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Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, existing approaches overlook either spatial information or experiment-induced distortions, leading to significant discrepancies between reconstruction results and in vivo cell locations, causing unreliable downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS). By employing innovative Distributive Constraints into the Optimization scheme, ST-GEARS retrieves anchors with exceeding precision that connect closest spots across sections in vivo. Guided by the anchors, it first rigidly aligns sections, next solves and denoises Elastic Fields to counteract distortions. Through mathematically proved Bi-sectional Fields Application, it eventually recovers the original spatial profile. Studying ST-GEARS across number of sections, sectional distances and sequencing platforms, we observed its outstanding performance on tissue, cell, and gene levels. ST-GEARS provides precise and well-explainable 'gears' between in vivo situations and in vitro analysis, powerfully fueling potential of biological discoveries. Existing 3D Spatial Transcriptomics reconstruction approaches often overlook spatial information or experiment-induced distortions. Here, authors propose ST-GEARS to bridge the gap between in vivo cell locations and in vitro analysis, accurately recovering spatial profiles. [ABSTRACT FROM AUTHOR]

Purpose: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. Methods: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). Results: Overall, 86 and 54 patients with MVI− (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725–0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696–0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893–0.987) and testing sets (AUC = 0.913, 95% CI: 0.785–0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). Conclusion: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). Methods: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). Results: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650–0.882) and 0.789 (95% CI 0.674–0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. Conclusion: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB Clinical relevance statement: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. Key Points: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts. [ABSTRACT FROM AUTHOR]

Previous studies have reported changes in white matter microstructures in patients with insomnia. However, few neuroimaging studies have focused specifically on white matter tracts in insomnia patients after having received treatment. In this prospective study, diffusion-tensor imaging was used in two samples of heart-kidney imbalance insomnia patients who were treated with placebo or Jiao-Tai-Wan, a traditional Chinese medicine commonly used to treat heart-kidney imbalance insomnia, to assess the changes in white matter tracts. Tract-based spatial statistical analyses were first applied to compare the changes in mean diffusivity and fractional anisotropy of white matter between 75 heart-kidney imbalance insomnia patients and 41 healthy control participants. In subsequent randomized, double-blind, placebo-controlled trials, comparisons of mean diffusivity and fractional anisotropy were also performed in 24 heart-kidney imbalance insomnia patients (8 males; 16 females; 42.5 ± 10.4 years) with Jiao-Tai-Wan and 26 heart-kidney imbalance insomnia patients (11 males; 15 females; 39.7 ± 9.4 years) with a placebo, with age and sex as covariates. Fractional anisotropy values in left corticospinal tract were increased in heart-kidney imbalance insomnia patients. Heart-kidney imbalance insomnia patients showed lower mean diffusivity and fractional anisotropy values of several white matter tracts than healthy control participants, such as the bilateral anterior limb of internal capsule, bilateral superior longitudinal fasciculus and bilateral posterior corona radiata. After being treated with Jiao-Tai-Wan, heart-kidney imbalance insomnia patients showed a trend towards reduced fractional anisotropy values in the left corticospinal tract. Jiao-Tai-Wan may improve the sleep quality by reversing the structural changes of the left corticospinal tract caused by heart-kidney imbalance insomnia. [ABSTRACT FROM AUTHOR]

Electronic devices working under harsh environments require protective polymer coatings that can resist the corrosion of their metallic components. Solvent-based polymer coating methods, however, are failing to meet the increasing demand by the trending miniaturization and high integration of devices. Here, we present an ultrathin bilayer conformal coating with excellent protection capability fabricated via a solvent-free initiated chemical vapor deposition method. The bilayer coating consists of a bottom barrier layer crosslinked through urea linkages and a short-fluorinated hydrophobic surface layer anchored by aromatic crosslinkers. The hydrophobic surface resists the adhesion of water and corrosive species while the barrier layer further inhibits their penetration. As a result, the bilayer coating exhibits superior anti-corrosion performance compared to single-layered coatings with a more than 103-fold decrease in corrosion rate than bare copper and protection efficiency higher than 99.9%. Using an LED circuit board as a model device, the bilayer coating can be conformally deposited on different parts of the device. The bilayer-coated device endured 24 h working underwater, whereas device spray-coated with conventional polyurethane resin coating failed after 550 min, suggesting promising practical applications of the reported method in the protection of various electronic devices. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the dynamic evolution of image features of COVID-19 patients appearing as a solitary lesion at initial chest CT scan. Materials and methods: Twenty-two COVID-19 patients with solitary pulmonary lesion from three hospitals in China were enrolled from January 18, 2020 to March 18, 2020. The clinical feature and laboratory findings at first visit, as well as characteristics and dynamic evolution of chest CT images were analyzed. Among them, the CT score evaluation was the sum of the lung involvement in five lobes (0–5 points for each lobe, with a total score ranging from 0 to 25). Results: 22 COVID-19 patients (11 males and 11 females, with an average age of 40.7 ± 10.3) developed a solitary pulmonary lesion within 4 days after the onset of symptoms, the peak time of CT score was about 11 days (with a median CT score of 6), and was discharged about 19 days. The peak of CT score was positively correlated with the peak time and the discharge time (p < 0.001, r = 0.793; p < 0.001, r = 0.715). Scan-1 (first visit): 22 cases (100%) showed GGO and one lobe was involved, CT score was 1.0/1.0 (median/IQR). Scan-2 (peak): 15 cases (68%) showed crazy-paving pattern, 19 cases (86%) showed consolidation, and 2.5 lobes were involved, CT score was 6.0/12.0. Scan-3 (before discharge): ten cases (45%) showed linear opacities, none had crazy-paving pattern, and 2.5 lobes were involved, CT score was 6.0/11.0. Scan-4 (after discharge): three cases (19%) showed linear opacities and one lobe was involved, CT score was 2.0/5.0. Conclusion: The chest CT features are related to the course of COVID-19 disease, and dynamic chest CT scan are helpful to monitor disease progress and patients' condition. In recovered patients with COVID-19, the positive CT manifestations were found within 4 days, lung involvement peaking at approximately 11 days, and discharged at about 19 days. The patients with more severe the lung injury was, the later the peak time appeared and the longer the recovery time was. Although the lesion was resolved over time, isolation and reexamination were required after discharge. [ABSTRACT FROM AUTHOR]

Two novel porous metal-organic frameworks (MOFs) Cu(TCPBDA) and Cd(TCPBDA) based on a new ligand, namely N,N,N′,N′-tetrakis(4-carboxyphenyl)biphenyl-4,4′-diamine (H4TCPBDA), were synthesized by solvent thermal method and characterized with scanning electron microscopy, powder X-ray diffraction, BET analysis and spectrofluorometry. The static adsorption of the two MOFs for lead ions (Pb2+) was investigated. Cu(TCPBDA) showed a higher adsorption capacity for Pb2+ compared with Cd(TCPBDA), and its maximum adsorption capacity was 300 mg/g. The adsorption performance of Cu(TCPBDA) was then further studied. It was found that the adsorption of Cu(TCPBDA) for Pb2+ fitted the Freundlich model and pseudo-second-order equation very well. The adsorption ability of Cu(TCPBDA) was applied to fast and efficient adsorption for Pb2+ in toner. The results indicates that Cu(TCPBDA) is a promising material in adsorption and removal of heavy metal ions. [ABSTRACT FROM AUTHOR]

Background: Mastering right hemicolectomy techniques using laparoscopy in colorectal cancer surgery is very difficult. Although the long-term prognosis of laparoscopic right hemicolectomy (LRH) and complete mesocolic excision is unquestionable, different surgeons have their own opinions on routes of conducting LRH.Objectives: LRH surgery is very complex due to the upper abdominal anatomical structure and vascular variation. Therefore, it has been considered the most difficult of all colorectal cancer surgeries. Our innovative middle cranial approach (MCA) was developed to avoid unnecessary injuries and minimize the operative time, thereby reducing the patient's hospital stay and improving their short-term prognosis.Methods: We compared 90 colon cancer patients who underwent the MCA between January 2016 and January 2017 with 82 patients who underwent the conventional central approach conducted by the same group of physicians (with Dr Cui as the surgeon) from 2011 to 2015. A short-term statistical analysis was performed.Results: A total of 90 patients were included: 43 men and 47 women. Twenty-three patients underwent abdominal surgery (including stomach, rectum, and sigmoid colon surgery; appendectomy; and uterine attachment surgery). The median age of these patients was 62.6 (28-85) years; the median BMI was 22.9 (14.7-33.3) kg/m2; the mean bleeding volume was 53.9 (10-100) ml; the mean tumour diameter was 5.7 (0.8-9) cm, and the average number of lymph nodes detected was 19.2 (7-49).Conclusions: Our study showed that radical resection of right-sided colon cancer using the MCA was safe and feasible for the treatment of colorectal cancer patients. [ABSTRACT FROM AUTHOR]

Few literatures have evaluated the exact role of metabolomics in the identification process of potential biomarkers for acute kidney injury among the patients receiving renal transplantation. On top of this, the success of metabolomics in biomarker translation seems to lie in the robust quantitative method. As such, a single-center retrospective observational study was conducted enrolling 42 patients underwent renal transplantation with/without acute kidney injury, as well as 24 healthy volunteers, in Shanghai Changzheng Hospital. Plasma amino acid metabolic patterns for the participants were investigated by targeted UHPLC-MS/MS metabolic profiling. The most significant changes of the explored metabolites were related to the disturbance of tryptophan metabolism and arginine metabolism. Abnormal circulating tryptophan and symmetric dimethylarginine were identified to be potential biomarkers of acute kidney injury, combination of which showed a higher area under receiver-operator curve value (AUC = 0.901), improved sensitivity (0.889) and specificity (0.831) compared with creatinine only. Overall, these results revealed that targeted metabolomics analysis would be a potent and promising strategy for identification and pre-validation of biomarkers of acute kidney injury in renal transplantation patients. [ABSTRACT FROM AUTHOR]