Alterations of mannosylated glycopatterns recognized by Hippeastrum hybrid lectin in saliva of patients with lung cancer.

OBJECTIVES: Lung cancer (LC) is the malignant tumor with the highest mortality rate worldwide, and precise early diagnosis can improve patient prognosis. The purpose of this study was to investigate whether alterations in the glycopatterns recognized by the Hippeastrum hybrid lectin (HHL) in salivary proteins are associated with the development of LC. MATERIALS AND METHODS: First, we collected saliva samples from LC (15 lung adenocarcinoma (ADC); 15 squamous cell carcinoma (SCC); 15 small cell lung cancer (SCLC)) and 15 benign pulmonary disease (BPD) for high-throughput detection of abundance levels of HHL-recognized glycopatterns using protein microarrays, and then validated the pooled samples from each group with lectin blotting analysis. Finally, the N-glycan profiles of salivary glycoproteins isolated from the pooled samples using HHL-magnetic particle conjugates were characterized separately using MALDI-TOF/TOF-MS. RESULTS: The results showed that the abundance level of glycopatterns recognized by HHL in salivary proteins was elevated in LC compared to BPD. The proportion of mannosylated N-glycans was notably higher in ADC (31.7%), SCC (39.0%), and SCLC (46.6%) compared to BPD (23.3%). CONCLUSIONS: The altered salivary glycopatterns such as oligomannose, Manα1-3Man, or Manα1-6Man N-glycans recognized by HHL might serve as potential biomarkers for the diagnosis of LC patients. CLINICAL RELEVANCE: This study provides crucial information for studying changes in salivary to differentiate between BPD and LC and facilitate the discovery of biomarkers for LC diagnosis based on precise alterations of mannosylated N-glycans in saliva.

DMGM: deformable-mechanism based cervical cancer staging via MRI multi-sequence.

Objective.This study aims to leverage a deep learning approach, specifically a deformable convolutional layer, for staging cervical cancer using multi-sequence MRI images. This is in response to the challenges doctors face in simultaneously identifying multiple sequences, a task that computer-aided diagnosis systems can potentially improve due to their vast information storage capabilities.Approach.To address the challenge of limited sample sizes, we introduce a sequence enhancement strategy to diversify samples and mitigate overfitting. We propose a novel deformable ConvLSTM module that integrates a deformable mechanism with ConvLSTM, enabling the model to adapt to data with varying structures. Furthermore, we introduce the deformable multi-sequence guidance model (DMGM) as an auxiliary diagnostic tool for cervical cancer staging.Main results.Through extensive testing, including comparative and ablation studies, we validate the effectiveness of the deformable ConvLSTM module and the DMGM. Our findings highlight the model's ability to adapt to the deformation mechanism and address the challenges in cervical cancer tumor staging, thereby overcoming the overfitting issue and ensuring the synchronization of asynchronous scan sequences. The research also utilized the multi-modal data from BraTS 2019 as an external test dataset to validate the effectiveness of the proposed methodology presented in this study.Significance.The DMGM represents the first deep learning model to analyze multiple MRI sequences for cervical cancer, demonstrating strong generalization capabilities and effective staging in small dataset scenarios. This has significant implications for both deep learning applications and medical diagnostics. The source code will be made available subsequently.

Pharmacological expansion of type 2 alveolar epithelial cells promotes regenerative lower airway repair.

Type 2 alveolar epithelial cells (AEC2s) are stem cells in the adult lung that contribute to lower airway repair. Agents that promote the selective expansion of these cells might stimulate regeneration of the compromised alveolar epithelium, an etiology-defining event in several pulmonary diseases. From a high-content imaging screen of the drug repurposing library ReFRAME, we identified that dipeptidyl peptidase 4 (DPP4) inhibitors, widely used type 2 diabetes medications, selectively expand AEC2s and are broadly efficacious in several mouse models of lung damage. Mechanism of action studies revealed that the protease DPP4, in addition to processing incretin hormones, degrades IGF-1 and IL-6, essential regulators of AEC2 expansion whose levels are increased in the luminal compartment of the lung in response to drug treatment. To selectively target DPP4 in the lung with sufficient drug exposure, we developed NZ-97, a locally delivered, lung persistent DPP4 inhibitor that broadly promotes efficacy in mouse lung damage models with minimal peripheral exposure and good tolerability. This work reveals DPP4 as a central regulator of AEC2 expansion and affords a promising therapeutic approach to broadly stimulate regenerative repair in pulmonary disease.

DARS-AS1: A Vital Oncogenic LncRNA Regulator with Potential for Cancer Prognosis and Therapy.

DARS-AS1, short for Aspartyl-tRNA synthetase antisense RNA 1, has emerged as a pivotal player in cancers. Upregulation of this lncRNA is a recurrent phenomenon observed across various cancer types, where it predominantly assumes oncogenic roles, exerting influence on multiple facets of tumor cell biology. This aberrant expression of DARS-AS1 has triggered extensive research investigations, aiming to unravel its roles and clinical values in cancer. In this review, we elucidate the significant correlation between dysregulated DARS-AS1 expression and adverse survival prognoses in cancer patients, drawing from existing literature and pan-cancer analyses from The Cancer Genome Atlas (TCGA). Additionally, we provide comprehensive insights into the diverse functions of DARS-AS1 in various cancers. Our review encompasses the elucidation of the molecular mechanisms, ceRNA networks, functional mediators, and signaling pathways, as well as its involvement in therapy resistance, coupled with the latest advancements in DARS-AS1-related cancer research. These recent updates enrich our comprehensive comprehension of the pivotal role played by DARS-AS1 in cancer, thereby paving the way for future applications of DARS-AS1-targeted strategies in tumor prognosis evaluation and therapeutic interventions. This review furnishes valuable insights to advance the ongoing efforts in combating cancer effectively.

Predictive Factor of Tumor Aggressiveness in Patients with Extrahepatic Cholangiocarcinoma Based on Diffusion-weighted MRI.

BACKGROUND: Extrahepatic cholangiocarcinoma (EHCC), an exceedingly malignant neoplasm, often eludes early detection, culminating in a dire prognosis. Accurate cancer staging systems and pathological differentiation are designed to guide adjuvant interventions and predict postoperative prognoses. OBJECTIVE: This study sought to investigate the predictive capacity of DW-MRI in discerning T stages, lymph node metastasis, and pathological differentiation grades in patients with EHCC. METHODS: Eighty-five patients were pathologically diagnosed with EHCC and underwent abdominal MRI within two weeks before surgery at our hospital from Aug 2011 to Aug 2021. Tumor axial maximum area (AMA) and apparent diffusion coefficient (ADC) values for diverse T stages, N stages, and differentiation grades were retrospectively analyzed. RESULTS: The Mann-Whitney U test displayed significantly higher lesion AMA values (P =0.006) and lower tumor ADC values (P = 0.001) in the nodepositive group (median ADC and AMA value: 1.220×10-3 mm2/s, 82.231 mm2) than in the node-negative group (median ADC and AMA value: 1.316×10-3 mm2/s, 51.174 mm2). A tumor ADC value<1.249×10-3 mm2/s from the receiver operating characteristic curve (AUC=0.725, P=0.001) exhibited the capability to predict node-positive EHCC with a sensitivity of 64.29%, and specificity of 73.68%. Furthermore, a progressive decrease in the degree of EHCC differentiation was associated with a reduction in the tumor ADC value (P=0.000). CONCLUSION: The N stage and differentiation of EHCC can be evaluated non-invasively using diffusion-weighted MRI.

VIBRANT: spectral profiling for single-cell drug responses.

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Immunotherapy and targeted therapy for cholangiocarcinoma: Artificial intelligence research in imaging.

Cholangiocarcinoma (CCA) is a highly aggressive hepatobiliary malignancy, second only to hepatocellular carcinoma in prevalence. Despite surgical treatment being the recommended method to achieve a cure, it is not viable for patients with advanced CCA. Gene sequencing and artificial intelligence (AI) have recently opened up new possibilities in CCA diagnosis, treatment, and prognosis assessment. Basic research has furthered our understanding of the tumor-immunity microenvironment and revealed targeted molecular mechanisms, resulting in immunotherapy and targeted therapy being increasingly employed in the clinic. Yet, the application of these remedies in CCA is a challenging endeavor due to the varying pathological mechanisms of different CCA types and the lack of expressed immune proteins and molecular targets in some patients. AI in medical imaging has emerged as a powerful tool in this situation, as machine learning and deep learning are able to extract intricate data from CCA lesion images while assisting clinical decision making, and ultimately improving patient prognosis. This review summarized and discussed the current immunotherapy and targeted therapy related to CCA, and the research progress of AI in this field.

Survival analysis of patients with extrahepatic cholangiocarcinoma: a nomogram for clinical and MRI features.

BACKGROUND: This study aimed to establish a predictive model to estimate the postoperative prognosis of patients with extrahepatic cholangiocarcinoma (ECC) based on preoperative clinical and MRI features. METHODS: A total of 104 patients with ECC confirmed by surgery and pathology were enrolled from January 2013 to July 2021, whose preoperative clinical, laboratory, and MRI data were retrospectively collected and examined, and the effects of clinical and imaging characteristics on overall survival (OS) were analyzed by constructing Cox proportional hazard regression models. A nomogram was constructed to predict OS, and calibration curves and time-dependent receiver operating characteristic (ROC) curves were employed to assess OS accuracy. RESULTS: Multivariate regression analyses revealed that gender, DBIL, ALT, GGT, tumor size, lesion's position, the signal intensity ratio of liver to paraspinal muscle (SIRLiver/Muscle), and the signal intensity ratio of spleen to paraspinal muscle (SIRSpleen/Muscle) on T2WI sequences were significantly associated with OS, and these variables were included in a nomogram. The concordance index of nomogram for predicting OS was 0.766, and the AUC values of the nomogram predicting 1-year and 2-year OS rates were 0.838 and 0.863, respectively. The calibration curve demonstrated good agreement between predicted and observed OS. 5-fold and 10-fold cross-validation show good stability of nomogram predictions. CONCLUSIONS: Our nomogram based on clinical, laboratory, and MRI features well predicted OS of ECC patients, and could be considered as a convenient and personalized prediction tool for clinicians to make decisions.

The value of an MRI-based radiomics model in predicting the survival and prognosis of patients with extrahepatic cholangiocarcinoma.

OBJECTIVES: The study aimed to establish radiomics models based on magnetic resonance imaging (MRI) multiparameter images to predict the survival and prognosis of patients with extrahepatic cholangiocarcinoma (ECC). METHODS: Seventy-eight patients with ECC confirmed by pathology were collected retrospectively. The radiomics model_a/b/c were constructed based on the 1/2/3-year survival of patients with ECC. The best texture features were selected according to postoperative survival time and ECC patient status to calculate the radiomics score (Rad-score). A cutoff value was selected, and patients were divided into high-risk and low-risk groups. RESULTS: Model_a, model_b, and model_c were used to predict 1-, 2-, and 3-year postoperative survival rates, respectively. The area under the curve values in the training and test groups were 1.000 and 0.933 for model_a, 0.909 and 0.907 for model_b, 1.000 and 0.975 for model_c, respectively. The survival prediction model based on the Rad-score showed that the postoperative mortality risk differed significantly between risk groups (p < 0.0001). CONCLUSIONS: The MRI radiomics model could be used to predict the survival and prognosis of patients with ECC.

ATR inhibition induces synthetic lethality in mismatch repair-deficient cells and augments immunotherapy.

The mismatch repair (MMR) deficiency of cancer cells drives mutagenesis and offers a useful biomarker for immunotherapy. However, many MMR-deficient (MMR-d) tumors do not respond to immunotherapy, highlighting the need for alternative approaches to target MMR-d cancer cells. Here, we show that inhibition of the ATR kinase preferentially kills MMR-d cancer cells. Mechanistically, ATR inhibitor (ATRi) imposes synthetic lethality on MMR-d cells by inducing DNA damage in a replication- and MUS81 nuclease-dependent manner. The DNA damage induced by ATRi is colocalized with both MSH2 and PCNA, suggesting that it arises from DNA structures recognized by MMR proteins during replication. In syngeneic mouse models, ATRi effectively reduces the growth of MMR-d tumors. Interestingly, the antitumor effects of ATRi are partially due to CD8+ T cells. In MMR-d cells, ATRi stimulates the accumulation of nascent DNA fragments in the cytoplasm, activating the cGAS-mediated interferon response. The combination of ATRi and anti-PD-1 antibody reduces the growth of MMR-d tumors more efficiently than ATRi or anti-PD-1 alone, showing the ability of ATRi to augment the immunotherapy of MMR-d tumors. Thus, ATRi selectively targets MMR-d tumor cells by inducing synthetic lethality and enhancing antitumor immunity, providing a promising strategy to complement and augment MMR deficiency-guided immunotherapy.

MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network.

BACKGROUND: Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI. METHODS: We recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated. RESULTS: Model 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively. CONCLUSION: The DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.

The glycopatterns of Pseudomonas aeruginosa as a potential biomarker for its carbapenem resistance.

IMPORTANCE: Bacterial surface glycans are an attractive therapeutic target in response to antibiotics; however, current knowledge of the corresponding mechanisms is rather limited. Antimicrobial susceptibility testing, genome sequencing, and MALDI-TOF MS, commonly used in recent years to analyze bacterial resistance, are unable to rapidly and efficiently establish associations between glycans and resistance. The discovery of new antimicrobial strategies still requires the introduction of promising analytical methods. In this study, we applied lectin microarray technology and a machine-learning model to screen for important glycan structures associated with carbapenem-resistant P. aeruginosa. This work highlights that specific glycopatterns can be important biomarkers associated with bacterial antibiotic resistance, which promises to provide a rapid entry point for exploring new resistance mechanisms in pathogens.

ALKBH5 modulates hematopoietic stem and progenitor cell energy metabolism through m6A modification-mediated RNA stability control.

N6-methyladenosine (m6A) RNA modification controls numerous cellular processes. To what extent these post-transcriptional regulatory mechanisms play a role in hematopoiesis has not been fully elucidated. We here show that the m6A demethylase alkB homolog 5 (ALKBH5) controls mitochondrial ATP production and modulates hematopoietic stem and progenitor cell (HSPC) fitness in an m6A-dependent manner. Loss of ALKBH5 results in increased RNA methylation and instability of oxoglutarate-dehydrogenase (Ogdh) messenger RNA and reduction of OGDH protein levels. Limited OGDH availability slows the tricarboxylic acid (TCA) cycle with accumulation of α-ketoglutarate (α-KG) and conversion of α-KG into L-2-hydroxyglutarate (L-2-HG). L-2-HG inhibits energy production in both murine and human hematopoietic cells in vitro. Impaired mitochondrial energy production confers competitive disadvantage to HSPCs and limits clonogenicity of Mll-AF9-induced leukemia. Our study uncovers a mechanism whereby the RNA m6A demethylase ALKBH5 regulates the stability of metabolic enzyme transcripts, thereby controlling energy metabolism in hematopoiesis and leukemia.

Preoperative prediction of the lymphovascular tumor thrombus of colorectal cancer with the iodine concentrations from dual-energy spectral CT.

BACKGROUND: The aim of this study was to explore application value of iodine concentration from dual-energy spectral computed tomography (DESCT) in preoperative prediction of lymphovascular tumor thrombus in patients with colorectal cancer (CRC). METHODS: We finally retrospectively analyzed 50 patients with CRC who underwent abdominal DESCT before receiving any preoperative treatment and underwent surgery to obtain pathological specimens which were stained with hematoxylin-eosin (HE) staining. According to the presence of cancer cell nests in blood vessels and lymphatic vessels, the subjects were divided into the positive group and negative group of lymphovascular tumor thrombus. Two radiologists independently measured the normalized iodine concentration (NIC) values, effective atomic number (Zeff) and CT values of virtual monochromatic images (VMIs) at 40-90 keV of the primary tumors in the arterial phase (AP) and venous phase (VP). Used SPSS 17.0 to calculate the receiver operating characteristic (ROC) curve to evaluate diagnostic value. RESULTS: The patients were divided into lymphovascular tumor thrombus positive group(n = 16) and negative group(n = 34). The values of NIC-AP and NIC-VP in the positive group were 0.17 ± 0.09, 0.51 ± 0.13, respectively. And those in the negative group were 0.15 ± 0.06, 0.43 ± 0.12, respectively. There was significant difference in NIC-VP value between the two groups (p = 0.039), but there was no significant difference in NIC-AP value (p = 0.423). The optimal threshold value of NIC-VP value for diagnosis of lymphovascular tumor thrombus was 0.364. The sensitivity was 68.8% and the specificity was 67.6%. CONCLUSIONS: The NIC-VP value of DESCT can be used to predict the presence or absence of the lymphovascular tumor thrombus in CRC patients before operation, which is helpful to select the best treatment scheme and evaluate its prognosis.

Beneficial or detrimental: Recruiting more types of benign cases for cancer diagnosis based on salivary glycopatterns.

With the advantages of convenient, painless and non-invasive collection, saliva holds great promise as a valuable biomarker source for cancer detection, pathological assessment and therapeutic monitoring. Salivary glycopatterns have shown significant potential for cancer screening in recent years. However, the understanding of benign lesions at non-cancerous sites in cancer diagnosis has been overlooked. Clarifying the influence of benign lesions on salivary glycopatterns and cancer screening is crucial for advancing the development of salivary glycopattern-based diagnostics. In this study, 2885 samples were analyzed using lectin microarrays to identify variations in salivary glycopatterns according to the number, location, and type of lesions. By utilizing our previously published data of tumor-associated salivary glycopatterns, the performance of machine learning algorithm for cancer screening was investigated to evaluate the effect of adding benign disease cases to the control group. The results demonstrated that both the location and number of lesions had discernible effects on salivary glycopatterns. And it was also revealed that incorporating a broad range of benign diseases into the controls improved the classifier's performance in distinguishing cancer cases from controls. This finding holds guiding significance for enhancing salivary glycopattern-based cancer screening and facilitates their practical implementation in clinical settings.

Fabricating process-electrochemical property correlation of laser-scribed graphene and smartphone-based electrochemical platform for portable and sensitive biosensing.

Laser-scribed graphene (LSG), a promising electrode material has attracted special research interest in recent years. Here, the fabricating process-electrochemical property correlation of laser-scribed graphene (LSG) devices was discussed emphatically and a pertinent optimization was performed to achieve better electroanalytical performance. Experiment results indicated that the laser scribing technique possessed great process latitude and reducing laser power and scribing speed facilitated fabricating high-quality graphene electrodes. Benefiting from its binder-free 3D porous network structure and high active/geometric area ratio, the optimized LSG electrode was superior to the screen-printed carbon electrode (SPCE) on electrochemical performance in the [Fe(CN)6]3-/4- redox system. Integrating the LSG electrode with a homemade hand-held detector, a portable electrochemical sensing platform with smartphone readout was developed. It realized a specific detection of H2O2 (linear range: 0.02-3.4 mM, sensitivity: 24.56 µA mM-1 cm-2), glucose (linear range: 0.04-4.0 mM, sensitivity: 16.35 µA mM-1 cm-2) by directly decorating biological enzymes without artificial redox mediator and featured a satisfactory comprehensive performance. The constructed immunosensor for tumor necrosis factor-α exhibited a wide linear range (2-500 pg mL-1) and a 4.3-fold enhancement in sensitivity compared with that of SPCE. With satisfactory selectivity, reproducibility, and sensitivity, the developed smartphone-based electrochemical sensing platform held great promise in accurate detection on the spot. This work also provided a significant reference for tailoring binder-free carbonaceous electrode materials toward the desired application.

Prediction of angiogenesis in extrahepatic cholangiocarcinoma using MRI-based machine learning.

Purpose: Reliable noninvasive method to preoperative prediction of extrahepatic cholangiocarcinoma (eCCA) angiogenesis are needed. This study aims to develop and validate machine learning models based on magnetic resonance imaging (MRI) for predicting vascular endothelial growth factor (VEGF) expression and the microvessel density (MVD) of eCCA. Materials and methods: In this retrospective study from August 2011 to May 2020, eCCA patients with pathological confirmation were selected. Features were extracted from T1-weighted, T2-weighted, and diffusion-weighted images using the MaZda software. After reliability testing and feature screening, retained features were used to establish classification models for predicting VEGF expression and regression models for predicting MVD. The performance of both models was evaluated respectively using area under the curve (AUC) and Adjusted R-Squared (Adjusted R2). Results: The machine learning models were developed in 100 patients. A total of 900 features were extracted and 77 features with intraclass correlation coefficient (ICC) < 0.75 were eliminated. Among all the combinations of data preprocessing methods and classification algorithms, Z-score standardization + logistic regression exhibited excellent ability both in the training cohort (average AUC = 0.912) and the testing cohort (average AUC = 0.884). For regression model, Z-score standardization + stochastic gradient descent-based linear regression performed well in the training cohort (average Adjusted R2 = 0.975), and was also better than the mean model in the test cohort (average Adjusted R2 = 0.781). Conclusion: Two machine learning models based on MRI can accurately predict VEGF expression and the MVD of eCCA respectively.

Long-term outcomes of ultrasound guided high intensity focused ultrasound ablation for patients with uterine fibroids classified by T2WI: a multicenter retrospective study.

OBJECTIVE: To evaluate the long-term outcomes of ultrasound-guided high-intensity focused ultrasound (USgHIFU) ablation of uterine fibroids classified by T2-weighted magnetic resonance imaging (T2WI-MRI). MATERIALS AND METHODS: The data of 1427 premenopausal women with symptomatic uterine fibroids who underwent USgHIFU at four teaching hospitals in China were analyzed retrospectively. The uterine fibroids were classified based on their T2WI-MRI signal intensities relative to that of skeletal muscle, myometrium and endometrium as: hypointense, isointense, heterogeneous hyperintense fibroids (HHF), slightly HHF (sHHF) and markedly HHF (mHHF), respectively. The rates of symptom relief and reintervention post-USgHIFU ablation were compared between the classified groups. RESULTS: A total of 1303 patients were followed up for 44 (40, 49) months. The symptom relief rate of the hypointense and isointense fibroids was 83.3% and 79.5%, respectively, which were significantly higher (p < .05) compared to that of HHF, sHHF and mHHF (58.3%, 44.2% and 60.4%), respectively. sHHF had the lowest symptom relief rate (p < .05). The cumulative reintervention rate for hypointense, isointense, HHF, sHHF and mHHF types were 8.8%, 10.8%, 21.4%, 39.9% and 19.8%, respectively. The reintervention rate of hypointense/isointense fibroids was significantly lower than that of HHF/mHHF/sHHF (p < .01), while sHHF had the highest re-intervention rate (p < .01). Thus, reintervention rate is inversely correlated to the rate of symptom relief. CONCLUSIONS: USgHIFU ablation is effective for hypointense, isointense, HHF and mHHF with acceptable long-term follow-up outcomes. However, sHHF is associated with a higher reintervention rate.

Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF).

Tissue biology involves an intricate balance between cell-intrinsic processes and interactions between cells organized in specific spatial patterns, which can be respectively captured by single-cell profiling methods, such as single-cell RNA-seq (scRNA-seq), and histology imaging data, such as Hematoxylin-and-Eosin (H&E) stains. While single-cell profiles provide rich molecular information, they can be challenging to collect routinely and do not have spatial resolution. Conversely, histological H&E assays have been a cornerstone of tissue pathology for decades, but do not directly report on molecular details, although the observed structure they capture arises from molecules and cells. Here, we leverage adversarial machine learning to develop SCHAF (Single-Cell omics from Histology Analysis Framework), to generate a tissue sample's spatially-resolved single-cell omics dataset from its H&E histology image. We demonstrate SCHAF on two types of human tumors-from lung and metastatic breast cancer-training with matched samples analyzed by both sc/snRNA-seq and by H&E staining. SCHAF generated appropriate single-cell profiles from histology images in test data, related them spatially, and compared well to ground-truth scRNA-Seq, expert pathologist annotations, or direct MERFISH measurements. SCHAF opens the way to next-generation H&E2.0 analyses and an integrated understanding of cell and tissue biology in health and disease.

Complex cerebrovascular diseases in Roberts syndrome caused by novel biallelic ESCO2 variations.

OBJECTIVE: Roberts syndrome (RBS), also known as Roberts-SC phocomelia syndrome, is a rare autosomal recessive developmental disorder caused by mutations in the ESCO2 gene. Cardinal clinical manifestations are pre- and postnatal growth retardation and craniofacial and limb malformations. Here, we report RBS in a Chinese adolescent with novel biallelic ESCO2 variations and complex cerebrovascular diseases. METHODS: Medical history, neurological examinations, neuroimaging, and pathology were collected in the proband and the family. Whole exome sequencing (WES) with copy number variation analysis was performed to screen for genetic variations. RESULTS: The clinical features of the proband were craniofacial and limb malformations together with complex cerebrovascular diseases. She suffered ischemic stroke at 6 years old and died of cerebellar hemorrhage secondary to an aneurysm at 13 years old. Besides, neuroimaging showed the triad of leukoencephalopathy, calcifications, and cysts. Brain histopathology revealed angiomatous changes and perivascular cysts suggesting chronic small cerebral vasculopathy. Whole exome sequencing (WES) identified novel biallelic variations in the ESCO2 gene (c.1220A>T, p.H407L and c.1562delC, p.A521fs). CONCLUSIONS: We describe complex cerebrovascular diseases in Roberts syndrome caused by novel ESCO2 biallelic variations. This case expands not only the cerebral involvement in Roberts syndrome but also the disease spectrum of the neuroimaging triad with leukoencephalopathy, calcifications, and cysts.