infusion chemotherapy (HAIC) with TKIs and PD-1 inhibitors for the treatment of uHCC. Methods: Between 2015 and 2020, 208 HCC patients received HAIC alone or HAIC in combination with TKIs and PD-1 inhibitors. The overall survival(OS), and progression-free survival(PFS) and the best treatment response were compared between the two treatment groups. Propensity score matching (PSM)was used to minimize confounding bias. Results: Among the enrolled patients, 116 patients (55.8%) received combination therapy, while 92 patients (44.2%) received HAIC alone. The baseline characteristics were similar between the two groups. After PSM, 82 pairs of well-matched liver cancer patients were selected; the overall response rate in the combination group trended better than that in the HAIC alone group. The hazard ratios (HRs) for OS and PFS of the combination approach compared to the HAIC-alone approach were 0.47 (95% CI, 0.322-0.687; p<0.001) and 0.58 (95% CI, 0.397-0.848; p=0.005), respectively. Conclusion: For uHCC patients, combination therapy can provide better OS and PFS compared to HAIC alone.

Eco-friendly nanotechnology in rheumatoid arthritis: ANFIS-XGBoost enhanced layered nanomaterials.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by inflammation and pain in the joints, which can lead to joint damage and disability over time. Nanotechnology in RA treatment involves using nano-scale materials to improve drug delivery efficiency, specifically targeting inflamed tissues and minimizing side effects. The study aims to develop and optimize a new class of eco-friendly and highly effective layered nanomaterials for targeted drug delivery in the treatment of RA. The study's primary objective is to develop and optimize a new class of layered nanomaterials that are both eco-friendly and highly effective in the targeted delivery of medications for treating RA. Also, by employing a combination of Adaptive Neuron-Fuzzy Inference System (ANFIS) and Extreme Gradient Boosting (XGBoost) machine learning models, the study aims to precisely control nanomaterials synthesis, structural characteristics, and release mechanisms, ensuring delivery of anti-inflammatory drugs directly to the affected joints with minimal side effects. The in vitro evaluations demonstrated a sustained and controlled drug release, with an Encapsulation Efficiency (EE) of 85% and a Loading Capacity (LC) of 10%. In vivo studies in a murine arthritis model showed a 60% reduction in inflammation markers and a 50% improvement in mobility, with no significant toxicity observed in major organs. The machine learning models exhibited high predictive accuracy with a Root Mean Square Error (RMSE) of 0.667, a correlation coefficient (r) of 0.867, and an R2 value of 0.934. The nanomaterials also demonstrated a specificity rate of 87.443%, effectively targeting inflamed tissues with minimal off-target effects. These findings highlight the potential of this novel approach to significantly enhance RA treatment by improving drug delivery precision and minimizing systemic side effects.