A PilZ domain protein interacts with the transcriptional regulator HinK to regulate type VI secretion system in Pseudomonas aeruginosa.

Type VI secretion systems (T6SS) are bacterial macromolecular complexes that secrete effectors into target cells or the extracellular environment, leading to the demise of adjacent cells and providing a survival advantage. Although studies have shown that the T6SS in Pseudomonas aeruginosa is regulated by the Quorum Sensing system and second messenger c-di-GMP, the underlying molecular mechanism remains largely unknown. In this study, we discovered that the c-di-GMP-binding adaptor protein PA0012 has a repressive effect on the expression of the T6SS HSI-I genes in P. aeruginosa PAO1. To probe the mechanism by which PA0012 (renamed TssZ, Type Six Secretion System -associated PilZ protein) regulates the expression of HSI-I genes, we conducted yeast two-hybrid screening and identified HinK, a LasR-type transcriptional regulator, as the binding partner of TssZ. The protein-protein interaction between HinK and TssZ was confirmed through co-immunoprecipitation assays. Further analysis suggested that the HinK-TssZ interaction was weakened at high c-di-GMP concentrations, contrary to the current paradigm wherein c-di-GMP enhances the interaction between PilZ proteins and their partners. Electrophoretic mobility shift assays revealed that the non-c-di-GMP-binding mutant TssZR5A/R9A interacts directly with HinK and prevents it from binding to the promoter of the quorum-sensing regulator pqsR. The functional connection between TssZ and HinK is further supported by observations that TssZ and HinK impact the swarming motility, pyocyanin production, and T6SS-mediated bacterial killing activity of P. aeruginosa in a PqsR-dependent manner. Together, these results unveil a novel regulatory mechanism wherein TssZ functions as an inhibitor that interacts with HinK to control gene expression.

Permanent fluidic magnets for liquid bioelectronics.

Brownian motion allows microscopically dispersed nanoparticles to be stable in ferrofluids, as well as causes magnetization relaxation and prohibits permanent magnetism. Here we decoupled the particle Brownian motion from colloidal stability to achieve a permanent fluidic magnet with high magnetization, flowability and reconfigurability. The key to create such permanent fluidic magnets is to maintain a stable magnetic colloidal fluid by using non-Brownian magnetic particles to self-assemble a three-dimensional oriented and ramified magnetic network structure in the carrier fluid. This structure has high coercivity and permanent magnetization, with long-term magnetization stability. We establish a scaling theory model to decipher the permanent fluid magnet formation criteria and formulate a general assembly guideline. Further, we develop injectable and retrievable permanent-fluidic-magnet-based liquid bioelectronics for highly sensitive, self-powered wireless cardiovascular monitoring. Overall, our findings highlight the potential of permanent fluidic magnets as an ultrasoft material for liquid devices and systems, from bioelectronics to robotics.

Physiologic changes in the hepatopulmonary syndrome before and after liver transplant: A longitudinal and predictor analysis.

BACKGROUND AND AIMS: Hepatopulmonary syndrome (HPS) is a common complication of liver disease defined by abnormal oxygenation and intrapulmonary vascular dilatation, treated with liver transplantation. Little is known about changes in HPS physiological parameters over time. We sought to describe baseline clinical and physiological characteristics in HPS and their relationships, temporal changes in physiological parameters before and after transplant, and predictors of changes in oxygenation. APPROACH AND RESULTS: This was a retrospective cohort study in the Canadian HPS Program (n = 132). Rates of change after diagnosis were: -3.7 (-6.4, -0.96) mm Hg/year for partial pressure of arterial oxygen (PaO 2 ); -26 (-96, 44) m/year for 6-minute walk distance, and 3.3% (-6.6, -0.011) predicted/year for diffusion capacity. Noninvasive shunt of ≥ 20% predicted a slower PaO 2 decline by 0.88 (0.36, 1.4) mm Hg/month. We identified 2 PaO 2 deterioration classes-"very severe disease, slow decliners" (PaO 2 45.0 mm Hg; -1.0 mm Hg/year); and "moderate disease, steady decliners" (PaO 2 65.5 mm Hg; -2.5 mm Hg/year). PaO 2 increased by 6.5 (5.3, 7.7) mm Hg/month in the first year after transplant. The median time to normalization was 149 (116, 184) days. Posttransplant improvement in PaO 2 was 2.5 (0.1, 4.9) mm Hg/month faster for every 10 mm Hg greater pretransplant orthodeoxia. CONCLUSIONS: We present a large and long longitudinal data analysis in HPS. In addition to rates of physiological decline and improvement before and after liver transplantation, we present novel predictors of PaO 2 decline and improvement rates. Our findings enhance our understanding of the natural history of HPS and provide pathophysiologic clues. Importantly, they may assist providers in prognostication and prioritization before and after transplant.

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with recurrent HCC to identify differentially expressed genes (DEGs), the involved pathways, biological functions, and potential gene signatures of recurrent HCC post-transplant using deep machine learning (ML) methodology. MATERIALS AND METHODS: We analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichment, gene ontology (GO) analyses and protein-protein interactions (PPIs) with top 10 hub gene networks. We also predicted the landscape of infiltrating immune cells using Cibersortx. We next develop pathway and GO term-based deep learning models leveraging primary tissue gene expression data from The Cancer Genome Atlas (TCGA) to identify gene signatures in recurrent HCC. RESULTS: The PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD274, in the top 10 hub gene analysis. Significantly higher infiltration of monocytes and lower M1 macrophages were found in recurrent HCC tumors. Our deep learning approach identified a 20-gene signature in recurrent HCC. Amongst the 20 genes, through multiple analysis, IL6 was found to be significantly associated with HCC recurrence. CONCLUSION: Our deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immune cell infiltration. In conclusion, IL6 was identified to play an important role in HCC recurrence.

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment.

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

The Application of CT Radiomics in the Diagnosis of Vein Wall Invasion in Patients With Renal Cell Carcinoma Combined With Tumor Thrombus.

OBJECTIVE: The objective of this study was to explore the application of radiomics combined with machine learning to establish different models to assist in the diagnosis of venous wall invasion in patients with renal cell carcinoma and venous tumor thrombus and to evaluate the diagnostic efficacy. MATERIALS AND METHODS: We retrospectively reviewed the data of 169 patients in Peking University Third Hospital from March 2015 to January 21, who was diagnosed as renal mass with venous invasion. According to the intraoperative findings, 111 patients were classified to the venous wall invasion group and 58 cases in the non-invasion group. ITK-snap was used for tumor segmentation and PyRadiomics 3.0.1 package was used for feature extraction. A total of 1598 features could be extracted from each CT image. The patients were divided into training set and testing set by time. The elastic-net regression with 4-fold cross-validation was used as a dimension-reduction method. After feature selection, a support vector machines (SVM) model, a logistic regression (LR) model, and an extra trees (ET) model were established. Then the sensitivity, specificity, accuracy, and the area under the curve (AUC) were calculated to evaluate the diagnostic performance of each model on the testing set. RESULTS: Patients before September 2019 were divided into the training set, of which 88 patients were in the invasion group and 42 patients were in the non-invasion group. The others were in the testing set, of which 32 patients were in the invasion group and 16 patients were in the non-invasion group. A total of 34 radiomics features were obtained by the elastic-net regression. The SVM model had an AUC value of 0.641 (95% CI, 0.463-0.769), a sensitivity of 1.000, and a specificity of 0.062. The LR model had an AUC value of 0.769 (95% CI, 0.620-0.877), a sensitivity of 0.913, and a specificity of 0.312. The ET model had an AUC value of 0.853 (95% CI, 0.734-0.948), a sensitivity of 0.783, and a specificity of 0.812. Among the 3 models, the ET model had the best diagnostic effect, with a good balance of sensitivity and specificity. And the higher the tumor thrombus grade, the better the diagnostic efficacy of the ET model. In inferior vena cava tumor thrombus, the sensitivity, specificity, accuracy, and AUC of ET model can be improved to 0.889, 0.800, 0.857, 0.878 (95% CI, 0.745-1.000). CONCLUSION: Machine learning combined with radiomics method can effectively identify whether venous wall was invaded by tumor thrombus and has high diagnostic efficacy with an AUC of 0.853 (95% CI, 0.734-0.948).

Biosynthesis of Octacosamicin A: Uncommon Starter/extender Units and Product Releasing via Intermolecular Amidation.

Octacosamicin A is an antifungal metabolite featuring a linear polyene-polyol chain flanked by N-hydroxyguanidine and glycine moieties. We report here that sub-inhibitory concentrations of streptomycin elicited the production of octacosamicin A in Amycolatopsis azurea DSM 43854T . We identified the biosynthetic gene cluster (oca BGC) that encodes a modular polyketide synthase (PKS) system for assembling the polyene-polyol chain of octacosamicin A. Our analysis suggested that the N-hydroxyguanidine unit originates from a 4-guanidinobutyryl-CoA starter unit, while the PKS incorporates an α-hydroxyketone moiety using a (2R)-hydroxymalonyl-CoA extender unit. The modular PKS system contains a non-canonical terminal module that lacks thioesterase (TE) and acyl carrier protein (ACP) domains, indicating the biosynthesis is likely to employ an unconventional and cryptic off-loading mechanism that attaches glycine to the polyene-polyol chain via an intermolecular amidation reaction.

Bony Congenital Nasolacrimal Duct Obstruction: A Novel Phenotype of Aplasia of Lacrimal and Major Salivary Glands.

PURPOSE: Aplasia of lacrimal and salivary glands (ALSG) is a syndromic disorder characterized by aplasia of lacrimal and salivary systems. Reported ophthalmic manifestations of ALSG include aplasia of lacrimal glands, punctal agenesis, lacrimal sac mucocele, and membranous congenital nasolacrimal duct obstruction (CNLDO). Bony CNLDO, a rare clinical entity, has not been associated with any syndromic disorder. This study investigated the relationship between genetic mutations and bony CNLDO in 3 Chinese families with ALSG. DESIGN: Single-center observational case study. PARTICIPANTS: Three Chinese families with bony CNLDO, including 7 affected and 9 healthy family members. METHODS: Slit-lamp ophthalmic examination, comprehensive physical examination, orbital computed tomography (CT) imaging, cervicofacial magnetic resonance imaging, audiometry, and whole exome sequencing on periphery blood were performed. Variants were cross-referenced with 1000 control genomes and various population databases. Pathologic variants were identified using bioinformatic tools. MAIN OUTCOME MEASURES: Clinical examination, diagnostic imaging, whole exome sequencing, and bioinformatic analysis findings. RESULTS: Affected patients showed decreased tear production on the Schimer I test and reduced tear breakup time. Bony CNLDO was observed on CT, showing unilateral or bilateral bony termination at the middle or terminal segment of the nasolacrimal canal. Magnetic resonance imaging showed aplasia or absence of lacrimal, parotid, and submandibular glands. Physical examination revealed normal ears, digits, and facial morphology. Audiometry and dental assessment were conducted on the pediatric patients and yielded normal results. The clinical characteristics of patients aligned with a diagnosis of ALSG. Genomic analysis revealed 3 novel heterozygous missense mutations of the Fgf10 gene: c.316T→C, c.327C→G, and c.332T→G. The inheritance pattern was autosomal dominant with variable penetrance. These variants were not observed in 1000 control genomes and population databases. These variant positions also were shown to be highly conserved across various animal species. Mutated genes and proteins were predicted as deleterious with most computational models, with a few suggesting they may be benign. CONCLUSIONS: Bony CNLDO was identified as a novel phenotype of ALSG implicated by missense mutations of highly conserved residues in the Fgf10 gene. These cases broadened our knowledge of Fgf10-related phenotypes and prompted clinicians to consider syndromic associations in patients with bony CNLDO. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Insight into the Types of Alkanolamines on the Properties of Copper(II) Formate-Based Conductive Ink.

Conductive inks are one of the most important functional materials for printed flexible electronic devices, and their properties determine the methods of subsequent patterning and metallization. In comparison with copper nanoparticle or nanowire inks, copper particle-free inks employing copper(II) formate (Cuf) as a precursor have attracted the interest of researchers due to their flexibility in preparation, excellent stability, and lower conversion temperature. Alkanolamines can provide Cuf with excellent solubility in alcohols while being less toxic and having a certain reducibility, making them preferable ligands in comparison with aliphatic amines and pyridine. However, there have been few studies on the effects of the alkanolamine types on the performance of Cuf inks. Also, the decomposition mechanism of copper-alkanolamine complex inks is not clear. In this work, different kinds of alkanolamines were chosen as ligands to formulate Cuf inks to address the mentioned issues. The influences of amine types on the stability, wettability, thermal decomposition behavior, and electrical performance of the formulated Cuf particle-free inks were investigated in detail. The results show that the utilization of alkanolamines could provide Cuf with excellent solubility in alcohols, resulting in an ink with good stability and favorable wetting properties. The thermal decomposition temperature and electrical performance of the formulated copper ink are largely dependent on the amine used. When amines with a longer carbon chain and more branches were utilized to prepare the ink, a decreased decomposition temperature was observed on the derived inks because of the steric hindrance effect. Copper films with good morphology and conductivity could be obtained at low temperatures by selecting the appropriate alkanolamine. Copper particle-free conductive ink from 2-amino-2-methyl-1-propanol demonstrated better morphology and electrical performance (16.09 µΩ·cm) and was successfully used for conductive circuits by direct-writing.

Factors predicting indistinct plane of surgical capsule in patients underwent HoLEP procedures.

PURPOSE: To evaluate factors that effectively predict indistinct plane (IP) in patients who underwent holmium laser enucleation of the prostate (HoLEP). METHODS: Data of 208 consecutive patients from our HoLEP database were reviewed and analyzed. IP was defined in 107 cases, as the plane could be identified only depending on endoscopic beak dissection rather than laser dissection in the initial stage of HoLEP, whereas the control group consisted of 101 cases. Variables including age, body mass index, prostatic volume (PV), intravesical prostatic protrusion, prostate-specific antigen, prostate-specific antigen density, bladder stones, urinary tract infection, microscopic hematuria, prior biopsy (PB), diabetes, hypertension, history of acute urinary retention, 5-alpha reductase inhibitor treatment, catheter dependency, residual urine, region, smoking, and alcohol consumption were compared between the two groups. The risk factors for predicting the presence of IP were determined using a multivariable binary logistic regression model using a forward selection approach with a focus on improvement in the area under the receiver operating characteristic curve (AUC). RESULTS: The incidence of IP was 51.4% (107/208). PV (OR = 0.977, p < 0.001) and PB (OR = 0.297, p = 0.028) were identified as the independent predictors of capsule plane status. PV with a cutoff of 54 ml had the best predictive effectiveness for IP based on AUC (0.727; 95% CI 0.659-0.795). The specificity and sensitivity of this cutoff were 82.2% and 53.3%, respectively. CONCLUSION: PV is the most reliable factor to predict IP during HoLEP procedures. There is a high possibility of IP in patients with a PV less than 54 ml.

Influence of tumor thrombus morphology on the surgical complexity in renal cell carcinoma with inferior vena cava tumor thrombus: a single-center, large-sample study from China.

BACKGROUND: The morphology of tumor thrombus varies from person to person and it may affect surgical methods and tumor prognosis. However, studies on the morphology of tumor thrombus are limited. The purpose of our study was to evaluate the impact of tumor thrombus morphology on surgical complexity. METHODS: We retrospectively reviewed the clinical data of 229 patients with renal cell carcinoma combined with inferior vena cava (IVC) tumor thrombus who underwent surgical treatment at Peking University Third Hospital between January 2014 and December 2021. The patients were divided into floating morphology (107 patients) and filled morphology (122 patients) tumor thrombi groups. Chi-square and Mann-Whitney U tests were used for categorical and continuous variables, respectively. Postoperative complications were evaluated using the Clavien-Dindo surgical complication classification method. RESULTS: Patients with filled morphology tumor thrombus required more surgical techniques than those with floating morphology tumor thrombus, which was reflected in more open surgeries (P < 0.001), more IVC interruptions (P <0.001), lesser use of the delayed occlusion of the proximal inferior vena cava (DOPI) technique (P < 0.001), and a greater need for cut-off of the short hepatic vein (P < 0.001) and liver dissociation (P = 0.001). Filled morphology significantly increased the difficulty of surgery in patients with renal cell carcinoma with tumor thrombus, reflected in longer operation time (P < 0.001), more surgical blood loss (P <0.001), more intra-operative blood transfusion (P < 0.001), and longer postoperative hospital stay (P < 0.001). Filled morphology tumor thrombus also led to more postoperative complications (53% vs. 20%; P < 0.001). CONCLUSION: Compared with floating morphology thrombus, filled morphology thrombus significantly increased the difficulty of surgery in patients with renal cell carcinoma with IVC tumor thrombus.

Electronic Textiles for Wearable Point-of-Care Systems.

Traditional public health systems are suffering from limited, delayed, and inefficient medical services, especially when confronted with the pandemic and the aging population. Fusing traditional textiles with diagnostic, therapeutic, and protective medical devices can unlock electronic textiles (e-textiles) as point-of-care platform technologies on the human body, continuously monitoring vital signs and implementing round-the-clock treatment protocols in close proximity to the patient. This review comprehensively summarizes the research advances on e-textiles for wearable point-of-care systems. We start with a brief introduction to emphasize the significance of e-textiles in the current healthcare system. Then, we describe textile sensors for diagnosis, textile therapeutic devices for medical treatment, and textile protective devices for prevention, by highlighting their working mechanisms, representative materials, and clinical application scenarios. Afterward, we detail e-textiles' connection technologies as the gateway for real-time data transmission and processing in the context of 5G technologies and Internet of Things. Finally, we provide new insights into the remaining challenges and future directions in the field of e-textiles. Fueled by advances in chemistry and materials science, textile-based diagnostic devices, therapeutic devices, protective medical devices, and communication units are expected to interact synergistically to construct intelligent, wearable point-of-care textile platforms, ultimately illuminating the future of healthcare system in the Internet of Things era.

Anti-swelling Microporous Membrane for High-capacity and Long-life Zn-I2 Batteries.

Zinc-iodine (Zn-I2) batteries are gaining popularity due to cost-effectiveness and ease of manufacturing. However, challenges like polyiodide shuttle effect and Zn dendrite growth hinder their practical application. Here, we report a cation exchange membrane to simultaneously prevent the polyiodide shuttle effect and regulate Zn2+ deposition. Comprised of rigid polymers, this membrane shows superior swelling resistance and ion selectivity compared to commercial Nafion. The resulting Zn-I2 battery exhibits a high Coulombic efficiency of 99.4% and low self-discharge rate of 4.47% after 48 h rest. By directing a uniform Zn2+ flux, the membrane promotes a homogeneous electric field, resulting in a dendrite-free Zn surface. Moreover, its microporous structure enables pre-adsorption of additional active materials prior to battery assembly, boosting battery capacity to 287 mA h g-1 at 0.1 A g-1. At 2 A g-1, the battery exhibits a steady running for 10,000 cycles with capacity retention up to 96.1%, demonstrating high durability of the membrane. The practicality of the membrane is validated via a high loading (35 mg cm-2) pouch cell with impressive cycling stability, paving a way for membrane design towards advanced Zn-I2 batteries.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

BACKGROUND: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. METHODS: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. RESULTS: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. CONCLUSIONS: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Implantable Triboelectric Nanogenerators for Self-Powered Cardiovascular Healthcare.

Triboelectric nanogenerators (TENGs) have gained significant traction in recent years in the bioengineering community. With the potential for expansive applications for biomedical use, many individuals and research groups have furthered their studies on the topic, in order to gain an understanding of how TENGs can contribute to healthcare. More specifically, there have been a number of recent studies focusing on implantable triboelectric nanogenerators (I-TENGs) toward self-powered cardiac systems healthcare. In this review, the progression of implantable TENGs for self-powered cardiovascular healthcare, including self-powered cardiac monitoring devices, self-powered therapeutic devices, and power sources for cardiac pacemakers, will be systematically reviewed. Long-term expectations of these implantable TENG devices through their biocompatibility and other utilization strategies will also be discussed.

Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases.

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.

Applications and Advances in Machine Learning Force Fields.

Force fields (FFs) form the basis of molecular simulations and have significant implications in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is required to accurately describe system properties. However, an off-the-shelf FF may not be suitable for certain specialized systems, and researchers often need to tailor the FF that fits specific requirements. Before applying machine learning (ML) techniques to construct FFs, the mainstream FFs were primarily based on first-principles force fields (FPFF) and empirical FFs. However, the drawbacks of FPFF and empirical FFs are high cost and low accuracy, respectively, so there is a growing interest in using ML as an effective and precise tool for reconciling this trade-off in developing FFs. In this review, we introduce the fundamental principles of ML and FFs in the context of machine learning force fields (MLFF). We also discuss the advantages and applications of MLFF compared to traditional FFs, as well as the MLFF toolkits widely employed in numerous applications.

Long-term outcomes after cytoreductive nephrectomy and thrombectomy of patients with metastatic renal cell carcinoma with venous tumor thrombus: a retrospective study from a large Chinese center.

BACKGROUND: Targeted therapy combined with immunotherapy is the current first-line treatment for metastatic renal cell carcinoma (mRCC), but patients with tumor thrombus (TT) may suffer from lower limb edema or even sudden cardiac death, so the purpose of this study is to investigate the efficacy and safety of surgical treatment in patients with mRCC and TT and explore worse factors to affect the prognosis in this series of patients. PATIENTS AND METHODS: A total of 85 mRCC patients with TT who received cytoreductive nephrectomy and thrombectomy at our medical center from 2014 to 2023 are included. All patients received postoperative systemic therapy. Overall survival (OS) is defined as the time from surgery to death due to any reason or the last follow-up. Kaplan-Meier analysis was performed to evaluate OS and differences among groups were tested by log-rank. Multivariable Cox proportional hazards analysis was performed to ascertain independent relationships between clinicopathological factors and OS. RESULTS: The median age of patients was 58 years old. Eleven patients (12.9%) had no symptoms, 39 patients (45.9%) had local symptoms, 15 patients (17.6%) had systemic symptoms, and 20 patients (23.5%) had both. Mayo grade of TT was 0, 1, 2, 3, and 4 for 12, 27, 31, 7, and 8 patients respectively. Fifty-five patients had lung metastasis, 23 had bone metastasis, 16 had liver metastasis, 13 had adrenal metastasis, and 9 had lymph node metastasis. Of all patients, 17 patients had multiple metastases. The median operation time is 289 min and the median intraoperative hemorrhage is 800 ml. Twenty-eight patients experienced postoperative complications, 8 of which were serious complications of modified Clavien grade III or higher. The median OS of all patients was 33 months and median follow up time was 26 months. In multivariate analysis, systemic symptom (p = 0.00753), pathological type (p = 0.0166), sarcomatous degeneration (p = 0.0334), and perirenal fat infiltration (p = 0.0202) are independent predictors of OS. CONCLUSION: Cytoreductive nephrectomy and thrombectomy is relatively safe and effective for patients with mRCC accompanied by TT. In this series of patients, the worse prognosis is associated with systemic symptoms, non-clear cell carcinoma, sarcomatous degeneration and perirenal fat infiltration.

Human umbilical cord mesenchymal stem cell-derived exosomes combined with gelatin methacryloyl hydrogel to promote fractional laser injury wound healing.

To investigate whether human umbilical cord mesenchymal stem cell-derived exosomes combined with gelatin methacryloyl (GelMA) hydrogel are beneficial in promoting healing of laser-injured skin wounds in mice. Supernatants of cultured human umbilical cord mesenchymal stem cells (HUC-MSCs) were collected to obtain human umbilical cord MSC-derived exosomes (HUC-MSCs-Exos), which were combined with GelMA hydrogel complex to treat a mouse fractional laser injury model. The study was divided into PBS group, EX (HUC-MSCs-Exos) group, GEL (GelMA hydrogel) group and EX+GEL (HUC-MSCs-Exos combined with GelMA hydrogel) group. The healing of laser-injured skin in each group was observed by gross view and dermatoscopy, and changes in skin structure, angiogenesis and proliferation-related indexes were observed during the healing process of laser-injured skin in each group. The results of the animal experiments showed that the EX and GEL groups alone and the EL+EX group exhibited less inflammatory response compared to the PBS group. The EX and GEL groups showed marked tissue proliferation and favourable angiogenesis, which promoted the wound healing well. The GEL+EX group had the most significant promotion of wound healing compared to the PBS group. qPCR results showed that the expression levels of proliferation-related factors, including KI67 and VEGF and angiogenesis-related factor CD31, were significantly higher in the GEL+EX group than in the other groups, with a time-dependent effect. The combination of HUC-MSCs-Exos and GelMA hydrogel is beneficial in reducing the early inflammatory response of laser-injured skin in mice and promoting its proliferation and angiogenesis, which in turn promotes wound healing.

[Effect of Interactions Among Obesity-Related Proteins on Breast Cancer Risk: A Preliminary Study].

Objective: To explore the potential interactions among obesity-related proteins in the pathogenic process of breast cancer (BC) in women. Methods: We conducted a case-control study, enrolling 279 primary breast cancer cases and 260 age-frequency-matched healthy women between April 2014 and May 2015. Based on the evidence of previous published literature on obesity-related proteins and BC risks, we selected proteins that received more attention and measured the plasma levels of these proteins by enzyme-linked immunosorbent assay (ELISA). After stratification of the subjects according to their menopausal status, an analytic strategy combining multivariate logistic regression and generalized multifactor dimensionality reduction (GMDR) was used to explore the effect of the possible interactions of these proteins on BC risk. Results: There were marginal high-order interactions among insulin-like growth factor 1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), C-reactive protein (CRP), resistin (RETN), soluble leptin receptor (sOB-R), and adiponectin (ADP) in premenopausal women (with the balanced accuracy for the testing set being 59.01%, cross-validation consistency being 10/10, and permutation test P=0.05). There were high-order interactions among leptin (LEP), sOB-R, ADP, CRP, IGFBP3 and visfatin (VF) in postmenopausal women (with the balanced accuracy for the testing set being 67.31%, cross-validation consistency being 10/10, and permutation test P=0.01). Along with an increase in the number of obesity-related proteins to which the subjects were exposed, the risk of developing breast cancer gradually increased in both pre- and postmenopausal women ( OR pre =2.18, 95% CI: 1.69-2.82; OR post =2.41, 95% CI: 1.75-3.32). Conclusions: This preliminary study suggested high-order interactions among obesity-related proteins on BC risk in both pre- and postmenopausal women. In future studies, close attention should be given to these potential interactions when these proteins are used jointly as predictors, as well as in developing a comprehensive risk scoring system for BC.