Achieving Uniform Li Deposition and Suppressed Electrolyte Flammability in Li-Metal Batteries via Designing Localized High-Concentration Electrolytes.

The increasing need for energy storage devices with high energy density has led to significant interest in Li-metal batteries (LMBs). However, the use of commercial electrolytes in LMBs is problematic due to their flammability, inadequate performance at low temperatures, and tendency to promote the growth of lithium dendrites and other flaws. This study introduces a localized high-concentration electrolyte (LHCE) that addresses these issues by employing non-flammable electrolyte components and incorporating carefully designed additives to enhance flame retardancy and low-temperature performance. By incorporating additives to optimize the electrolyte, it is possible to attain inorganic-dominated solid electrolyte interphases on both the cathode and anode. This achievement results in a uniform deposition of lithium, as well as the suppression of electrolyte decomposition and cathode deterioration. Consequently, this LHCE achieve over 300 stable cycles for both LiNi0.9Mn0.05Co0.05O2||Li cells and LiCoO2||Li cells, as well as 50 cycles for LiNi0.8Mn0.1Co0.1O2 (NCM811||Li) pouch cells. Furthermore, NCM811||Li cells maintain 84% discharge capacity at -20 °C, in comparison to the capacity at room temperature. The utilization of this electrolyte presents novel perspectives for the safe implementation of LMBs.

Beyond Small Molecules: Antibodies and Peptides for Fibroblast Activation Protein Targeting Radiopharmaceuticals.

Fibroblast activation protein (FAP) is a serine protease characterized by its high expression in cancer-associated fibroblasts (CAFs) and near absence in adult normal tissues and benign lesions. This unique expression pattern positions FAP as a prospective biomarker for targeted tumor radiodiagnosis and therapy. The advent of FAP-based radiotheranostics is anticipated to revolutionize cancer management. Among various types of FAP ligands, peptides and antibodies have shown advantages over small molecules, exemplifying prolonged tumor retention in human volunteers. Within its scope, this review summarizes the recent research progress of the FAP radiopharmaceuticals based on antibodies and peptides in tumor imaging and therapy. Additionally, it incorporates insights from recent studies, providing valuable perspectives on the clinical utility of FAP-targeted radiopharmaceuticals.

Stabilization of LiCoO2 Cathodes in High Voltage Lithium Metal Batteries Through 2-(Trifluoromethyl)Benzamide (2-TFMBA) Electrolyte Additives.

Increasing the charging cutoff voltage of LiCoO2 to 4.6 V is significant for enhancing battery density. However, the practical application of Li‖LiCoO2 batteries with a 4.6 V cutoff voltage faces significant impediments due to the detrimental changes under high voltage. This study presents a novel bifunctional electrolyte additive, 2-(trifluoromethyl)benzamide (2-TFMBA), which is employed to establish a stable and dense cathode-electrolyte interface (CEI). Characterization results reveal that an optimized CEI is achieved through the synergistic effects of the amide groups and trifluoromethyl groups within 2-TFMBA. The resulting CEI not only enhances the structural stability of LiCoO2 but also serves as a high-speed lithium-ion conduction channel, which expedites the insertion and extraction of lithium ions. The Li‖LiCoO2 batteries with 0.5 wt% 2-TFMBA achieves an 84.7% capacity retention rate after enduring 300 cycles at a current rate of 1 C, under a cut-off voltage of 4.6 V. This study provides valuable strategic insights into the stabilization of cathode materials in high-voltage batteries.

Identification of angiogenesis-related genes in diabetic foot ulcer using machine learning algorithms.

Background: Diabetic foot ulcers (DFUs) are among the most prevalent and dangerous complications of diabetes. Angiogenesis is pivotal for wound healing; however, its role in the chronic wound healing process in DFU requires further investigation. We aimed to investigate the pathogenic processes of angiogenesis in DFU from a molecular biology standpoint and to offer insightful information about DFU prevention and therapy. Methods: Differential gene and weighted gene co-expression network analyses (WGCNA) were employed to screen for genes related to DFU using the downloaded and collated GSES147890 datasets. With the goal of identifying hub genes, an interaction among proteins (PPI) network was constructed, and enrichment analysis was carried out. Utilizing a variety of machine learning techniques, including Boruta, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), we were able to determine which hub genes most strongly correspond to DFU. This allowed us to create an ideally suited DFU forecasting model that was validated via an external dataset. Finally, by merging 36 angiogenesis-related genes (ARGs) and machine learning models, we identified the genes involved in DFU-related angiogenesis. Results: By merging 260 genes located in the green module and 59 differentially expressed genes (DEGs), 35 candidate genes highly associated with DFU were found for more investigation. 35 candidate genes were enriched in epidermal growth factor receptor binding, nuclear division regulation, fluid shear stress, atherosclerosis, and negative regulation of chromosomal structure for the enrichment study. Fifteen hub genes were found with the aid of the CytoHubba plug. The LASSO method scored better in terms of prediction performance (GSE134341) (LASSO:0.89, SVM:0.65, Boruta:0.66) based on the validation of the external datasets. We identified thrombomodulin (THBD) as a key target gene that potentially regulates angiogenesis during DFU development. Based on the external validation dataset (GSE80178 and GSE29221), receiver operating characteristic (ROC) curves with higher efficiency were generated to confirm the potential of THBD as a biomarker of angiogenesis in DFU. Furthermore supporting this finding were the results of Western blot and real-time quantitative polymerase chain reaction (RT-qPCR), which showed decreased THBD expression in human umbilical vein endothelial cells (HUVECs) cultivated under high glucose. Conclusions: The findings implicate that THBD may influence DFU progression as a potential target for regulating angiogenesis, providing a valuable direction for future studies.

Marriage of radiotracers and total-body PET/CT rapid imaging system: current status and clinical advances.

Radiotracers and medical imaging equipment are the two main keys to molecular imaging. While radiotracers are of great interest to research and industry, medical imaging equipment technology is blossoming everywhere. Total-body PET/CT (TB-PET/CT) has emerged in response to this trend and is rapidly gaining traction in the fields of clinical oncology, cardiovascular medicine, inflammatory/infectious diseases, and pediatric diseases. In addition, the use of a growing number of radiopharmaceuticals in TB-PET/CT systems has shown promising results. Notably, the distinctive features of TB-PET/CT, such as its ultra-long axial field of view (194 cm), ultra-high sensitivity, and capability for low-dose tracer imaging, have enabled enhanced imaging quality while reducing the radiation dose. The envisioned whole-body dynamic imaging, delayed imaging, personalized disease management, and ultrafast acquisition for motion correction, among others, are achieved. This review highlights two key factors affecting molecular imaging, describing the rapid imaging effects of radiotracers allowed at low doses on TB-PET/CT and the improvements offered compared to conventional PET/CT.

The awareness and determinants of diabetic foot ulcer prevention among diabetic patients: Insights from NHANES (2011-2018).

The prevention of diabetic foot ulcers (DFU) precedes treatment, in that early prevention significantly reduces the incidence of foot ulcers. The main objectives of this study were to examine the current prevalence of proactive foot ulcer examinations among diabetic patients and analyze influencing factors, in order to provide a scientific reference for the prevention of DFU in diabetic patients. The National Health and Nutrition Examination Survey (NHANES) 2011-2018 (n = 1278) data were utilized in this cross-sectional study. The dependent variable was whether patients underwent self-initiated foot ulcer inspections; risk factors that may lead to foot ulcers were included as independent variables. To explore the connection between the patient's subjective motivation to inspect foot ulcers and risk variables, the weighted logistic regression model was further carried out. Among all risk factors, race, body mass index (BMI) and hypertension were statistically significant between whether patients were examined for foot ulcers or not. In the fully adjusted logistic regression model, only hypertension was positively correlated with diabetic patient-initiated examination for foot ulcers. This study suggests that there is still room for improvement in the knowledge and behavior of diabetic patients to be proactive in preventing DFU. Health care and community workers should conduct targeted training on diabetic foot prevention to reduce and prevent DFU by reinforcing knowledge to build positive attitudes and drive preventive behavior change.

Preclinical characterization of tunlametinib, a novel, potent, and selective MEK inhibitor.

Background: Aberrant activation of RAS-RAF-MEK-ERK signaling pathway has been implicated in more than one-third of all malignancies. MEK inhibitors are promising therapeutic approaches to target this signaling pathway. Though four MEK inhibitors have been approved by FDA, these compounds possess either limited efficacy or unfavorable PK profiles with toxicity issues, hindering their broadly application in clinic. Our efforts were focused on the design and development of a novel MEK inhibitor, which subsequently led to the discovery of tunlametinib. Methods: This study verified the superiority of tunlametinib over the current MEK inhibitors in preclinical studies. The protein kinase selectivity activity of tunlametinib was evaluated against 77 kinases. Anti-proliferation activity was analyzed using the 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) or (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) (MTS) assay. ERK and phospho-ERK levels were evaluated by Western blot analysis. Flow cytometry analysis was employed to investigate cell cycle and arrest. Cell-derived xenograft (CDX) and Patient-derived xenograft (PDX) models were used to evaluate the tumor growth inhibition. The efficacy of tunlametinib as monotherapy treatment was evaluated in KRAS/BRAF mutant or wild type xenograft model. Furthermore, the combination studies of tunlametinib with BRAF/KRASG12C/SHP2 inhibitors or chemotherapeutic agent were conducted by using the cell proliferation assay in vitro and xenograft models in vivo. Results: In vitro, tunlametinib demonstrated high selectivity with approximately 19-fold greater potency against MEK kinase than MEK162, and nearly 10-100-fold greater potency against RAS/RAF mutant cell lines than AZD6244. In vivo, tunlametinib resulted in dramatic tumor suppression and profound inhibition of ERK phosphorylation in tumor tissue. Mechanistic study revealed that tunlametinib induced cell cycle arrest at G0/G1 phase and apoptosis of cells in a dose-proportional manner. In addition, tunlametinib demonstrated a favorable pharmacokinetic profile with dose-proportionality and good oral bioavailability, with minimal drug exposure accumulation. Furthermore, tunlametinib combined with BRAF/KRASG12C/SHP2 inhibitors or docetaxel showed synergistically enhanced response and marked tumor inhibition. Conclusion: Tunlametinib exhibited a promising approach for treating RAS/RAF mutant cancers alone or as combination therapies, supporting the evaluation in clinical trials. Currently, the first-in-human phase 1 study and pivotal clinical trial of tunlametinib as monotherapy have been completed and pivotal trials as combination therapy are ongoing.

Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning.

Background: Diabetic osteoporosis exhibits heterogeneity at the molecular level. Ferroptosis, a controlled form of cell death brought on by a buildup of lipid peroxidation, contributes to the onset and development of several illnesses. The aim was to explore the molecular subtypes associated with ferroptosis in diabetic osteoporosis at the molecular level and to further elucidate the potential molecular mechanisms. Methods: Integrating the CTD, GeneCards, FerrDb databases, and the microarray data of GSE35958, we identified ferroptosis-related genes (FRGs) associated with diabetic osteoporosis. We applied unsupervised cluster analysis to divide the 42 osteoporosis samples from the GSE56814 microarray data into different subclusters based on FRGs. Subsequently, FRGs associated with two ferroptosis subclusters were obtained by combining database genes, module-related genes of WGCNA, and differentially expressed genes (DEGs). Eventually, the key genes from FRGs associated with diabetic osteoporosis were identified using the least absolute shrinkage and selection operator (LASSO), Boruta, support vector machine recursive feature elimination (SVM - RFE), and extreme gradient boosting (XGBoost) machine learning algorithms. Based on ROC curves of external datasets (GSE56815), the model's efficiency was examined. Results: We identified 15 differentially expressed FRGs associated with diabetic osteoporosis. In osteoporosis, two distinct molecular clusters related to ferroptosis were found. The expression results and GSVA analysis indicated that 15 FRGs exhibited significantly different biological functions and pathway activities in the two ferroptosis subclusters. Therefore, we further identified 17 FRGs associated with diabetic osteoporosis between the two subclusters. The results of the comprehensive analysis of 17 FRGs demonstrated that these genes were heterogeneous and had a specific interaction between the two subclusters. Ultimately, the prediction model had a strong foundation and excellent AUC values (0.84 for LASSO, 0.84 for SVM - RFE, 0.82 for Boruta, and 0.81 for XGBoost). IDH1 is a common gene to all four algorithms thus being identified as a key gene with a high AUC value (AUC = 0.698). Conclusions: As a ferroptosis regulator, IDH1 is able to distinguish between distinct molecular subtypes of diabetic osteoporosis, which may offer fresh perspectives on the pathogenesis of the disease's clinical symptoms and prognostic heterogeneity.

Highly sensitive SPR curvature sensor based on graded-index fiber.

At present, fiber curvature sensors based on surface plasmon resonance (SPR) are mostly of the multimode fiber core type or cladding type. These types have many SPR modes, resulting that the sensitivity cannot be adjusted and is difficult to improve. In this Letter, a highly sensitive SPR curvature sensor based on graded-index fiber is proposed. The light-injecting fiber is eccentrically connected with the graded-index fiber to inject single-mode light. Due to the self-focusing effect, the light beam propagates in the graded-index multimode fiber with a cosine trajectory, and the cosine beam contacts the flat grooved sensing region fabricated on the graded-index fiber to generate SPR. Due to the single transmission mode of the proposed fiber SPR sensor, the curvature sensing sensitivity is greatly improved. By changing the light injection position of the graded-index multimode fiber, the sensitivity can be adjusted. The proposed curvature sensing probe has a high sensitivity and can identify the bending direction. When bending in the X direction, the sensitivity reaches 5.62 nm/m-1, and when bending in the - X direction, the sensitivity reaches 4.75 nm/m-1, which provides a new scheme for highly sensitive and directionally identifiable curvature measurement.

Linear Peptide-Based PET Tracers for Imaging PD-L1 in Tumors.

Programmed cell death receptor 1 (PD-1) and its ligand PD-L1 are particularly interesting immune checkpoint proteins for human cancer treatment. Positron emission tomography (PET) imaging allows for the dynamic monitoring of PD-L1 status during tumor progression, thus informing patients' response index. Herein, we report the synthesis of two linear peptide-based radiotracers, [64Cu]/[68Ga]HKP2201 and [64Cu]/[68Ga]HKP2202, and validate their utility for PD-L1 visualization in preclinical models. The precursor peptide HKP2201 was derived from a linear peptide ligand, CLP002, which was previously identified by phage display and showed nanomolar affinity toward PD-L1. Appropriate modification of CLP002 via PEGylation and DOTA conjugation yielded HKP2201. The dimerization of HKP2201 generated HKP2202. The 64Cu and 68Ga radiolabeling of both precursors was studied and optimized. PD-L1 expression in mouse melanoma cell line B16F10, mouse colon cancer cell line MC38, and their allografts were assayed by immunofluorescence and immunohistochemistry staining. Cellular uptake and binding assays were conducted in both cell lines. PET imaging and ex vivo biodistribution studies were employed in tumor mouse models bearing B16F10 and MC38 allografts. [64Cu]/[68Ga]HKP2201 and [64Cu]/[68Ga]HKP2202 were obtained with satisfactory radiocharacteristics. They all showed lower liver accumulation compared to [64Cu]/[68Ga]WL12. B16F10 and MC38 cells and their tumor allografts sections were verified to express PD-L1. These tracers demonstrated a concentration-dependent cell affinity and a comparable half-maximal effect concentration (EC50) with radiolabeled WL12. Competitive binding and blocking studies demonstrated the specific target of these tracers to PD-L1. PET imaging and ex vivo biodistribution studies revealed notable tumor uptake in tumor-bearing mice and rapid clearance from blood and major organs. Importantly, [64Cu]/[68Ga]HKP2202 showed higher tumor uptake compared to [64Cu]/[68Ga]HKP2201. Of note, [64Cu] labeled tracers showed longer retention in tumors than [68Ga] labeled traces, indicating advantages in the long-term tracking of PD-L1 dynamics. In comparison, [68Ga]HKP2201 and [68Ga]HKP2202 showed lower liver accumulation, enabling its great potential in the fast detection of both primary and metastatic tumors, including hepatic carcinoma. [64Cu]/[68Ga]HKP2201 and [64Cu]/[68Ga]HKP2202 are promising PET tracers for visualizing PD-L1 status. Notably, their combination would cooperate in rapid diagnosis and subsequent treatment guidance. Future assessment of the radiotracers in patients is needed to fully evaluate their clinical value.

Multi working mode SPR chip laboratory for high refractive index detection.

The Fiber SPR chip laboratory has become a popular choice in biochemical detection. To meet the needs of different kinds of analytes for the detection range and number of channels of the chip, we proposed a multi-mode SPR chip laboratory based on microstructure fiber in this paper. The chip laboratory was integrated with microfluidic devices made from PDMS and detection units made of bias three-core fiber and dumbbell fiber. By injecting light into different cores of a bias three-core fiber, different detection areas of dumbbell fiber can be selected, enabling the chip laboratory to enter high refractive index detection, multi-channel detection and other working modes. In the high refractive index detection mode, the chip can detect liquid samples with a refractive index range of 1.571-1.595. In multi-channel detection mode, the chip can achieve dual parameter detection of glucose and GHK-Cu, with sensitivities of 4.16 nm/(mg/mL) and 9.729 nm/(mg/mL), respectively. Additionally, the chip can switch to temperature compensation mode. The proposed multi working mode SPR chip laboratory, based on micro structured fiber, offers a new approach for the development of portable testing equipment that can detect multiple analytes and meet multiple requirements.

Transcriptome analysis based on machine learning reveals a role for autoinflammatory genes of chronic nonbacterial osteomyelitis (CNO).

Chronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disorder. The origin and development of CNO involve many complex immune processes, resulting in delayed diagnosis and a lack of effective treatment. Although bioinformatics analysis has been utilized to seek key genes and pathways in CNO, only a few bioinformatics studies that focus on CNO pathogenesis and mechanisms have been reported. This study aimed to identify key biomarkers that could serve as early diagnostic or therapeutic markers for CNO. Two RNA-seq datasets (GSE133378 and GSE187429) were obtained from the Gene Expression Omnibus (GEO). Weighted gene coexpression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to identify the genes associated with CNO. Then, the autoinflammatory genes most associated with CNO were identified based on the GeneCards database and a CNO prediction model, which was created by the LASSO machine learning algorithm. The accuracy of the model and effects of the autoinflammatory genes according to receiver operating characteristic (ROC) curves were verified in external datasets (GSE7014). Finally, we performed clustering analysis with ConsensusClusterPlus. In total, eighty CNO-related genes were identified and were significantly enriched in the biological processes regulation of actin filament organization, cell-cell junction organization and gamma-catenin binding. The main enriched pathways were adherens junctions, viral carcinogenesis and systemic lupus erythematosus. Two autoinflammatory genes with high expression in CNO samples were identified by combining an optimal machine learning algorithm (LASSO) with the GeneCards database. An external validation dataset (GSE187429) was utilized for ROC analysis of the prediction model and two genes, and the results indicated good efficiency. Then, based on consensus clustering analysis, we found that the expression of UTS2 and MPO differed between clusters. Finally, the ceRNA network of lncRNAs and the small molecule compounds targeting the two autoinflammatory genes were predicted. The identification of two autoinflammatory genes, the HCG18/has-mir-147a/UTS2/MPO axis and signalling pathways in this study can help us understand the molecular mechanism of CNO formation and provides candidate targets for the diagnosis and treatment of CNO.

Triaxial Creep Mechanical Behaviors and Creep Damage Model of Dolomitic Limestone Material under Multi-Stage Incremental Loading.

Dolomitic limestone is the main surrounding rock material in Yangzong tunnel engineering; the instantaneous mechanical properties and creep behaviors of limestone are significant for stability evaluation during the stages of tunnel excavation and long-term maintenance. Herein, four conventional triaxial compression tests were carried out to explore its instantaneous mechanical behavior and failure characteristics; subsequently, the creep behaviors of limestone subjected to multi-stage incremental axial loading at the confinements of 9 MPa and 15 MPa were studied by employing an advanced rock mechanics testing system (i.e., MTS815.04). The results reveal the following. (1) comparing the curves of axial strain-, radial strain-, and volumetric strain-stress under different confining pressures shows that these curves present a similar trend, whereas the stress drops during the post-peak stage decelerate with the increase in confining pressure, suggesting that the rock transits from brittleness to ductility. The confining pressure also has a certain role in controlling the cracking deformation during the pre-peak stage. Besides, the proportions of compaction- and dilatancy-dominated phases in the volumetric strain-stress curves differ obviously. Moreover, the failure mode of the dolomitic limestone is a shear-dominated fracture but is also affected by the confining pressure. (2) When the loading stress reaches a creep threshold stress, the primary and steady-state creep stages occur successively, and a higher deviatoric stress corresponds to a greater creep strain. When the deviatoric stress surpasses an accelerated creep threshold stress, a tertiary creep appears and then is followed by creep failure. Furthermore, the two threshold stresses at 15 MPa confinement are greater than that at 9 MPa confinement, suggesting that the confining pressure has an obvious impact on the threshold values and a higher confining pressure corresponds to a greater threshold value. Additionally, the specimen's creep failure mode is one of "abrupt" shear-dominated fracturing and is similar to that under a conventional triaxial compression test at high confining pressure. (3) A multi-element nonlinear creep damage model is developed by bonding a proposed visco-plastic model in series with the Hookean substance and Schiffman body, and can accurately describe the full-stage creep behaviors.

In Vivo Reversal of P-Glycoprotein-Mediated Drug Resistance in a Breast Cancer Xenograft and in Leukemia Models Using a Novel, Potent, and Nontoxic Epicatechin EC31.

The modulation of P-glycoprotein (P-gp, ABCB1) can reverse multidrug resistance (MDR) and potentiate the efficacy of anticancer drugs. Tea polyphenols, such as epigallocatechin gallate (EGCG), have low P-gp-modulating activity, with an EC50 over 10 µM. In this study, we optimized a series of tea polyphenol derivatives and demonstrated that epicatechin EC31 was a potent and nontoxic P-gp inhibitor. Its EC50 for reversing paclitaxel, doxorubicin, and vincristine resistance in three P-gp-overexpressing cell lines ranged from 37 to 249 nM. Mechanistic studies revealed that EC31 restored intracellular drug accumulation by inhibiting P-gp-mediated drug efflux. It did not downregulate the plasma membrane P-gp level nor inhibit P-gp ATPase. It was not a transport substrate of P-gp. A pharmacokinetic study revealed that the intraperitoneal administration of 30 mg/kg of EC31 could achieve a plasma concentration above its in vitro EC50 (94 nM) for more than 18 h. It did not affect the pharmacokinetic profile of coadministered paclitaxel. In the xenograft model of the P-gp-overexpressing LCC6MDR cell line, EC31 reversed P-gp-mediated paclitaxel resistance and inhibited tumor growth by 27.4 to 36.1% (p < 0.001). Moreover, it also increased the intratumor paclitaxel level in the LCC6MDR xenograft by 6 fold (p < 0.001). In both murine leukemia P388ADR and human leukemia K562/P-gp mice models, the cotreatment of EC31 and doxorubicin significantly prolonged the survival of the mice (p < 0.001 and p < 0.01) as compared to the doxorubicin alone group, respectively. Our results suggested that EC31 was a promising candidate for further investigation on combination therapy for treating P-gp-overexpressing cancers.

Fiber SPR biosensor sensitized by MOFs for MUC1 protein detection.

The concentration of tumor markers is low, which needs a highly sensitive, stable and fast detection method. In this paper, we proposed and demonstrated a U-shape fiber SPR biosensor sensitized by MOFs materials. The surface of the U-shape SPR sensor was modified with MOFs materials to enhance the sensitivity, and the nucleic acid aptamer was immobilized on the sensor surface because of the biocompatibility of MOFs materials. By the high specificity of the nucleic acid aptamer, the MUC1 protein was recognized and detected. The testing results indicate that the sensor has a logarithmic linear response in the MUC1 protein concentration detection range of 1 pg/ml-100 µg/ml, its sensitivity and detection limit are 5.33 nm/log(µg/ml) and 0.16 pg/ml respectively. After being sensitized by MOFs, the detection sensitivity of the sensor can be increased by 1.62 times，the LOD can be decreased by 0.75 times. The sensor has high sensitivity and specificity, which has broad application prospects in clinical detection of tumor markers.

Increased lower limb length ratio in patients with patellar instability.

PURPOSE: Patellar height is a risk factor for patellar instability, correlated with the tibia length/femur length (T/F) ratio. This study aimed to explore the changes in the T/F ratio in patients with patella instability and the potential correlation with the morphology of the patellofemoral joint and extensor moment arm. METHOD: A retrospective analysis was performed to assess the ratio of lower limb length morphological characteristics of the patellofemoral by full weight-bearing long-leg standing radiographs, magnetic resonance imaging, and computed tomography in 75 patients with patellar instability and 75 participants from a randomly selected control group from January 2020 to September 2021. A total of eight parts were measured, including mechanical tibia length/femur length (mT/F) ratio, anatomical tibia length/femur length (aT/F) ratio, hip-knee-ankle angle, femoral neck-shaft angle, femoral valgus cut angle, patellar height, Dejour classification, sulcus angle, trochlear angle, medial trochlear inclination, lateral trochlear inclination, patella tilt angle and patellar tendon moment arm to evaluate the difference of morphology between patient group and control groups. RESULTS: The mT/F (0.840 ± 0.031 vs. 0.812 ± 0.026, p < 0.001) and aT/F (0.841 ± 0.033 vs. 0.808 ± 0.028, p < 0.001) ratios in the patient group were significantly greater than that in the control group. There was a significant correlation between patellar height and increased mT/F and aT/F ratios (p < 0.05). CONCLUSION: Patients with patellar instability had a larger lower limb length ratio, and the change in lower limb length ratio was correlated with patellar height.

Revealing novel pyroptosis-related therapeutic targets for sepsis based on machine learning.

BACKGROUND: Sepsis is one of the most lethal diseases worldwide. Pyroptosis is a unique form of cell death, and the mechanism of interaction with sepsis is not yet clear. The aim of this study was to uncover pyroptosis genes associated with sepsis and to provide early therapeutic targets for the treatment of sepsis. METHODS: Based on the GSE134347 dataset, sepsis-related genes were mined by differential expression analysis and weighted gene coexpression network analysis (WGCNA). Subsequently, the sepsis-related genes were analysed for enrichment, and a protein‒protein interaction (PPI) network was constructed. We performed unsupervised consensus clustering of sepsis patients based on 33 pyroptosis-related genes (PRGs) provided by prior reviews. We finally obtained the PRGs mostly associated with sepsis by machine learning prediction models combined with prior reviews. The GSE32707 dataset served as an external validation dataset to validate the model and PRGs via receiver operating characteristic (ROC) curves. The NetworkAnalyst online tool was utilized to create a ceRNA network of lncRNAs and miRNAs around PRGs mostly associated with sepsis. RESULTS: A total of 170 genes associated with sepsis and 13 hub genes were acquired by WGCNA and PPI network analysis. The results of the enrichment analysis implied that these genes were mainly involved in the regulation of the inflammatory response and the positive regulation of bacterial and fungal defence responses. The prolactin signalling pathway and IL-17 signalling pathway were the primary enrichment pathways. Thirty-three PRGs can effectively classify septic patients into two subtypes, implying that there is a reciprocal relationship between sepsis and pyroptosis. Eventually, NLRC4 was considered the PRG most strongly associated with sepsis. The validation results of the prediction model and NLRC4 based on ROC curves were 0.74 and 0.67, respectively, both of which showed better predictive values. Meanwhile, the ceRNA network consisting of 6 lncRNAs and 2 miRNAs was constructed around NLRC4. CONCLUSION: NLRC4, as the PRG mostly associated with sepsis, could be considered a potential target for treatment. The 6 lncRNAs and 2 miRNAs centred on NLRC4 could serve as a further research direction to uncover the deeper pathogenesis of sepsis.

Three-dimensional micro displacement sensor based on fiber SPR mechanisms.

Three fiber micro displacement sensors can be combined to realize three-dimensional (3D) displacement sensing, but the system is complex. In this paper, a 3D displacement sensor based on fiber SPR was proposed, which was composed of displacement fiber and sensing fiber. By cascading the eccentric dual-core fiber and graded multimode fiber, the displacement fiber was realized. The V-groove was processed in the vertical and horizontal directions of the graded multimode fiber, and the inclined SPR sensing areas were fabricated to realize the sensing fiber. A straight beam from the middle core of the displacement fiber contacted the vertical V-groove inclined plane of the sensing fiber to realize the Y axis (up and down) direction micro displacement, contacted the horizontal V-groove inclined plane of the sensing fiber to realize the Z axis (front and back) direction micro displacement sensing. An oblique beam from the eccentric core of the displacement fiber cooperated with the sensing fiber to realize the micro displacement sensing in the X-axis (left and right) direction. The testing results indicate that the fiber SPR 3D micro displacement sensor can sense micro displacement in the X axis, Y axis and Z axis, and the wavelength sensitivity is 0.148 nm/µm, -3.724 nm/µm and 3.543 nm/µm, respectively. The light intensity sensitivity is -0.0014a.u./µm, -0.0458a.u./µm and -0.0494a.u./µm, respectively. When adjusting the parameters of eccentric dual-core fiber, the larger the core distance is, the greater the displacement sensitivity in the X-axis direction of the sensor is, and the smaller the detection range is. The proposed sensor can realize 3D micro displacement sensing by itself, which is expected to be used in the field of 3D micro displacement measurement and 3D space precision positioning.

Development of a 68Gallium-Labeled D-Peptide PET Tracer for Imaging Programmed Death-Ligand 1 Expression.

The development of immune checkpoint blockade therapy based on programmed cell death-protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) has revolutionized cancer therapies in recent years. However, only a fraction of patients responds to PD-1/PD-L1 inhibitors, owing to the heterogeneous expression of PD-L1 in tumor cells. This heterogeneity presents a challenge in the precise detection of tumor cells by the commonly used immunohistochemistry (IHC) approach. This situation calls for better methods to stratify patients who will benefit from immune checkpoint blockade therapy, to improve treatment efficacy. Positron emission tomography (PET) enables real-time visualization of the whole-body PD-L1 expression in a noninvasive way. Therefore, there is a need for the development of radiolabeled tracers to detect PD-L1 distribution in tumors through PET imaging. Compared to their L-counterparts, dextrorotary (D)-peptides have properties such as proteolytic resistance and remarkably prolonged metabolic half-lives. This study designed a new method to detect PD-L1 expression based on PET imaging of 68Ga-labeled PD-L1-targeted D-peptide, a D-dodecapeptide antagonist (DPA), in tumor-bearing mice. The results showed that the [68Ga]DPA can specifically bind to PD-L1-overexpressing tumors in vivo, and showed favorable stability as well as excellent imaging ability, suggesting that [68Ga]DPA-PET is a promising approach for the assessment of PD-L1 status in tumors.

Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning.

Objectives: DFU is a serious chronic disease with high disability and fatality rates, yet there is no completely effective therapy. While ferroptosis is integrated to inflammation and infection, its involvement in DFU is still unclear. The study aimed to identify ferroptosis-related genes in DFU, providing potential therapeutic targets. Methods: In the GEO database, two DFU microarray datasets (GSE147890 and GSE80178) were collected. WGCNA was conducted to identify the modular genes most involved in DFU. Subsequently, enrichment analysis and PPI analysis were performed. To yield the DFU-associated ferroposis genes, the ferroposis genes were retrieved from the FerrDb database and overlapped with the modular genes. Eventually, an optimal DFU prediction model was created by combining multiple machine learning algorithms (LASSO, SVM-RFE, Boruta, and XGBoost) to detect ferroposis genes most closely associated with DFU. The accuracy of the model was verified by utilizing external datasets (GSE7014) based on ROC curves. Results: WGCNA yielded seven modules in all, and 1223 DFU-related modular genes were identified. GO analysis revealed that inflammatory response, decidualization, and protein binding were the most highly enriched terms. These module genes were also enriched in the ErbB signaling, IL-17 signaling, MAPK signaling, growth hormone synthesis, secretion and action, and tight junction KEGG pathways. Twenty-five DFU-associated ferroposis genes were obtained by cross-linking with modular genes, which could distinguish DFU patients from controls. Ultimately, the prediction model based on machine learning algorithms was well established, with high AUC values (0.79 of LASSO, 0.80 of SVM, 0.75 of Boruta, 0.70 of XGBoost). MAFG and MAPK3 were identified by the prediction model as the most highly associated ferroposis-genes in DFU. Furthermore, the external dataset (GSE29221) validation revealed that MAPK3 (AUC = 0.81) had superior AUC values than MAFG (AUC = 0.62). Conclusion: As the most related ferroptosis-genes with DFU, MAFG and MAPK3 may be employed as potential therapeutic targets for DFU patients. Moreover, MAPK3, with higher accuracy, could be the more potential ferroptosis-related biomarker for further experimental validation.