Phytic Acid-Promoted Deposition of Gold Nanoparticles with Grafted Cationic Polymer Brushes for the Construction of Synergistic Contact-Killing and Photothermal Bactericidal Coatings.

Medical implants are constantly facing the risk of bacterial infections, especially infections caused by multidrug resistant bacteria. To mitigate this problem, gold nanoparticles with alkyl bromide moieties (Au NPs-Br) on the surfaces were prepared. Xenon light irradiation triggered the plasmon effect of Au NPs-Br to induce free radical graft polymerization of 2-(dimethylamino)ethyl methacrylate (DMAEMA), leading to the formation of poly(DMAEMA) brush-grafted Au NPs (Au NPs-g-PDM). The Au NPs-g-PDM nanocomposites were conjugated with phytic acid (PA) via electrostatic interaction and van der Waals interaction. The as-formed aggregates were deposited on the titanium (Ti) substrates to form the PA/Au NPs-g-PDM (PAP) hybrid coatings through surface adherence of PA and the gravitational effect. Synergistic bactericidal effects of contact-killing caused by the cationic PDM brushes, and local heating generated by the Au NPs under near-infrared irradiation, conferred strong antibacterial effects on the PAP-deposited Ti (Ti-PAP) substrates. The synergistic bactericidal effects reduced the threshold temperature required for the photothermal sterilization, which in turn minimized the secondary damage to the implant site. The Ti-PAP substrates exhibited 97.34% and 99.97% antibacterial and antiadhesive efficacy, respectively, against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), compared to the control under in vitro antimicrobial assays. Furthermore, the as-constructed Ti-PAP surface exhibited a 99.42% reduction in the inoculated S. aureus under in vivo assays. In addition, the PAP coatings exhibited good biocompatibility in the hemolysis and cytotoxicity assays as well as in the subcutaneous implantation of rats.

A Novel Hollow Graphene/Polydimethylsiloxane Composite for Pressure Sensors with High Sensitivity and Superhydrophobicity.

Flexible pressure sensors have attracted great interest as they play an important role in various fields such as health monitoring and human-machine interactions. The design of the pressure sensors still faces challenges in achieving a high sensitivity for a wide sensing range, and the interference of water restricts the applications of the sensors. Herein, we developed a graphene-polydimethylsiloxane film combining a hierarchical surface with nanowrinkles on it and a hollow structure. The microstructure design of the composite can be facilely controlled to improve the sensing and hydrophobic performance by tailoring the microsphere building units. Attributed to the irregular surface and hollow structure of the sensing layer, the optimized sensor exhibits a superior sensitivity of 1085 kPa-1 in a 50 kPa linear range. For practical applications, the nanowrinkles on the surface of the microspheres and the polymer coating endow the composite with waterproof properties. Inspired by the dual receptors of the skin, two designed microstructured films can simply integrate into one with double-sided microstructures. The sensing performance and the water-repellence property allow the sensor to detect physiological signals under both ambient and underwater conditions. Furthermore, underwater stimuli detection and communication are demonstrated. This method of fabricating a flexible sensor shows great potential in wearable and robotic fields.

Ultra-Strong Janus Covalent Organic Framework Membrane with Smart Response to Organic Vapor.

Vapor-driven smart Janus materials have made significant advancements in intelligent monitoring, control, and interaction, etc. Nevertheless, the development of ultrafast response single-layer Janus membrane, along with a deep exploration of the smart response mechanisms, remains a long-term endeavor. Here, the successful synthesis of a high-crystallinity single-layer Covalent organic framework (COF) Janus membrane is reported by morphology control. This kind of membrane displays superior mechanical properties and specific surface area, along with excellent responsiveness to CH2Cl2 vapor. The analysis of the underlying mechanisms reveals that the vapor-induced breathing effect of the COF and the stress mismatch of the Janus structure play a crucial role in its smart deformation performance. It is believed that this COF Janus membrane holds promise for complex tasks in various fields.

Dynamic Insights into the Growth Mechanisms of 2D Covalent Organic Frameworks on Graphene Surfaces.

Producing high-quality two-dimensional (2D) covalent organic frameworks (COFs) is crucial for industrial applications. However, this remains significantly challenging with current synthetic techniques. A deep understanding of the intermolecular interactions, reaction temperature, and oligomers is essential to facilitate the growth of highly crystalline COF films. Herein, molecular dynamics simulations were employed to explore the growth of 2D COFs from monomer assemblies on graphene. Our results showed that chain growth reactions dominated the COF surface growth and that van der Waals (vdW) interactions were important in enhancing the crystallinity through monomer preorganization. Moreover, appropriately tuning the reaction temperature improved the COF crystallinity and minimized the effects of amorphous oligomers. Additionally, the strength of the interface between the COF and the graphene substrate indicated that the adhesion force was proportional to the crystallinity of the COF. This work reveals the mechanisms for nucleation and growth of COFs on surfaces and provides theoretical guidance for fabricating high-quality 2D polymer-based crystalline nanomaterials.

Glucometabolic-Related Genes as Diagnostic Biomarkers and Therapeutic Targets for Alzheimer's Disease and Type 2 Diabetes Mellitus: A Bioinformatics Analysis.

Background: Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are two widespread chronic disorders characterized by shared risk factors and molecular pathways. Glucose metabolism, pivotal for cellular homeostasis and energy supply, plays a critical role in these diseases. Its disturbance has been linked to the pathogenesis of both AD and T2DM. However, a comprehensive investigation into the specific roles of glucometabolic genes in the onset and progression of AD and T2DM has yet to be conducted. Methods: By analyzing microarray datasets from the Gene Expression Omnibus (GEO) repository, we identified differentially expressed glucometabolic genes (DEGs) in AD and T2DM cohorts. A range of bioinformatics tools were employed for functional annotation, pathway enrichment, protein interaction network construction, module analysis, ROC curve assessment, correlation matrix construction, gene set enrichment analysis, and gene-drug interaction mapping of these DEGs. Key genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR) in AD and T2DM murine models. Results: Our investigation identified 41 glucometabolic-related DEGs, with six prominent genes (G6PD, PKM, ENO3, PFKL, PGD, and TALDO1) being common in both AD and T2DM cohorts. These genes play crucial roles in metabolic pathways including glycolysis, pentose phosphate pathway, and amino sugar metabolism. Their diagnostic potential was highlighted by area under curve (AUC) values exceeding 0.6 for AD and 0.8 for T2DM. Further analysis explored the interactions, pathway enrichments, regulatory mechanisms, and potential drug interactions of these key genes. In the AD murine model, quantitative real-time polymerase chain reaction (qRT-PCR) analysis revealed significant upregulation of G6pd, Eno3, and Taldo1. Similarly, in the T2DM murine model, elevated expression levels of G6pd, Pfkl, Eno3, and Pgd were observed. Conclusion: Our rigorous research sheds light on the molecular interconnections between AD and T2DM from a glucometabolic perspective, revealing new opportunities for pharmacological innovation and therapeutic approaches. This study appears to be the first to extensively investigate glucometabolic-associated DEGs and key genes in both AD and T2DM, utilizing multiple datasets. These insights are set to enhance our understanding of the complex pathophysiology underlying these widespread chronic diseases.

Tracing the sources and evaporation fate of surface water and groundwater using stable isotopes of hydrogen and oxygen.

Recognizing the origins and movement processes of surface water and groundwater is crucial for understanding hydrochemical genesis, conserving water supplies, and managing water resources. Estimating the source water typically involves identifying the intersection of evaporation line (EL) and meteoric water line. However, there is currently confusion in determining the regional EL and selecting strategies for estimating the source water. This study aimed to explore the source of surface water and groundwater, as well as evaporation effect utilizing stable isotope tracing (δ2H and δ18O). The line-conditioned excess was adopted to differentiate evaporated water and non-evaporated water, then Craig-Gordon model and an analytical framework with Bayesian theory were used to investigate the source of surface water and groundwater and the evaporation influence. The findings revealed that surface water and groundwater in the northern region of the Weihe River suffered more sever evaporation impacts that the south, and the evaporated surface water (7.54 % to 27.34 %) with a wider range of mean evaporation ratio than evaporated groundwater (5.38 % to 8.52 %). Monsoon precipitation is the main contributor to both surface water (contribution ratio: 0.46) and groundwater (0.49) sources. This research provides specific information on evaporation and detailed insights into the source water of surface water and groundwater, aiding in understanding the evaporation effect during the hydrological cycle and facilitating the management of regional water resources.

High-Strength Polycrystalline Covalent Organic Framework with Abnormal Thermal Transport Insensitive to Grain Boundary.

Grain boundaries (GBs) in two-dimensional (2D) covalent organic frameworks (COFs) unavoidably form during the fabrication process, playing pivotal roles in the physical characteristics of COFs. Herein, molecular dynamics simulations were employed to elucidate the fracture failure and thermal transport mechanisms of polycrystalline COFs (p-COFs). The results revealed that the tilt angle of GBs significantly influences out-of-plane wrinkles and residual stress in monolayer p-COFs. The tensile strength of p-COFs can be enhanced and weakened with the tilt angle, which exhibits an inverse relationship with the defect density. The crack always originates from weaker heptagon rings during uniaxial tension. Notably, the thermal transport in p-COFs is insensitive to the GBs due to the variation of minor polymer chain length at defects, which is abnormal for other 2D crystalline materials. This study contributes insights into the impact of GBs in p-COFs and offers theoretical guidance for structural design and practical applications of advanced COFs.

YTHDF1 promotes gallbladder cancer progression via post-transcriptional regulation of the m6A/UHRF1 axis.

Gallbladder cancer is a rare but fatal malignancy. However, the mechanisms underlying gallbladder carcinogenesis and its progression are poorly understood. The function of m6A modification and its regulators was still unclear for gallbladder cancer. The current study seeks to investigate the function of YTH m6A RNA-binding protein 1 (YTHDF1) in gallbladder cancer. Transcriptomic analysis and immunochemical staining of YTHDF1 in gallbladder cancer tissues revealed its upregulation compared to paracancerous tissues. Moreover, YTHDF1 promotes the proliferation assays, Transwell migration assays, and Transwell invasion assays of gallbladder cancer cells in vitro. And it also increased tumour growth in xenograft mouse model and metastases in tail vein injection model in vivo. In vitro, UHRF1 knockdown partly reversed the effects of YTHDF1 overexpression. Mechanistically, dual-luciferase assays proved that YTHDF1 promotes UHRF1 expression via direct binding to the mRNA 3'-UTR in a m6A-dependent manner. Overexpression of YTHDF1 enhanced UHRF1 mRNA stability, as demonstrated by mRNA stability assays, and Co-IP studies confirmed a direct interaction between YTHDF1 and PABPC1. Collectively, these findings provide new insights into the progression of gallbladder cancer as well as a novel post-transcriptional mechanism of YTHDF1 via stabilizing target mRNA.

Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter.

Objective: Voxel-based morphometry (VBM), surface-based morphometry (SBM), and radiomics are widely used in the field of neuroimage analysis, while it is still unclear that the performance comparison between traditional morphometry and emerging radiomics methods in diagnosing brain aging. In this study, we aimed to develop a VBM-SBM model and a radiomics model for brain aging based on cognitively normal (CN) individuals and compare their performance to explore both methods' strengths, weaknesses, and relationships. Methods: 967 CN participants were included in this study. Subjects were classified into the middle-aged group (n = 302) and the old-aged group (n = 665) according to the age of 66. The data of 360 subjects from the Alzheimer's Disease Neuroimaging Initiative were used for training and internal test of the VBM-SBM and radiomics models, and the data of 607 subjects from the Australian Imaging, Biomarker and Lifestyle, the National Alzheimer's Coordinating Center, and the Parkinson's Progression Markers Initiative databases were used for the external tests. Logistics regression participated in the construction of both models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the two model performances. The DeLong test was used to compare the differences in AUCs between models. The Spearman correlation analysis was used to observe the correlations between age, VBM-SBM parameters, and radiomics features. Results: The AUCs of the VBM-SBM model and radiomics model were 0.697 and 0.778 in the training set (p = 0.018), 0.640 and 0.789 in the internal test set (p = 0.007), 0.736 and 0.737 in the AIBL test set (p = 0.972), 0.746 and 0.838 in the NACC test set (p < 0.001), and 0.701 and 0.830 in the PPMI test set (p = 0.036). Weak correlations were observed between VBM-SBM parameters and radiomics features (p < 0.05). Conclusion: The radiomics model achieved better performance than the VBM-SBM model. Radiomics provides a good option for researchers who prioritize performance and generalization, whereas VBM-SBM is more suitable for those who emphasize interpretability and clinical practice.

Biobased Inks Based on Cuttlefish Ink and Cellulose Nanofibers for Biodegradable Patterned Soft Actuators.

Soft actuators with stimuli-responsive and reversible deformations have shown great promise in soft robotics. However, some challenges remain in existing actuators, such as the materials involved derived from nonrenewable resources, complex and nonscalable preparation methods, and incapability of complex and programmable deformation. Here, a biobased ink based on cuttlefish ink nanoparticles (CINPs) and cellulose nanofibers (CNFs) was developed, allowing for the preparation of biodegradable patterned actuators by direct ink writing technology. The hybrid CNF/CINP ink displays good rheological properties, allowing it to be accurately printed on a variety of flexible substrates. A bilayer actuator was developed by printing an ink layer on a biodegradable poly(lactic acid) film using extrusion-based 3D printing technology, which exhibits reversible and large bending behavior under the stimuli of humidity and light. Furthermore, programmable and reversible folding and coiling deformations in response to stimuli have been achieved by adjusting the ink patterns. This work offers a fast, scalable, and cost-effective strategy for the development of biodegradable patterned actuators with programmable shape-morphing.

Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms.

OBJECTIVE: To construct a multi-region MRI radiomics model for predicting pathological complete response (pCR) in breast cancer (BCa) patients who received neoadjuvant chemotherapy (NACT) and provide a theoretical basis for the peritumoral microenvironment affecting the efficacy of NACT. METHODS: A total of 133 BCa patients who received NACT, including 49 with confirmed pCR, were retrospectively analyzed. The radiomics features of the intratumoral region, peritumoral region, and background parenchymal enhancement (BPE) were extracted, and the most relevant features were obtained after dimensional reduction. Then, combining different areas, multivariate logistic regression analysis was used to select the optimal feature set, and six different machine learning models were used to predict pCR. The optimal model was selected, and its performance was evaluated using receiver operating characteristic (ROC) analysis. SHAP analysis was used to examine the relationship between the features of the model and pCR. RESULTS: For signatures constructed using three individual regions, BPE provided the best predictions of pCR, and the diagnostic performance of the intratumoral and peritumoral regions improved after adding the BPE signature. The radiomics signature from the combination of all the three regions with the XGBoost machine learning algorithm provided the best predictions of pCR based on AUC (training set: 0.891, validation set: 0.861), sensitivity (training set: 0.882, validation set: 0.800), and specificity (training set: 0.847, validation set: 0.84). SHAP analysis demonstrated that LZ_log.sigma.2.0.mm.3D_glcm_ClusterShade_T12 made the greatest contribution to the predictions of this model. CONCLUSION: The addition of the BPE MRI signature improved the prediction of pCR in BCa patients who received NACT. These results suggest that the features of the peritumoral microenvironment are related to the efficacy of NACT.

Application of AI on cholangiocarcinoma.

Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.

M6A RNA methylation in biliary tract cancer: the function roles and potential therapeutic implications.

Biliary tract cancers (BTCs) are relatively rare malignancies with a poor prognosis. For advanced BTCs, the efficacy of current chemotherapeutic approaches is limited. Consequently, there is an urgent need to deepen our understanding of the molecular mechanisms underlying BTC tumorigenesis and development for the exploration of effective targeted therapies. N6-methyladenosine (m6A), the most abundant RNA modifications in eukaryotes, is found usually dysregulated and involved in tumorigenesis, progression, and drug resistance in tumors. Numerous studies have confirmed that aberrant m6A regulators function as either oncogenes or tumor suppressors in BTCs by the reversible regulation of RNA metabolism, including splicing, export, degradation and translation. In this review, we summarized the current roles of the m6A regulators and their functional impacts on RNA fate in BTCs. The improved understanding of m6A modification in BTCs also provides a reasonable outlook for the exploration of new diagnostic strategies and efficient therapeutic targets.

Ultrastretchable Helical Carbon Nanotube-Woven Film.

Helical carbon nanotube (HCNT) is regarded as one of the most promising nanomaterials due to its excellent tensile strength and superhigh stretchability. Here, a novel HCNT-woven film (HWF) is proposed, and its in-plane and out-of-plane mechanical properties are systematically investigated via molecular dynamics (MD) simulation. The MD results show that HWF possesses highly stretchable capability resulting from sliding and straightening of CNT segments, and the maximum tensile strain can reach 2113%. Furthermore, the HWF presents an obvious tensile mechanical anisotropy. The torsion failure is the main fracture mode when the HWF is stretched along the longitudinal direction. However, when the HWF is stretched along the transverse direction, the fracture is mainly caused by intertube compression. On the other hand, the HWF can dissipate large amount of kinetic energy of projectile via sliding and fracture of HCNTs, leading to high specific penetration energy. This work provides a theoretical guidance for designing and fabricating next-generation superstrong two-dimensional CNT-based nanomaterials.

Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue.

BACKGROUND: Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT. METHODS: The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors. RESULTS: Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05). CONCLUSION: BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.

Tumor immune microenvironment-based clusters in predicting prognosis and guiding immunotherapy in breast cancer.

The development and progression of breast cancer (BC) depend heavily on the tumor microenvironment (TME), especially tumor infiltration leukocytes (TILs). TME-based classifications in BC remain largely unknown and need to be clarified. Using the bioinformatic analysis, we attempted to construct a prognostic nomogram based on clinical features and TME-related differentially expressed genes (DEGs). We also tried to investigate the association between the prognostic nomogram and clinical characteristics, TILs, possible signaling pathways, and response to immunotherapy in BC patients. DEGs for BC patients were identified from The Cancer Genome Atlas Breast Invasive Carcinoma database. TME-related genes were downloaded from the Immunology Database and Analysis Portal. After intersecting DEGs and TME-related genes, 3985 overlapping TME-related DEGs were selected for non-negative matrix factorization clustering, microenvironment cell populations-counter (MCP-counter), LASSO Cox regression, tumor immune dysfunction, and exclusion (TIDE) algorithm analyses. BC patients were divided into three clusters based on the TME-related DEGs and survival data, in which cluster 3 had the best overall survival (OS). Of note, cluster 3 exhibited the highest infiltration or lowest infiltration of CD3+ T-cells, CD8+ T-cells, cytotoxic lymphocytes, B-lymphocytes, monocytic lineage, and myeloid dendritic cells (MDCs). A total of 33 TME-related DEGs were identified as a prognostic gene signature by the LASSO regression analysis. The prognostic gene signature separated BC patients into low- and high-risk groups with significant differences in OS (p<0.01) and demonstrated powerful effectiveness (TCGA all group: 1-year area under the curve [AUC] = 0.773, 3-year AUC = 0.770, 5-year AUC = 0.792). By integrating demographic features, tumor-node metastasis (TNM) stages, and prognostic gene signature, we constructed a nomogram with better predictive value than other clinical features alone. TMErelated DEGs in the low-risk BC patients (with better OS) were enriched in chemokine, cytokine-cytokine receptor interaction, and JAK-STAT and Toll-like receptor signaling pathways. BC patients in the low-risk group exhibited higher TIDE scores associated with worse immune checkpoint blockade response. A prognostic nomogram based on TME-related DEGs and clinical characteristics could predict prognosis and guide immunotherapy in BC patients.

A single carbon nanotube-entangled high-performance buckypaper with tunable fracture mode.

It is well known that the traditional buckypaper (BP) is composed of a certain number of short carbon nanotubes (CNTs) intertwined with each other and sliding always happens when the BP is under tensile and impact loading, which results in inferior mechanical properties compared to single CNTs. In this work, a highly-entangled single-wire BP (SWBP) structure is constructed by a modified self-avoiding random walk approach. The in-plane mechanical properties and impacting behaviors of the SWBPs with different entanglement degrees and interface frictions are systematically investigated via newly developed coarse-grained molecular dynamics (CGMD) simulation. A coarse-grained method can effectively reflect the inter-tube van der Waals (vdW) interactions and the mechanical behaviors of CNTs, including tension, bending and adhesion. In this work, from the tensile simulations of the SWBP, the results showed that the self-locking mechanism between entangled CNTs could significantly enhance the tensile resistance of the film. Besides, the mechanical properties of the SWBP are highly dependent on the entanglement degree and the interface friction between CNTs. Furthermore, two distinct fracture modes, ductile fracture and brittle fracture, are revealed, which can be efficiently controlled by changing the related friction between CNTs. From the impacting simulations, it is found that the impacting performance can be effectively tuned by adjusting the entanglement degree of the film. In addition, the kinetic energy of the projectile could be rapidly dissipated through the stretching and bending of CNTs in the SWBP. This work provides an in-depth understanding of the effect of interface friction and entanglement degree on the mechanical properties of the buckypaper and provides a reference for the preparation of strong CNT-based micromaterials.

Phytic Acid-Promoted Exfoliation of Black Phosphorus Nanosheets for the Fabrication of Photothermal Antibacterial Coatings.

Medical device-associated infections (MDAI) caused by planktonic pathogens are of serious concern worldwide due to the emergence of drug resistance resulting from continuous overuse or misuse of antibiotics. Therefore, the design of non-antibiotics-based treatment for MDAI is of crucial importance. Black phosphorus (BP), a novel 2D material, has recently received much attention owing to its remarkable physical, chemical, mechanical, and functional features. However, the intricacy of the fabrication process has severely hampered the development of BP in prospective applications. In this study, a simple and eco-friendly liquid-phase exfoliation method of phytic acid (PA)-promoted exfoliation of BP nanosheets (PA@BP NSs) is developed for their potential application in antibacterial photothermal therapy. To impart the antimicrobial effects, the polydimethylsiloxane surfaces are functionalized with quaternized polymer (polyquaternium-2 or PQ) and PA@BP NSs, leading to the formation of PA-BP-PQ composite coatings. In addition to the contact-killing antibacterial effect of the cationic PQ, the PA-BP-PQ coating exhibits remarkable near-infrared irradiation-triggered bactericidal effects with low cytotoxicity both in vitro and in vivo. This study proposes a simple liquid-phase exfoliation technique for the fabrication of BP NSs and a one-step approach for the construction of PA-BP-PQ composite coatings for bi-modal (contact-killing and photothermal) antimicrobial therapy.

Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer.

OBJECTIVE: Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients. MATERIALS AND METHODS: A total of 152 patients diagnosed with GBC were retrospectively enrolled and divided into the serosal involvement group and no serosal involvement group according to paraffin pathology results. The regions of interest (ROIs) in the lesion on all CT images were drawn by two radiologists using ITK-SNAP software (version 3.8.0). A total of 412 features were extracted from the CT images of each patient. The Mann‒Whitney U test was applied to identify features with significant differences between groups. Seven machine learning algorithms and a deep learning model based on fully connected neural networks (f-CNNs) were used for radiomics model construction. The prediction efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Through the Mann‒Whitney U test, 75 of the 412 features extracted from the CT images of patients were significantly different between groups (P < 0.05). Among all the algorithms, logistic regression achieved the highest performance with an area under the curve (AUC) of 0.944 (sensitivity 0.889, specificity 0.8); the f-CNN deep learning model had an AUC of 0.916, and the model showed high predictive power for serosal involvement, with a sensitivity of 0.733 and a specificity of 0.801. CONCLUSION: Radiomics models based on features derived from CECT showed convincing performances in predicting serosal involvement in GBC.

Laparoscopic versus open surgery for gallbladder carcinoma: safety, feasibility, and oncological outcomes

Background Gallbladder carcinoma (GC) is a rare malignant tumor. Laparoscopic technology has revolutionized the reality of surgery. However, whether laparoscopic surgery is suitable for GC has not been clarified. We aimed to analyze the safety, feasibility, and oncological outcomes of laparoscopic surgery in GC. Methods The medical records of patients with GC treated at our hospital between January 2016 and December 2021 were retrospectively reviewed. Patients who underwent laparoscopic and open surgery were compared. Propensity score matched analysis was performed to balance the basic characteristics of the two groups. Kaplan–Meier curves were used to describe and compare the overall and disease-free survival rates between the groups. Results A total of 163 patients with GC were included. Cholelithiasis was detected in 64 (39.3%) patients. Seventy patients were matched after propensity score matching. The laparoscopic group was significantly better than the open group in terms of operation time (p < 0.001), blood loss (p = 0.002), drain time (p = 0.001), and hospital stay (p < 0.001). After a median follow-up time of 19 (12, 35) months, there was no significant difference in the cumulative overall (p = 0.650) and disease-free (p = 0.663) survival rates between the laparoscopic and open groups according to Kaplan–Meier curves. Conclusion Laparoscopic surgery can reduce the operation time and blood loss, and shorten drain time and hospital stay without increasing the incidence of complications. Patients undergoing laparoscopic and open surgery have a similar prognosis. Laparoscopic surgery is worth promoting in patients with GC (AU)