Sustained Secretion of CCL21 via an Implantable Cell Reservoir Hydrogel Enhances the Systemic Antitumor Effect of Radiotherapy.

The combination of radiotherapy (RT) and immunotherapy shows promise in improving the clinical treatment of solid tumors; however, it faces challenges of low response rates and systemic toxicity. Herein, an implantable alginate/collagen hydrogel encapsulating C-C motif ligand 21 (CCL21)-expressing dendritic cells (CCL21-DCs@gel) was developed to potentiate the systemic antitumor effects of RT. The hydrogel functioned as a suitable reservoir for in vivo culture and proliferation of CCL21-DCs, thereby enabling sustained CCL21 release. The local CCL21 gradient induced by CCL21-DCs@gel significantly enhanced the efficacy of RT in suppressing primary tumor growth and inhibiting distant metastasis across several mouse models. Furthermore, the combination of RT with CCL21-DCs@gel provided complete prophylactic protection to mice. Mechanistic investigations revealed that CCL21-DCs@gel potentiated RT by promoting tumor lymphangiogenesis and attracting immune cell infiltration into the tumor. Collectively, these results suggest that CCL21-DCs@gel is a promising adjunct to RT for effectively eradicating tumors and preventing tumor recurrence.

Noninvasively Deciphering the Immunosuppressive Tumor Microenvironment Using Galectin-1 PET to Inform Immunotherapy Responses.

Immune checkpoint blockade (ICB) has achieved groundbreaking results in clinical cancer therapy; however, only a subset of patients experience durable benefits. The aim of this study was to explore strategies for predicting tumor responses to optimize the intervention approach using ICB therapy. Methods: We used a bilateral mouse model for proteomics analysis to identify new imaging biomarkers for tumor responses to ICB therapy. A PET radiotracer was synthesized by radiolabeling the identified biomarker-targeting antibody with 124I. The radiotracer was then tested for PET prediction of tumor responses to ICB therapy. Results: We identified galectin-1 (Gal-1), a member of the carbohydrate-binding lectin family, as a potential negative biomarker for ICB efficacy. We established that Gal-1 inhibition promotes a sensitive immune phenotype within the tumor microenvironment (TME) for ICB therapy. To assess the pre-ICB treatment status of the TME, a Gal-1-targeted PET radiotracer, 124I-αGal-1, was developed. PET imaging with 124I-αGal-1 showed the pretreatment immunosuppressive status of the TME before the initiation of therapy, thus enabling the prediction of ICB resistance in advance. Moreover, the use of hydrogel scaffolds loaded with a Gal-1 inhibitor, thiodigalactoside, demonstrated that a single dose of thiodigalactoside-hydrogel significantly potentiated ICB and adoptive cell transfer immunotherapies by remodeling the immunosuppressive TME. Conclusion: Our study underscores the potential of Gal-1-targeted PET imaging as a valuable strategy for early-stage monitoring of tumor responses to ICB therapy. Additionally, Gal-1 inhibition effectively counteracts the immunosuppressive TME, resulting in enhanced immunotherapy efficacy.

Inconsistency between Human Observation and Deep Learning Models: Assessing Validity of Postmortem Computed Tomography Diagnosis of Drowning.

Drowning diagnosis is a complicated process in the autopsy, even with the assistance of autopsy imaging and the on-site information from where the body was found. Previous studies have developed well-performed deep learning (DL) models for drowning diagnosis. However, the validity of the DL models was not assessed, raising doubts about whether the learned features accurately represented the medical findings observed by human experts. In this paper, we assessed the medical validity of DL models that had achieved high classification performance for drowning diagnosis. This retrospective study included autopsy cases aged 8-91 years who underwent postmortem computed tomography between 2012 and 2021 (153 drowning and 160 non-drowning cases). We first trained three deep learning models from a previous work and generated saliency maps that highlight important features in the input. To assess the validity of models, pixel-level annotations were created by four radiological technologists and further quantitatively compared with the saliency maps. All the three models demonstrated high classification performance with areas under the receiver operating characteristic curves of 0.94, 0.97, and 0.98, respectively. On the other hand, the assessment results revealed unexpected inconsistency between annotations and models' saliency maps. In fact, each model had, respectively, around 30%, 40%, and 80% of irrelevant areas in the saliency maps, suggesting the predictions of the DL models might be unreliable. The result alerts us in the careful assessment of DL tools, even those with high classification performance.

Deep Learning-Based Diagnosis of Fatal Hypothermia Using Post-Mortem Computed Tomography.

In forensic medicine, fatal hypothermia diagnosis is not always easy because findings are not specific, especially if traumatized. Post-mortem computed tomography (PMCT) is a useful adjunct to the cause-of-death diagnosis and some qualitative image character analysis, such as diffuse hyperaeration with decreased vascularity or pulmonary emphysema, have also been utilized for fatal hypothermia. However, it is challenging for inexperienced forensic pathologists to recognize the subtle differences of fatal hypothermia in PMCT images. In this study, we developed a deep learning-based diagnosis system for fatal hypothermia and explored the possibility of being an alternative diagnostic for forensic pathologists. An in-house dataset of forensic autopsy proven samples was used for the development and performance evaluation of the deep learning system. We used the area under the receiver operating characteristic curve (AUC) of the system for evaluation, and a human-expert comparable AUC value of 0.905, sensitivity of 0.948, and specificity of 0.741 were achieved. The experimental results clearly demonstrated the usefulness and feasibility of the deep learning system for fatal hypothermia diagnosis.

Discretized hexagonal boron nitride quantum emitters and their chemical interconversion.

Quantum emitters in two-dimensional hexagonal boron nitride (hBN) are of significant interest because of their unique photophysical properties, such as single-photon emission at room temperature, and promising applications in quantum computing and communications. The photoemission from hBN defects covers a wide range of emission energies but identifying and modulating the properties of specific emitters remain challenging due to uncontrolled formation of hBN defects. In this study, more than 2000 spectra are collected consisting of single, isolated zero-phonon lines (ZPLs) between 1.59 and 2.25 eV from diverse sample types. Most of ZPLs are organized into seven discretized emission energies. All emitters exhibit a range of lifetimes from 1 to 6 ns, and phonon sidebands offset by the dominant lattice phonon in hBN near 1370 cm-1. Two chemical processing schemes are developed based on water and boric acid etching that generate or preferentially interconvert specific emitters, respectively. The identification and chemical interconversion of these discretized emitters should significantly advance the understanding of solid-state chemistry and photophysics of hBN quantum emission.

A 2.5D Deep Learning-Based Method for Drowning Diagnosis Using Post-Mortem Computed Tomography.

It is challenging to diagnose drowning in autopsy even with the help of post-mortem multi-slice computed tomography (MSCT) due to the complex pathophysiology and the shortage of forensic specialists equipped with radiology knowledge. Therefore, a computer-aided diagnosis (CAD) system was developed to help with diagnosis. Most deep learning-based CAD systems only utilize 2D information, which is proper for 2D data such as chest X-ray images. However, 3D information should also be considered for 3D data like CT. Conventional 3D methods require a huge amount of data and computational cost when using 3D methods. In this article, we proposed a 2.5D method that converts 3D data into 2D images to train 2D deep learning models for drowning diagnosis. The key point of this 2.5D method is that it uses a subset to represent the whole case, covering this case as much as possible while avoiding other repetitive information. To evaluate the effectiveness of the proposed method, conventional 2D, previous 2.5D, and 3D deep learning-based methods were tested using an MSCT dataset obtained from Tohoku university. Then, to provide explainable diagnosis results, a visualization method called Gradient-weighted Class Activation Mapping was employed to visualize features relevant to drowning in CT images. Results on drowning diagnosis showed that our proposed method achieved the best performance compared to other 2D, 2.5D, and 3D methods. The visual assessment also demonstrated that our method could find the saliency regions corresponding to drowning.

Noninvasive interrogation of CD8+ T cell effector function for monitoring early tumor responses to immunotherapy.

Accurately identifying patients who respond to immunotherapy remains clinically challenging. A noninvasive method that can longitudinally capture information about immune cell function and assist in the early assessment of tumor responses is highly desirable for precision immunotherapy. Here, we show that PET imaging using a granzyme B-targeted radiotracer named 68Ga-grazytracer, could noninvasively and effectively predict tumor responses to immune checkpoint inhibitors and adoptive T cell transfer therapy in multiple tumor models. 68Ga-grazytracer was designed and selected from several radiotracers based on non-aldehyde peptidomimetics, and exhibited excellent in vivo metabolic stability and favorable targeting efficiency to granzyme B secreted by effector CD8+ T cells during immune responses. 68Ga-grazytracer permitted more sensitive discrimination of responders and nonresponders than did 18F-fluorodeoxyglucose, distinguishing between tumor pseudoprogression and true progression upon immune checkpoint blockade therapy in mouse models with varying immunogenicity. In a preliminary clinical trial with 5 patients, no adverse events were observed after 68Ga-grazytracer injection, and clinical responses in cancer patients undergoing immunotherapy were favorably correlated with 68Ga-grazytracer PET results. These results highlight the potential of 68Ga-grazytracer PET to enhance the clinical effectiveness of granzyme B secretion-related immunotherapies by supporting early response assessment and precise patient stratification in a noninvasive and longitudinal manner.

Irreversible synthesis of an ultrastrong two-dimensional polymeric material.

Polymers that extend covalently in two dimensions have attracted recent attention1,2 as a means of combining the mechanical strength and in-plane energy conduction of conventional two-dimensional (2D) materials3,4 with the low densities, synthetic processability and organic composition of their one-dimensional counterparts. Efforts so far have proven successful in forms that do not allow full realization of these properties, such as polymerization at flat interfaces5,6 or fixation of monomers in immobilized lattices7-9. Another frequently employed synthetic approach is to introduce microscopic reversibility, at the cost of bond stability, to achieve 2D crystals after extensive error correction10,11. Here we demonstrate a homogenous 2D irreversible polycondensation that results in a covalently bonded 2D polymeric material that is chemically stable and highly processable. Further processing yields highly oriented, free-standing films that have a 2D elastic modulus and yield strength of 12.7 ± 3.8 gigapascals and 488 ± 57 megapascals, respectively. This synthetic route provides opportunities for 2D materials in applications ranging from composite structures to barrier coating materials.

Chemical kinetic mechanisms and scaling of two-dimensional polymers via irreversible solution-phase reactions.

Two-dimensional (2D) polymers are extended networks of multi-functional repeating units that are covalently linked together but confined to a single plane. The past decade has witnessed a surge in interest and effort toward producing and utilizing 2D polymers. However, facile synthesis schemes suitable for mass production are yet to be realized. In addition, unifying theories to describe the 2D polymerization process, such as those for linear polymers, have not yet been established. Herein, we perform a chemical kinetic simulation to study the recent synthesis of 2D polymers in homogeneous solution with irreversible chemistry. We show that reaction sites for polymerization in 2D always scale unfavorably compared to 3D, growing as molecular weight to the 1/2 power vs 2/3 power for 3D. However, certain mechanisms can effectively suppress out-of-plane defect formation and subsequent 3D growth. We consider two such mechanisms, which we call bond-planarity and templated autocatalysis. In the first, although single bonds can easily rotate out-of-plane to render polymerization in 3D, some double-bond linkages prefer a planar configuration. In the second mechanism, stacked 2D plates may act as van der Waals templates for each other to enhance growth, which leads to an autocatalysis. When linkage reactions possess a 1000:1 selectivity (γ) for staying in plane vs rotating, solution-synthesized 2D polymers can have comparable size and yield with those synthesized from confined polymerization on a surface. Autocatalysis could achieve similar effects when self-templating accelerates 2D growth by a factor ß of 106. A combined strategy relaxes the requirement of both mechanisms by over one order of magnitude. We map the dependence of molecular weight and yield for the 2D polymer on the reaction parameters, allowing experimental results to be used to estimate ß and γ. Our calculations show for the first time from theory the feasibility of producing two-dimensional polymers from irreversible polymerization in solution.

Diameter Dependence of Water Filling in Lithographically Segmented Isolated Carbon Nanotubes.

Although the structure and properties of water under conditions of extreme confinement are fundamentally important for a variety of applications, they remain poorly understood, especially for dimensions less than 2 nm. This problem is confounded by the difficulty in controlling surface roughness and dimensionality in fabricated nanochannels, contributing to a dearth of experimental platforms capable of carrying out the necessary precision measurements. In this work, we utilize an experimental platform based on the interior of lithographically segmented, isolated single-walled carbon nanotubes to study water under extreme nanoscale confinement. This platform generates multiple copies of nanotubes with identical chirality, of diameters from 0.8 to 2.5 nm and lengths spanning 6 to 160 µm, that can be studied individually in real time before and after opening, exposure to water, and subsequent water filling. We demonstrate that, under controlled conditions, the diameter-dependent blue shift of the Raman radial breathing mode (RBM) between 1 and 8 cm-1 measures an increase in the interior mechanical modulus associated with liquid water filling, with no response from exterior water exposure. The observed RBM shift with filling demonstrates a non-monotonic trend with diameter, supporting the assignment of a minimum of 1.81 ± 0.09 cm-1 at 0.93 ± 0.08 nm with a nearly linear increase at larger diameters. We find that a simple hard-sphere model of water in the confined nanotube interior describes key features of the diameter-dependent modulus change of the carbon nanotube and supports previous observations in the literature. Longer segments of 160 µm show partial filling from their ends, consistent with pore clogging. These devices provide an opportunity to study fluid behavior under extreme confinement with high precision and repeatability.

Highly Ordered Two-Dimensional MoS2 Archimedean Scroll Bragg Reflectors as Chromatically Adaptive Fibers.

Nanostructured fibers provide a basis for a unique class of multifunctional textiles, composites, and membrane applications, including those capable of chromatic modulating because of their high aspect ratio, surface area, and processing capability. Here in, we utilize two-dimensional (2D) materials including molybdenum disulfide (MoS2) and hexagonal boron nitride (hBN) to generate single layer Archimedean scroll fibers, possessing cross sections formed from a single 2D molecular layer. Chemical vapor deposited (CVD) monolayer MoS2 (0.29-0.33% in volume) and 226-259 nm-thick poly(methyl methacrylate) (PMMA) were used to create Bragg reflector fibers, exploiting the anisotropic function, exhibiting reflection at 630-709 nm, and verifying the highly ordered nanoinclusions. The Bragg reflectors show a memory response to heating and cooling, which switches the reflection wavelength from 629 to 698 nm. We simulate the reflection and transmission spectra of MoS2/PMMA and MoS2/polydimethylsiloxane layered composites to provide the design of scroll fiber composites using the transfer matrix methods. Moreover, we demonstrate the incorporation of a few-layer CVD hBN into the scroll fiber composite that emits photons at 576 nm. The highly oriented layered structures extend the capability of the fiber nanocomposites to take advantage of anisotropic optical, electrical, and thermal properties unique to 2D materials.

Automatic Diagnosis Based on Spatial Information Fusion Feature for Intracranial Aneurysm.

Timely and accurate auxiliary diagnosis of intracranial aneurysm can help radiologist make treatment plans quickly, saving lives and cutting costs at the same time. At present, Digital Subtraction Angiography (DSA) is the gold standard for the diagnosis of intracranial aneurysm, but as radiologists interpret those imaging sequences frame by frame, misdiagnosis might occur. The utilization of computer-aided diagnosis (CAD) can ease the burdens of radiologists and improve the detection accuracy of aneurysms. In this article, a deep learning method is applied to detect the intracranial aneurysm in 3D Rotational Angiography (3D-RA) based on a spatial information fusion (SIF) method, and instead of a 3D vascular model, 2D image sequences are used. Given the intracranial aneurysm and vascular overlap having similar feature in the most time, rather than focusing on distinguishing them in one frame, the morphological differences between frames are considered as major feature. In the training data, consecutive frames of every imaging time series are extracted and concatenated in a specific way, so that the spatial contextual information could be embedded into a single two-dimensional image. This method enables the time series with obvious correlation between frames be directly trained on 2D convolutional neural network (CNN), instead of 3D-CNN with huge computational cost. Finally, we got an accuracy of 98.89%, with sensitivity and specificity of 99.38% and 98.19%, respectively, which proves the feasibility and availability of the SIF feature.

A CNN-based prototype method of unstructured surgical state perception and navigation for an endovascular surgery robot.

Performance of robot-assisted endovascular surgery (ES) remains highly dependent on an individual surgeon's skills, due to common adoption of master-slave robotic structure. Surgeons' skill modeling and unstructured surgical state perception pose prohibitive challenges for an autonomous ES robot. In this paper, a novel convolutional neural network (CNN)-based framework is proposed to address these challenges for navigation of an ES robot based on surgeons' skill learning. An operating action probability estimator is proposed by integrating a two-dimensional CNN, with which the features of a surgical state image are extracted and then directly mapped to the action probability. A one-dimensional CNN with multi-input is developed to recognize the guide wire operating force condition. An eye-hand collaborative servoing algorithm is proposed to combine the outputs of these two networks and to control the robot under a closed-loop architecture. A real-world ES robot is employed for data collection and task performance evaluation in laboratory condition. Compared with the state of the art, the CNN-based method shows its capability of adapting to different situations and achieves similar success rate and average operating time. Robotic operation performs similar operating trajectory and maintains similar level of operating force with manual operation. The CNN-based method can be easily extended to many other surgical robots. Graphical abstract A surgeon's guide wire operating skills in endovascular surgery (ES) is learned by the proposed CNN-based method. Then, the learned model is used for autonomous control of a ES robot with surgical state input (images and operating force).

Concerted nucleophilic aromatic substitutions.

Nucleophilic aromatic substitution (SNAr) is one of the most widely applied reaction classes in pharmaceutical and chemical research, providing a broadly useful platform for the modification of aromatic ring scaffolds. The generally accepted mechanism for SNAr reactions involves a two-step addition-elimination sequence via a discrete, non-aromatic Meisenheimer complex. Here we use 12C/13C kinetic isotope effect (KIE) studies and computational analyses to provide evidence that prototypical SNAr reactions in fact proceed through concerted mechanisms. The KIE measurements were made possible by a new technique that leverages the high sensitivity of 19F as an NMR nucleus to quantitate the degree of isotopic fractionation. This sensitive technique permits the measurement of KIEs on 10 mg of natural abundance material in one overnight acquisition. As a result, it provides a practical tool for performing detailed mechanistic analyses of reactions that form or break C-F bonds.

Trifluoromethyl Benzoate: A Versatile Trifluoromethoxylation Reagent.

Trifluoromethyl benzoate (TFBz) is developed as a new shelf-stable trifluoromethoxylation reagent, which can be easily prepared from inexpensive starting materials using KF as the only fluorine source. The synthetic potency of TFBz is demonstrated by trifluoromethoxylation-halogenation of arynes, nucleophilic substitution of alkyl (pseudo)halides, cross-coupling with aryl stannanes, and asymmetric difunctionalization of alkenes. The unprecedented trifluoromethoxylation-halogenation of arynes proceeds smoothly at room temperature with the aid of a crown ether-complexed potassium cation, which significantly stabilizes the trifluoromethoxide anion derived from TFBz.

Silver-Mediated Trifluoromethylthiolation-Iodination of Arynes.

A one-pot trifluoromethylthiolation-iodination of arynes with trifluoromethylthiosilver (AgSCF3) and 1-iodophenylacetylene is described. This protocol allows rapid construction of o-trifluoromethylthiolated arene building blocks in moderate yields. These products were found to be excellent precursors for Yagupolskii-Umemoto-type electrophilic trifluoromethylation reagents.

Recent Advances in the One-Step Synthesis of Distally Fluorinated Ketones.

Fluorinated ketones are intriguing compounds in synthetic chemistry and life science-related fields. The development of efficient methodologies to obtain these compounds is of significant importance and has therefore attracted considerable attention. This Minireview highlights recent progress made in the synthesis of fluorine-containing ketones, with an emphasis on those methods in which the construction of carbonyl groups is synergetic with distal (ß-, γ-, δ-, etc.) incorporation of fluorine atoms or fluorinated groups.

Diphenyliodonium-Catalyzed Fluorination of Arynes: Synthesis of ortho-Fluoroiodoarenes.

Described is a one-pot vicinal fluorination-iodination of arynes at room temperature. The diphenyliodonium salt proved to be a privileged catalyst for this nucleophilic fluorination process using CsF as a fluorine source, and a subsequent facile electrophilic iodination with C4 F9 I was also found to be crucial to ensure the efficient fluorination. This new synthetic protocol has a broad substrate scope under mild reaction conditions.

Tibetan medicine "RNSP" in treatment of Alzheimer disease.

Alzheimer disease (Alzheimer Disease, AD) is one of the most common type in senile dementia. Its main pathological features were that a large number of senile plaques gathered in brain extracellular and tangles fibrosis appeared in nerve cells. Currently, the pathogenesis of AD is still uncertain, and scale investigation and combined brain CT, MRI data were analyzed mainly for clinical diagnosis. Mitigation and improvement of the nervous system activity to interfere with the subsequent behavior of the patients are the main methods for treatment. In clinical no drug can really prevent and cure AD. From the view point of Tibetan medicine studies, Tibetan medicine RNSP has effect on improving memory and repairing the neurons in the brain. In this study, we combined the characteristics of AD pathology, pathogenesis, diagnosis and treatment methods to explore the feasibility of Tibetan medicine RNSP for the treatment of AD to provide new ideas for the diagnosis and treatment of AD.

Silver-catalyzed formal insertion of arynes into Rf-I bonds.

An unprecedented silver-catalyzed formal insertion of arynes into Rf-I (Rf = CF3, C2F5) bonds has been developed. This protocol provides easy access to various ortho-perfluoroalkyl iodoarenes under mild conditions. In this insertion reaction, an ionic atom-transfer reaction of RfI occurs, and a silver-mediated metathesis process is involved in the efficient transfer of the electropositive iodine atom.