Deletion of stimulator of interferons genes aggravated cardiac dysfunction in physiological aged mice.

BACKGROUND: Stimulator of interferons genes (STING) is crucial for innate immune response. It has been demonstrated that cGAS-STING pathway was the driver of aging-related inflammation. However, whether STING is involved in cardiac dysfunction during the physiological aging process remains unclear. METHODS: Gene expression profiles were obtained from the Gene Expression Omnibus database, followed by weighted gene co-expression network analysis, gene ontology analysis and protein network interaction analysis to identify key pathway and genes associated with aging. The effects of STING on cardiac function, glucose homeostasis, inflammation, and autophagy in physiological aging were investigated with STING knockout mice. RESULTS: Bioinformatics analysis revealed STING emerged as a hub gene of interest. Subsequent experiments demonstrated the activation of STING pathway in the heart of aged mice. Knockout of STING alleviated the inflammation in aged mice. However, Knockout of STING impaired glucose tolerance, inhibited autophagy, enhanced oxidative stress and aggravated cardiac dysfunction in aged mice. CONCLUSION: Although reducing inflammation, long-term STING inhibition by genetic ablation exacerbated cardiac dysfunction in aged mice. Given the multifaceted nature of aging and the diverse cellular functions of STING beyond immune regulation, the negative effects of targeting STING as a strategy to mitigate aging phenotype should be fully considered.

The preoperative scoring system combining neutrophil/lymphocyte ratio and CA19-9 predicts the long-term prognosis of intrahepatic cholangiocarcinoma patients undergoing curative liver resection.

BACKGROUND: This study aims to investigate preoperative prognostic factors available for intrahepatic cholangiocarcinoma (ICC) patients and propose a new preoperative prognostic scoring system for ICC that combines CA19-9 and neutrophil/lymphocyte ratio (NLR). METHODS: In this retrospective analysis, 1728 patients diagnosed with ICC and undergoing curative liver resections were studied. This study employed univariate and multivariate Cox regression to find factors affecting recurrence and overall survival (OS), and furthermore assessed how preoperative models influenced tumor traits and postoperative recurrence. RESULTS: The results of the multivariate Cox regression analysis indicated that two preoperative variables, NLR and Ca19-9, were independent risk factors affecting postoperative recurrence and OS in ICC patients. Based on this data, assigning a score of 0 (NLR ≤ 2.4 and Ca19-9 ≤ 37U/ml) or 1 (NLR > 2.4 and Ca19-9 > 37U/ml) to these two factors, a preoperative prognostic score was derived. According to the scoring model, patients were divided into three groups: 0 points (low-risk group), 1 point (intermediate-risk group), and 2 points (high-risk group). The 5-year recurrence and OS rates for the three groups were 56.6%, 68.2%, 77.8%, and 56.8%, 40.6%, 27.6%, respectively, with all P values < 0.001. Furthermore, high-risk group patients were more prone to early recurrence (early recurrence rates for high-, intermediate-, and low-risk groups were 56.8%, 51.5%, and 37.1%, respectively, P < 0.001) and extrahepatic metastasis (extrahepatic metastasis rates for high-, intermediate-, and low-risk groups were 31.7%, 26.4%, and 15.4%, respectively, P < 0.001). In terms of tumor characteristics, high-risk group patients had larger tumor diameters and were more likely to experience microvascular invasion, lymph node metastasis, and perineural invasion. CONCLUSIONS: The predictive capacity of postoperative recurrence and OS rates in ICC patients is effectively captured by the preoperative scoring system incorporating NLR and CA19-9 levels.

ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images.

Ovarian cancer, predominantly epithelial ovarian cancer (EOC), is a global health concern due to its high mortality rate. Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and disease. Bevacizumab is a humanized monoclonal antibody, which blocks VEGF signaling in cancer, inhibits angiogenesis and causes tumor shrinkage, and has been recently approved by the FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Unfortunately, Bevacizumab may also induce harmful adverse effects, such as hypertension, bleeding, arterial thromboembolism, poor wound healing and gastrointestinal perforation. Given the expensive cost and unwanted toxicities, there is an urgent need for predictive methods to identify who could benefit from bevacizumab. Of the 18 (approved) requests from 5 countries, 6 teams using 284 whole section WSIs for training to develop fully automated systems submitted their predictions on a test set of 180 tissue core images, with the corresponding ground truth labels kept private. This paper summarizes the 5 qualified methods successfully submitted to the international challenge of automated prediction of treatment effectiveness in ovarian cancer using the histopathologic images (ATEC23) held at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023 and evaluates the methods in comparison with 5 state of the art deep learning approaches. This study further assesses the effectiveness of the presented prediction models as indicators for patient selection utilizing both Cox proportional hazards analysis and Kaplan-Meier survival analysis. A robust and cost-effective deep learning pipeline for digital histopathology tasks has become a necessity within the context of the medical community. This challenge highlights the limitations of current MIL methods, particularly within the context of prognosis-based classification tasks, and the importance of DCNNs like inception that has nonlinear convolutional modules at various resolutions to facilitate processing the data in multiple resolutions, which is a key feature required for pathology related prediction tasks. This further suggests the use of feature reuse at various scales to improve models for future research directions. In particular, this paper releases the labels of the testing set and provides applications for future research directions in precision oncology to predict ovarian cancer treatment effectiveness and facilitate patient selection via histopathological images.

Rapid and visual detection of Mycoplasma genitalium using recombinase polymerase amplification combined with lateral flow strips.

Mycoplasma genitalium (MG) is an important sexually transmitted pathogen that can cause urethritis in males and pelvic inflammatory disease in females. Due to its complex growth requirements and lengthy incubation times, culturing MG in clinical laboratories is impractical. Here we describe a rapid and visual assay combining recombinase polymerase amplification (RPA) with lateral flow (LF) strips to detect MG (MG-RPA-LF). The limit of detection (LoD) of this method was 33.6 genome equivalents (GE) per reaction, using a dilution series of purified genomic DNA. Clinical performance was evaluated by testing 100 urogenital swabs. Compared to the Simultaneous Amplification and Testing assay, our MG-RPA-LF assay showed a sensitivity of 94 % (95 % CI, 82 %-98 %) and a specificity of 100 % (95 % CI, 91 %-100 %). The overall concordance between the two methods was 97 % (95 % CI, 91 %-99 %) with a κ coefficient of 0.94 (P < 0.001). Without cumbersome and expensive instruments, this method is anticipated to be a promising alternative to diagnose MG infection, especially in resource-poor settings.

Development of Novel Fluorescent Probes for Specific Detection of Hypochlorous Acid.

Hypochlorous acid (HClO) is widely used in everyday life for bleaching and disinfecting tap water, and also in human metabolism, where it plays an important role in destroying foreign bacterial invaders and pathogens as well as immune defense and cellular functioning maintenance. Abnormal levels of hypochlorous acid have the potential to cause joint inflammation, neuronal degeneration, and even life-threatening cancer. Specific identification and effective detection of hypochlorous acid are important for monitoring human health and the environment. In recent years, organic fluorescent probes have attracted much attention because of their simple synthesis, easy operation, high sensitivity, and high specificity, and a variety of hypochlorous acid fluorescent probes based on low-cost, easy-to-operate, and rapid identification have been developed. In this paper, we review the fluorescent probes that have been developed in the past five years for the specific recognition of hypochlorous acid based on different fluorophores, such as triphenylamine, coumarin, 1,8-naphthalize, etc., as well as recognition units, such as N-N dimethyl thiosemicarbazone, and describe how the probes and hypochlorous acid interact for identification in the same manner as other fluorescent probes. In addition, the reaction mechanism between the probe and hypochlorous acid, the fluorescence change of the probe, and the detection limit are described to illustrate the progress in the detection of hypochlorous acid in recent years and to provide ideas for the development of hypochlorous acid fluorescent probes in the future.

Structural identification, rheological properties and immunological receptor of a complex galacturonoglucan from fruits of Schisandra chinensis (Turcz.) Baill.

A complex heteropolysaccharide SCP-2 named schisanan B (Mw = 1.005 × 105 g/mol) was obtained from water extracts of Schisandra chinensis fruits, and its planar structure was finally deduced as a galacturonoglucan by a combination of monosaccharide compositions, methylation analysis, partial acid hydrolysis, enzymatic hydrolysis and 1D/2D-nuclear magnetic resonance spectroscopy. The conformation of SCP-2 exhibited a globular shape with branching in ammonium formate aqueous solutions. The rheological properties of SCP-2 were investigated on concentrations, temperature, pH and salts. The in vitro immunomodulatory activity assay demonstrated that SCP-2 significantly enhanced the production of nitric oxide (NO) and stimulated the secretion of tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) in macrophages. Through a combination of high-resolution live-cell imaging, surface plasmon resonance, and molecular docking techniques, SCP-2 exhibited a strong binding affinity with the Toll-like receptor 4 (TLR4). Moreover, western blot analysis revealed that SCP-2 effectively induced downstream signaling proteins associated with TLR4 activation, thereby promoting macrophage activation. The evidence strongly indicates that TLR4 functions as a membrane protein target in the activation of macrophages and immune regulation induced by SCP-2.

Preparation and characterization of hierarchically porous hybrid gels for efficient dye adsorption.

Water treatment faces significant challenges due to the increasing complexity of pollutants and the need for more efficient, sustainable treatment methods. However, current adsorbent materials often struggle with issues such as low adsorption capacity, slow kinetics, and poor reusability, limiting their practical application. In this study, we developed a novel hierarchical porous hybrid gel (HPHG) for water treatment to address the limitations of conventional adsorbents. The HPHG features a multi-level porous structure (from 48 ± 28 nm to 4385 ± 823 nm) that significantly enhances its porosity and specific surface area. We systematically investigated the relationship between the material's structure and its adsorption performance. Kinetic studies revealed a tendency towards a pseudo-second-order adsorption model, attributed to the material's unique structural features that facilitate rapid mass exchange channels inside HPHG and provide abundant active sites for pollutant adsorption. Reusability tests demonstrated that the material retained 85.4% of its initial adsorption capacity after five adsorption-desorption cycles, highlighting its potential for practical applications. This study provides valuable insights into structure-performance relationships in advanced water treatment materials, offering a promising approach for designing next-generation adsorbents with superior efficiency and sustainability.

Pulsed electromagnetic fields potentiate bone marrow mesenchymal stem cell chondrogenesis by regulating the Wnt/ß-catenin signaling pathway.

BACKGROUND: Pulsed electromagnetic fields (PEMFs) show promise as a treatment for knee osteoarthritis (KOA) by reducing inflammation and promoting chondrogenic differentiation of bone marrow-derived mesenchymal stem cells (BMSCs). PURPOSE: To identify the efficacy window of PEMFs to induce BMSCs chondrogenic differentiation and explore the cellular mechanism under chondrogenesis of BMSCs in regular and inflammatory microenvironments. METHODS: BMSCs were exposed to PEMFs (75 Hz, 1.6/2/3/3.8 mT) for 7 and 14 days. The histology, proliferation, migration and chondrogenesis of BMSCs were assessed to identify the optimal parameters. Using these optimal parameters, transcriptome analysis was performed to identify target genes and signaling pathways, validated through immunohistochemical assays, western blotting, and qRT-PCR, with or without the presence of IL-1ß. The therapeutic effects of PEMFs and the effective cellular signaling pathways were evaluated in vivo. RESULTS: BMSCs treated with 3 mT PEMFs showed the optimal chondrogenesis on day 7, indicated by increased expression of ACAN, COL2A, and SOX9, and decreased levels of MMP3 and MMP13 at both transcriptional and protein levels. The advantages of 3 mT PEMFs diminished in the 14-day culture groups. Transcriptome analysis identified sFRP3 as a key molecule targeted by PEMF treatment, which competitively inhibited Wnt/ß-catenin signaling, regardless of IL-1ß presence or duration of exposure. This inhibition of the Wnt/ß-catenin pathway was also confirmed in a KOA mouse model following PEMF exposure. CONCLUSIONS: PEMFs at 75 Hz and 3 mT are optimal in inducing early-stage chondrogenic differentiation of BMSCs. The induction and chondroprotective effects of PEMFs are mediated by sFRP3 and Wnt/ß-catenin signaling, irrespective of inflammatory conditions.

Rethinking masked image modelling for medical image representation.

Masked Image Modelling (MIM), a form of self-supervised learning, has garnered significant success in computer vision by improving image representations using unannotated data. Traditional MIMs typically employ a strategy of random sampling across the image. However, this random masking technique may not be ideally suited for medical imaging, which possesses distinct characteristics divergent from natural images. In medical imaging, particularly in pathology, disease-related features are often exceedingly sparse and localized, while the remaining regions appear normal and undifferentiated. Additionally, medical images frequently accompany reports, directly pinpointing pathological changes' location. Inspired by this, we propose Masked medical Image Modelling (MedIM), a novel approach, to our knowledge, the first research that employs radiological reports to guide the masking and restore the informative areas of images, encouraging the network to explore the stronger semantic representations from medical images. We introduce two mutual comprehensive masking strategies, knowledge-driven masking (KDM), and sentence-driven masking (SDM). KDM uses Medical Subject Headings (MeSH) words unique to radiology reports to identify symptom clues mapped to MeSH words (e.g., cardiac, edema, vascular, pulmonary) and guide the mask generation. Recognizing that radiological reports often comprise several sentences detailing varied findings, SDM integrates sentence-level information to identify key regions for masking. MedIM reconstructs images informed by this masking from the KDM and SDM modules, promoting a comprehensive and enriched medical image representation. Our extensive experiments on seven downstream tasks covering multi-label/class image classification, pneumothorax segmentation, and medical image-report analysis, demonstrate that MedIM with report-guided masking achieves competitive performance. Our method substantially outperforms ImageNet pre-training, MIM-based pre-training, and medical image-report pre-training counterparts. Codes are available at https://github.com/YtongXie/MedIM.

Identification of SPP1+ macrophages in promoting cancer stemness via vitronectin and CCL15 signals crosstalk in liver cancer.

Macrophages play a multifaceted role in cancer biology, with both pro-tumorigenic and anti-tumorigenic functions. Understanding the mechanisms underlying macrophage involvement in cancer progression is essential for the development of therapeutic strategies. Our study analyzed single-cell RNA sequencing data from 12 patients with liver cancer and identified a subpopulation of macrophages characterized by elevated expression of SPP1, which correlates with poor prognosis in liver cancer patients. These SPP1+ macrophages induce upregulation of tumor stemness through a vitronectin (VTN)-dependent paracrine mechanism. Mechanistically, VTN derived from SPP1+ macrophages promote integrin αvß5/adenosine 5'-monophosphate-activated protein kinase (AMPK)/Yes-associated protein 1 (YAP1)/SYR-box transcription factor 4 (SOX4) signaling, mediating liver tumor stemness and progression. Conversely, CCL15 produced by liver cancer cells drives polarization of M0 macrophages toward an SPP1+ macrophage phenotype, establishing a positive feedback loop of macrophage-tumor stemness. Furthermore, the presence of SPP1+ macrophages confers chemoresistance in liver cancer, and inhibition of the macrophage-tumor feedback loop through targeting integrin αvß5/YAP1 signaling sensitizes liver cancer cells to chemotherapy. Our study highlights the crucial role of SPP1+ macrophages in liver cancer progression, providing novel insights for clinical liver cancer therapy.

Consistency-guided Differential Decoding for Enhancing Semi-supervised Medical Image Segmentation.

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data, especially on volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or developing consistency regularization schemes, our empirical findings suggest that differential decoder features emerge naturally when two decoders strive to generate consistent predictions. Based on the observation, we first analyze the treasure of discrepancy in learning towards consistency, under both pseudo-labeling and consistency regularization settings, and subsequently propose a novel SSL method called LeFeD, which learns the feature-level discrepancies obtained from two decoders, by feeding such information as feedback signals to the encoder. The core design of LeFeD is to enlarge the discrepancies by training differential decoders, and then learn from the differential features iteratively. We evaluate LeFeD against eight state-of-the-art (SOTA) methods on three public datasets. Experiments show LeFeD surpasses competitors without any bells and whistles, such as uncertainty estimation and strong constraints, as well as setting a new state of the art for semi-supervised medical image segmentation. Code has been released at https://github.com/maxwell0027/LeFeD.

Nanomaterial-integrated injectable hydrogels for craniofacial bone reconstruction.

The complex anatomy and biology of craniofacial bones pose difficulties in their effective and precise reconstruction. Injectable hydrogels (IHs) with water-swollen networks are emerging as a shape-adaptive alternative for noninvasively rebuilding craniofacial bones. The advent of versatile nanomaterials (NMs) customizes IHs with strengthened mechanical properties and therapeutically favorable performance, presenting excellent contenders over traditional substitutes. Structurally, NM-reinforced IHs are energy dissipative and covalently crosslinked, providing the mechanics necessary to support craniofacial structures and physiological functions. Biofunctionally, incorporating unique NMs into IH expands a plethora of biological activities, including immunomodulatory, osteogenic, angiogenic, and antibacterial effects, further favoring controllable dynamic tissue regeneration. Mechanistically, NM-engineered IHs optimize the physical traits to direct cell responses, regulate intracellular signaling pathways, and control the release of biomolecules, collectively bestowing structure-induced features and multifunctionality. By encompassing state-of-the-art advances in NM-integrated IHs, this review offers a foundation for future clinical translation of craniofacial bone reconstruction.

Reversible ON- and OFF-switch receptors Clec4G and Rab1A reveal the hormetic effects of a pectin polysaccharide in Aralia elata (Miq.) Seem.

BACKGROUND: Numerous studies indicate that natural polysaccharides have immune-enhancing effects as a host defense potentiator. Few reports are available on hormetic effects of natural polysaccharides, and the underlying mechanisms remain unclear. PURPOSE: AELP-B6 (arabinose- and galactose-rich pectin polysaccharide) from Aralia elata (Miq.) Seem was taken as a case study to clarify the potential mechanism of hormetic effects of natural polysaccharides. METHODS: The pharmacodynamic effect of AELP-B6 was verified by constructing the CTX-immunosuppressive mouse model. The hormetic effects were explored by TMT-labeled proteomics, energy metabolism analysis, flow cytometry and western blot. The core-affinity target of AELP-B6 was determined by pull down, nanoLC-nanoESI+-MS, CETSA, immunoblot and SPR assay. The RAW264.7Clec4G-RFP and RAW264.7Rab1A-RFP cell lines were simultaneously constructed to determine the affinity difference between AELP-B6 and targets by confocal laser scanning live-cell imaging. Antibody blocking assays were further used to verify the mechanism of hormetic effects. RESULTS: AELP-B6 at low and medium doses may maintain the structural integrity of thymus and spleen, increase the concentrations of TNF-α, IFN-γ, IL-3 and IL-8, and alleviate CTX-induced reduction of immune cell viability in vivo. Proteomics and energy metabolism analysis revealed that AELP-B6 regulate HIF-1α-mediated metabolic programming, causing Warburg effects in macrophages. AELP-B6 at low and medium doses promoted the release of intracellular immune factors, and driving M1-like polarization of macrophages. As a contrast, AELP-B6 at high dose enhanced the expression levels of apoptosis related proteins, indicating activation of the intrinsic apoptotic cascade. Two highly expressed transmembrane proteins in macrophages, Clec4G and Rab1A, were identified as the primary binding targets of AELP-B6 which co-localized with the cell membrane and directly impacted with immune cell activation and apoptosis. AELP-B6 exhibits affinity differences with Clec4G and Rab1A, which is the key to the hormetic effects. CONCLUSION: We observed hormesis of natural polysaccharide (AELP-B6) for the first time, and AELP-B6 mediates the hormetic effects through two dose-related targets. Low dose of AELP-B6 targets Clec4G, thereby driving the M1-like polarization via regulating NF-κB signaling pathway and HIF-1α-mediated metabolic programming, whereas high dose of AELP-B6 targets Rab1A, leading to mitochondria-dependent apoptosis.

Structural elucidation a complex galactosyl and glucosyl-rich pectin from the pericarp of immature fruits of Juglans mandshurica Maxim.

A glucosyl-rich pectin, JMMP-3 (Mw, 2.572 × 104 g/mol, O-methyl % = 3.62%), was isolated and purified from the pericarp of the immature fruit of Juglans mandshurica Maxim. (QingLongYi). The structure of JMMP-3 was studied systematically by infrared spectroscopy, monosaccharide compositions, methylation analysis, partial acid hydrolysis, and 1/2D-NMR. The backbone of JMMP-3 possessed a smooth region (→ 4GalA1 →) and a hairy region (→ 4GalA1 → 2Rha1 →) with a molar ratio of 2: 5. The substitution of four characteristic side chains (R1-R4) occurs at C-4 of → 2,4)-α-Rhap-(1→, where R1 is composed of → 5)-α-Araf-(1→, R2 is composed of → 4)-ß-Galp-(1 → and ß-Galp-(1→, R3 is composed of α-Glcp-(1→, →4)-α-Glcp-(1 → and → 4,6)-α-Glcp-(1→, and R4 is composed of → 5)-α-Araf-(1→, ß-Galp-(1→, → 4)-ß-Galp-(1→, → 3,4)-ß-Galp-(1→, → 4,6)-ß-Galp-(1 → and → 2,4)-ß-Galp-(1 → . In addition, the antitumor activity of JMMP-3 on HepG2 cells was preliminarily investigated.

Single cell analyses reveal the PD-1 blockade response-related immune features in hepatocellular carcinoma.

Background: Immunotherapy has demonstrated its potential to improve the prognosis of patients with hepatocellular carcinoma (HCC); however, patients' responses to immunotherapy vary a lot. A comparative analysis of the tumor microenvironment (TME) in responders and non-responders is expected to unveil the mechanisms responsible for the immunotherapy resistance and provide potential treatment targets. Methods: We performed sequencing analyses using 10x Genomics technology on six HCC patients who responded to anti-PD-1 therapy and one HCC patient who did not respond. Additionally, we obtained single cell data from untreated, responsive, and nonresponsive HCC patients from public databases, and used part of the datasets as a validation cohort. These data were integrated using algorithms such as Harmony. An independent validation cohort was established. Furthermore, we performed spatial transcriptomic sequencing on the tumor adjacent tissues of three HCC responsive patients using 10x Genomics spatial transcriptomic technology. Additionally, we analyzed data about three HCC patients obtained from public databases. Finally, we validated our conclusions using immunofluorescence, flow cytometry, and in vivo experiments. Results: Our findings confirmed the presence of "immune barrier" partially accounting for the limited efficacy of immunotherapy. Our analysis revealed a significant increase in TREM2+ Macrophages among non-responsive patients expressing multiple immunosuppressive signals. anti-Csf1r monoclonal antibodies effectively eliminated these macrophages and augmented the therapeutic effects of anti-PD-1 therapy. TCR+ Macrophages possessed direct tumor-killing capabilities. IL1B+ cDC2 was the primary functional subtype of cDC2 cells. Absence of THEMIShi CD8+ T subtypes might diminish immunotherapeutic effects. Furthermore, CD8+ T cells entered a state of stress after anti-PD-1 treatment, which might be associated with CD8+ T cell exhaustion and senescence. Conclusions: The profiles of immune TMEs showed differences in HCC patients responsive, non-responsive and untreated. These differences might explain the discounted efficacy of immunotherapy in some HCC patients. The cells and molecules, which we found to carry unique capabilities, may be targeted to enhance immunotherapeutic outcomes in patients with HCC.

Novel genetic alterations in liver cancer distinguish distinct clinical outcomes and combination immunotherapy responses.

Introduction: Genomic profiling has revolutionized therapeutic interventions and the clinical management of liver cancer. However, pathogenetic mechanisms, molecular determinants of recurrence, and predictive biomarkers for first-line treatment (anti-PD-(L)1 plus bevacizumab) in liver cancer remain incompletely understood. Materials and methods: Targeted next-generation sequencing (tNGS) (a 603-cancer-gene panel) was applied for the genomic profiling of 232 hepatocellular carcinoma (HCC) and 22 intrahepatic cholangiocarcinoma (ICC) patients, among which 47 unresectable/metastatic HCC patients underwent anti-PD-1 plus bevacizumab therapy. Genomic alterations were estimated for their association with vascular invasion (VI), location of onset, recurrence, overall survival (OS), recurrence-free survival (RFS), and anti-PD-1 plus bevacizumab therapy response. Results: The genomic landscape exhibited that the most commonly altered genes in HCC were TP53, FAT3, PDE4DIP, KMT2C, FAT1, and MYO18A, while TP53, FAT1, FAT3, PDE4DIP, ROS1, and GALNT11 were frequently altered in ICC; notably, KRAS (18.18% vs. 1.29%) and BAP1 (13.64% vs. 1.29%) alterations were significantly more prevalent in ICC. Comparison analysis demonstrated the distinct clinicopathological/genomic characterizations between Chinese and Western HCC cohorts. Genomic profiling of HCC underlying VI showed that LDLR, MSH2, KDM5D, PDE3A, and FOXO1 were frequently altered in the VI group compared to patients without VIs. Compared to the right hepatic lobes of HCC patients, the left hepatic lobe of HCC patients had superior OS (median OS: 36.77 months vs. unreached, p < 0.05). By further comparison, Notch signaling pathway-related alterations were significantly prevalent among the right hepatic lobes of HCC patients. Of note, multivariate Cox regression analysis showed that altered RB1, NOTCH3, MGA, SYNE1, and ZFHX3, as independent prognostic factors, were significantly correlated with the OS of HCC patients. Furthermore, altered LATS1 was abundantly enriched in the HCC-recurrent group, and impressively, it was independent of clinicopathological features in predicting RFS (median RFS of altered type vs. wild-type: 5.57 months vs. 22.47 months, p < 0.01). Regarding those treated HCC patients, TMB value, altered PTPRZ1, and cell cycle-related alterations were identified to be positively associated with the objective response rate (ORR), but KMT2D alterations were negatively correlated with ORR. In addition, altered KMT2D and cell cycle signaling were significantly associated with reduced and increased time to progression-free survival (PFS), respectively. Conclusion: Comprehensive genomic profiling deciphered distinct molecular characterizations underlying VI, location of onset, recurrence, and survival time in liver cancer. The identification of novel genetic predictors of response to anti-PD-1 plus bevacizumab in HCC facilitated the development of an evidence-based approach to therapy.

Small-size temperature/high-pressure integrated sensor via flip-chip method.

Hydraulic technology with smaller sizes and higher reliability trends, including fault prediction and intelligent control, requires high-performance temperature and pressure-integrated sensors. Current designs rely on planar wafer- or chip-level integration, which is limited by pressure range, chip size, and low reliability. We propose a small-size temperature/high-pressure integrated sensor via the flip-chip technique. The pressure and temperature units are arranged vertically, and the sensing signals of the two units are integrated into one plane through silicon vias and gold-gold bonding, reducing the lateral size and improving the efficiency of signal transmission. The flip-chip technique ensures a reliable electrical connection. A square diaphragm with rounded corners is designed and optimised with simulation to sense high pressure based on the piezoresistive effect. The temperature sensing unit with a thin-film platinum resistor measures temperature and provides back-end high-precision compensation, which will improve the precision of the pressure unit. The integrated chip is fabricated by MEMS technology and packaged to fabricate the extremely small integrated sensor. The integrated sensor is characterised, and the pressure sensor exhibits a sensitivity and sensitivity drift of 7.97 mV/MPa and -0.19% FS in the range of 0-20 MPa and -40 to 120 °C. The linearity, hysteresis, repeatability, accuracy, basic error, and zero-time drift are 0.16% FS, 0.04% FS, 0.06% FS, 0.18% FS, ±0.23% FS and 0.04% FS, respectively. The measurement error of the temperature sensor and temperature coefficient of resistance is less than ±1 °C and 3142.997 ppm/°C, respectively. The integrated sensor has broad applicability in fault diagnosis and safety monitoring of high-end equipment such as automobile detection, industrial equipment, and oil drilling platforms.

CADS: A Self-supervised Learner via Cross-modal Alignment and Deep Self-distillation for CT Volume Segmentation.

Self-supervised learning (SSL) has long had great success in advancing the field of annotation-efficient learning. However, when applied to CT volume segmentation, most SSL methods suffer from two limitations, including rarely using the information acquired by different imaging modalities and providing supervision only to the bottleneck encoder layer. To address both limitations, we design a pretext task to align the information in each 3D CT volume and the corresponding 2D generated X-ray image and extend self-distillation to deep self-distillation. Thus, we propose a self-supervised learner based on Cross-modal Alignment and Deep Self-distillation (CADS) to improve the encoder's ability to characterize CT volumes. The cross-modal alignment is a more challenging pretext task that forces the encoder to learn better image representation ability. Deep self-distillation provides supervision to not only the bottleneck layer but also shallow layers, thus boosting the abilities of both. Comparative experiments show that, during pre-training, our CADS has lower computational complexity and GPU memory cost than competing SSL methods. Based on the pre-trained encoder, we construct PVT-UNet for 3D CT volume segmentation. Our results on seven downstream tasks indicate that PVT-UNet outperforms state-of-the-art SSL methods like MOCOv3 and DiRA, as well as prevalent medical image segmentation methods like nnUNet and CoTr. Code and pre-trained weight will be available at https://github.com/yeerwen/CADS.

UniMiSS+: Universal Medical Self-Supervised Learning From Cross-Dimensional Unpaired Data.

Self-supervised learning (SSL) opens up huge opportunities for medical image analysis that is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D medical images like computerized tomography (CT) remains challenging due to its high imaging cost and privacy restrictions. In our pilot study, we advocated bringing a wealth of 2D images like chest X-rays as compensation for the lack of 3D data, aiming to build a universal medical self-supervised representation learning framework, called UniMiSS. Especially, we designed a pyramid U- like medical Transformer (MiT) as the backbone to make UniMiSS possible to perform SSL with both 2D and 3D images. Consequently, the predecessor UniMiSS has two obvious merits compared to current 3D-specific SSL: (1) more effective - superior to learning strong representations, benefiting from more and diverse data; and (2) more versatile - suitable for various downstream tasks without the restriction on the dimensionality barrier. Unfortunately, UniMiSS did not dig deeply into the intrinsic anatomy correlation between 2D medical images and 3D volumes due to the lack of paired multi-modal/dimension patient data. In this extension paper, we propose the UniMiSS+, in which we introduce the digitally reconstructed radiographs (DRR) technology to simulate X-ray images from a CT volume to access paired CT and X-ray data. Benefiting from the paired group, we introduce an extra pair- wise constraint to boost the cross-modality correlation learning, which also can be adopted as a cross-dimension regularization to further improve the representations. We conduct expensive experiments on multiple 3D/2D medical image analysis tasks, including segmentation and classification. The results show that the proposed UniMiSS+ achieves promising performance on various downstream tasks, not only outperforming the ImageNet pre-training and other advanced SSL counterparts substantially but also improving the predecessor UniMiSS pre-training. Code is available at: https://github.com/YtongXie/UniMiSS-code.

A Reverse Thinking Based on Partially Methylated Aldononitrile Acetates to Analyze Glycoside Linkages of Polysaccharides Using Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry.

Glycoside linkage analyses of medicine and food homologous plant polysaccharides have always been a key point and a difficulty of structural characterization. The gas chromatography-mass spectrometry (GC-MS) method is one of the commonly used traditional techniques to determine glycoside linkages via partially methylated alditol acetates and aldononitrile acetates (PMAAs and PMANs). Due to the simplicity of derivatization and the highly structural asymmetry of PMANs, reverse thinking is proposed using liquid chromatography-electrospray ionization-multiple reaction monitoring mass spectrometry (LC-ESI-MRM-MS) for the first time to directly determine the neutral and acidic glycosyl linkages of polysaccharides. The complete characterization of glycoside linkages deduced from PMANs was achieved using a combination of tR values, characteristic MRM ion pairs, diagnostic ESI+-MS/MS fragmentation ions (DFIs), and optimal collision energy (OCE). The DFI and OCE parameters were confirmed to be effective for the auxiliary discrimination of some isomers of the PMANs. The practicality of LC-ESI+-MRM-MS was further verified by analyzing the glycoside linkages of polysaccharides in five medicine and food homologous plants. This method can serve as an alternative to GC-MS for the simultaneous determination of neutral and acidic glycosyl linkages in polysaccharides.