Multiplication-free and hardware-efficient baud-rate timing error detector for Nyquist and faster than Nyquist optical IM/DD systems.

Clock recovery (CR) algorithms that support higher baud rates and advanced modulation formats are crucial for short-distance optical interconnections, and it is desirable to push CR to operate at baud rate with minimal computing resources and power. In this Letter, we proposed a hardware-efficient and multiplication operation-free baud-rate timing error detector (TED) as a solution to meet these demands. Our approach involves employing both the absolute value of samples and the nonlinear sign operation to emphasize the clock tone, which is deteriorated by severe bandwidth limitation in Nyquist and faster than Nyquist (FTN) systems. Through experimental investigations based on a transceiver system with a 3 dB bandwidth of 30 GHz, the proposed baud-rate TED exhibits excellent performance. The proposed scheme successfully achieves clock synchronization of the received signals with the transmitted signals, including 50 GBaud PAM4/8, 80 GBaud PAM4, and up to 120 GBaud PAM4 FTN signals. To the best of our knowledge, the CR based on the proposed baud-rate TED is the most optimal solution for ultrahigh-speed short-reach IM/DD transmission, comprehensively considering the timing jitter, bit error rate (BER), and implementation complexity.

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation.

Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly adopt a supervised approach and rely heavily on coupled data-to-text pairs. However, for many targeted scenarios and for non-English languages, sufficient quantities of labeled data are often not available. As a result, it is necessary to collect and label data-text pairs for training, which is both costly and time-consuming. To relax the dependency on labeled data of downstream tasks, we propose an intuitive and effective zero-shot learning framework, ZeroNLG, which can deal with multiple NLG tasks, including image-to-text (image captioning), video-to-text (video captioning), and text-to-text (neural machine translation), across English, Chinese, German, and French within a unified framework. ZeroNLG does not require any labeled downstream pairs for training. During training, ZeroNLG (i) projects different domains (across modalities and languages) to corresponding coordinates in a shared common latent space; (ii) bridges different domains by aligning their corresponding coordinates in this space; and (iii) builds an unsupervised multilingual auto-encoder to learn to generate text by reconstructing the input text given its coordinate in shared latent space. Consequently, during inference, based on the data-to-text pipeline, ZeroNLG can generate target sentences across different languages given the coordinate of input data in the common space. Within this unified framework, given visual (imaging or video) data as input, ZeroNLG can perform zero-shot visual captioning; given textual sentences as input, ZeroNLG can perform zero-shot machine translation. We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and "believable" outputs and significantly outperforms existing zero-shot methods. Our code and data are available at https://github.com/yangbang18/ZeroNLG.

A medical multimodal large language model for future pandemics.

Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available labels. However, few such labels exist for rare diseases (e.g., new pandemics). Here we report a medical multimodal large language model (Med-MLLM) for radiograph representation learning, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering a rare disease, our Med-MLLM can be rapidly deployed and easily adapted to them with limited labels. Furthermore, our model supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for clinical tasks that involve both visual and textual data. We demonstrate the effectiveness of our Med-MLLM by showing how it would perform using the COVID-19 pandemic "in replay". In the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input data; 2) three kinds of downstream tasks, including disease reporting, diagnosis, and prognosis; 3) five COVID-19 datasets; and 4) three different languages, including English, Chinese, and Spanish. All experiments show that our model can make accurate and robust COVID-19 decision-support with little labelled data.

Hyperphosphatemia-Related False-Positive 99m Tc-Pyrophosphate Myocardial Scan: A Case Report With Endomyocardial Biopsy Result.

ABSTRACT: With documented high specificity, 99m Tc-pyrophosphate (PYP) scan enables the diagnosis of transthyretin cardiomyopathy to be made reliably without endomyocardial biopsy in patients who do not have monoclonal gammopathy. We report a case with extensive myocardial uptake of Perugini 3 score in the 3-hour 99m Tc-PYP myocardial SPECT that suggested transthyretin cardiac amyloidosis. However, a followed endomyocardial biopsy revealed no amyloid deposition. In this case, hyperphosphatemia was the most likely and presumptive cause of the false-positive 99m Tc-PYP scan. With this case, our experiences of the potential causes of false-positive results of 99m Tc-PYP are further expanded.

Feasibility evaluation of middle-phase 18F-florbetaben positron emission tomography imaging using centiloid quantification and visual assessment.

Background: 18F-florbetaben (FBB) positron emission tomography (PET) scan has been widely used in research and routine clinical practice. Most studies used late-phase (scanning from 90 to 110 min after injection) FBB scans to generate beta-amyloid accumulation data. The feasibility of middle-phase scan is seldom discussed. Using the middle-phase data can shorten the patients' waiting between the injection and scan, and hospital can acquire more flexible schedule of routine scan. Methods: Paired middle-phase (60-80 min) FBB scans and standard (90-110 min) FBB scans were obtained from 27 subjects (12 neurodegenerative dementia, 8 mild cognitive impairment, 3 normal control, and 4 patients not suffering from neurodegenerative dementia). Standardized uptake value ratios (SUVRs) were calculated and converted to centiloid (CL) scale to investigate the impact on image quantification. CL pipeline validation were performed to build an equation converting the middle-phase data into equivalent standard scans. Cohen's kappa of binary interpretation and brain amyloid plaque load (BAPL) score were also used to evaluate the intrareader agreement of the FBB image from the two protocols. Results: The middle-phase FBB SUVR showed an excellent correlation, which provided a linear regression equation of SUVRFBB60-80 = 0.88 × SUVRFBB90-110 + 0.07, with R2=0.98. The slope of the equation indicated that there was bias between the middle and standard acquisition. This can be converted into the CL scale using CL = 174.68 × SUVR - 166.39. Cohen's kappa of binary interpretation and BAPL score were 1.0 (P<0.0001). Conclusions: Our findings indicate that the middle-phase FBB protocol is feasible in clinical applications for scans that are at either end of beta-amyloid spectrum, which provides comparable semiquantitative results to standard scan. Patient's waiting time between the injection and scan can be shortened.

Concept-Aware Video Captioning: Describing Videos With Effective Prior Information.

Concepts, a collective term for meaningful words that correspond to objects, actions, and attributes, can act as an intermediary for video captioning. While many efforts have been made to augment video captioning with concepts, most methods suffer from limited precision of concept detection and insufficient utilization of concepts, which could provide caption generation with inaccurate and inadequate prior information. Considering these issues, we propose a Concept-awARE video captioning framework (CARE) to facilitate plausible caption generation. Based on the encoder-decoder structure, CARE detects concepts precisely via multimodal-driven concept detection (MCD) and offers sufficient prior information to caption generation by global-local semantic guidance (G-LSG). Specifically, we implement MCD by leveraging video-to-text retrieval and the multimedia nature of videos. To achieve G-LSG, given the concept probabilities predicted by MCD, we weight and aggregate concepts to mine the video's latent topic to affect decoding globally and devise a simple yet efficient hybrid attention module to exploit concepts and video content to impact decoding locally. Finally, to develop CARE, we emphasize on the knowledge transfer of a contrastive vision-language pre-trained model (i.e., CLIP) in terms of visual understanding and video-to-text retrieval. With the multi-role CLIP, CARE can outperform CLIP-based strong video captioning baselines with affordable extra parameter and inference latency costs. Extensive experiments on MSVD, MSR-VTT, and VATEX datasets demonstrate the versatility of our approach for different encoder-decoder networks and the superiority of CARE against state-of-the-art methods. Our code is available at https://github.com/yangbang18/CARE.

Dauer larva-derived extracellular vesicles extend the life of Caenorhabditis elegans.

There is growing evidence that extracellular vesicles (EVs) play a functional role in tissue repair and anti-aging by transferring the contents of donor cells to recipient cells. We hypothesized that Dauer (C. elegans), known as "ageless" nematodes, can also secrete extracellular vesicles and influence the lifespan of C. elegans. Here, we isolated EVs of dauer larvae (dauer EVs). Dauer EVs were characterized using transmission electron microscopy, nanoparticle tracking analysis (NTA), and Western blot analysis. Wild-type C. elegans were fed in the presence or absence of dauer EVs and tested for a range of phenotypes, including longevity, mobility and reproductive capacity. Results showed that dauer EVs increased the average lifespan of nematodes by 15.74%, improved mobility, slowed age-related pigmentation as well as body length, and reduced the accumulation of reactive oxygen species and lipids, while not impairing nematode reproductive capacity. These findings suggest that dauer EVs can extend the lifespan of C. elegans as well as the healthy lifespan by reducing ROS accumulation, with potential anti-aging capacity.

Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network.

Purpose: Deep learning-based denoising is promising for myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods use fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. The attention mechanism (Att) is able to learn the relationships between the local receptive field and other voxels in the image. In this study, we propose a 3D attention-guided generative adversarial network (AttGAN) for denoising fast MP-SPECT images. Methods: Fifty patients who underwent 1184 MBq 99mTc-sestamibi stress SPECT/CT scan were retrospectively recruited. Sixty projections were acquired over 180° and the acquisition time was 10 s/view for the full time (FT) mode. Fast MP-SPECT projection images (1 s to 7 s) were generated from the FT list mode data. We further incorporated binary patient defect information (0 = without defect, 1 = with defect) into AttGAN (AttGAN-def). AttGAN, AttGAN-def, cGAN, and Unet were implemented using Tensorflow with the Adam optimizer running up to 400 epochs. FT and fast MP-SPECT projection pairs of 35 patients were used for training the networks for each acquisition time, while 5 and 10 patients were applied for validation and testing. Five-fold cross-validation was performed and data for all 50 patients were tested. Voxel-based error indices, joint histogram, linear regression, and perfusion defect size (PDS) were analyzed. Results: All quantitative indices of AttGAN-based networks are superior to cGAN and Unet on all acquisition time images. AttGAN-def further improves AttGAN performance. The mean absolute error of PDS by AttcGAN-def was 1.60 on acquisition time of 1 s/prj, as compared to 2.36, 2.76, and 3.02 by AttGAN, cGAN, and Unet. Conclusion: Denoising based on AttGAN is superior to conventional CNN-based networks for MP-SPECT.

Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT.

BACKGROUND: Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN) in projection-domain (cGAN-prj) and reconstruction-domain (cGAN-recon). METHODS: Sixty-four noisy SPECT projections were simulated for a population of 100 XCAT phantoms with different anatomical variations and 99mTc-sestamibi distributions. Series of LD projections were obtained by scaling the full dose (FD) count rate to be 1/20 to 1/2 of the original. Twenty patients with 99mTc-sestamibi stress SPECT/CT scans were retrospectively analyzed. For each patient, LD SPECT images (7/10 to 1/10 of FD) were generated from the FD list mode data. All projections were reconstructed by the quantitative OS-EM method. A 3D cGAN was implemented to predict FD images from their corresponding LD images in the projection- and reconstruction-domain. The denoised projections were reconstructed for analysis in various quantitative indices along with cGAN-recon, Gaussian, and Butterworth-filtered images. RESULTS: cGAN denoising improves image quality as compared to LD and conventional post-reconstruction filtering. cGAN-prj can further reduce the dose level as compared to cGAN-recon without compromising the image quality. CONCLUSIONS: Denoising based on cGAN-prj is superior to cGAN-recon for MP-SPECT.

Dual-tracer positron emission tomography/computed tomography as an imaging probe of de novo lipogenesis in preclinical models of hepatocellular carcinoma.

Background: De novo lipogenesis is upregulated in many cancers, and targeting it represents a metabolic approach to cancer treatment. However, the treatment response is unpredictable because lipogenic activity varies greatly among individual tumors, thereby necessitating the assessment of lipogenic activity before treatment. Here, we proposed an imaging probe, positron emission tomography/computed tomography (PET/CT) with dual tracers combining 11C-acetate and 18F-fluorodeoxyglucose (18F-FDG), to assess the lipogenic activity of hepatocellular carcinoma (HCC) and predict the response to lipogenesis-targeted therapy. Methods: We investigated the association between 11C-acetate/18F-FDG uptake and de novo lipogenesis in three HCC cell lines (from well-differentiated to poorly differentiated: HepG2, Hep3B, and SkHep1) by examining the expression of lipogenic enzymes: acetyl-CoA synthetase 2 (ACSS2), fatty acid synthase (FASN), and ATP citrate lyase (ACLY). The glycolysis level was determined through glycolytic enzymes: pyruvate dehydrogenase expression (PDH). On the basis of the findings of dual-tracer PET/CT, we evaluated the treatment response to a lipase inhibitor (orlistat) in cell culture experiments and xenograft mice. Results: Dual-tracer PET/CT revealed the lipogenic activity of various HCC cells, which was positively associated with 11C-acetate uptake and negatively associated with 18F-FDG uptake. This finding represents the negative association between 11C-acetate and 18F-FDG uptake. Because these two tracers revealed the lipogenic and glycolytic activity, respectively, which implies an antagonism between lipogenic metabolism and glucose metabolism in HCC. In addition, dual-tracer PET/CT not only revealed the lipogenic activity but also predicted the treatment response to lipogenesis-targeted therapy. For example, HepG2 xenografts with high 11C-acetate but low 18F-FDG uptake exhibited high lipogenic activity and responded well to orlistat treatment, whereas SkHep1 xenografts with low 11C-acetate but high 18F-FDG uptake exhibited lower lipogenic activity and poor response to orlistat. Conclusion: The proposed non-invasive dual-tracer PET/CT imaging can reveal the lipogenesis and glycolysis status of HCC, thus providing an ideal imaging probe for predicting the therapeutic response of HCC to lipogenesis-targeted therapy.

Interactions between dopamine transporter and N-methyl-d-aspartate receptor-related amino acids on cognitive impairments in schizophrenia.

BACKGROUND: Cognitive impairments, the main determinants of functional outcomes in schizophrenia, had limited treatment responses and need a better understanding of the mechanisms. Dysfunctions of the dopamine system and N-methyl-d-aspartate receptor (NMDAR), the primary pathophysiologies of schizophrenia, may impair cognition. This study explored the effects and interactions of striatal dopamine transporter (DAT) and plasma NMDAR-related amino acids on cognitive impairments in schizophrenia. METHODS: We recruited 36 schizophrenia patients and 36 age- and sex-matched healthy controls (HC). All participants underwent cognitive assessments of attention, memory, and executive function. Single-photon emission computed tomography with 99mTc-TRODAT and ultra-performance liquid chromatography were applied to determine DAT availability and plasma concentrations of eight amino acids, respectively. RESULTS: Compared with HC, schizophrenia patients had lower cognitive performance, higher methionine concentrations, decreased concentrations of glutamic acid, cysteine, aspartic acid, arginine, the ratio of glutamic acid to gamma-aminobutyric acid (Glu/GABA), and DAT availability in the left caudate nucleus (CN) and putamen. Regarding memory scores, Glu/GABA and the DAT availability in left CN and putamen exhibited positive relationships, while methionine concentrations showed negative associations in all participants. The DAT availability in left CN mediated the methionine-memory relationship. An exploratory backward stepwise regression analysis for the four biological markers associated with memory indicated that DAT availability in left CN and Glu/GABA remained in the final model. CONCLUSIONS: This study demonstrated the interactions of striatal DAT and NMDAR-related amino acids on cognitive impairments in schizophrenia. Future studies to comprehensively evaluate their complex interactions and treatment implications are warranted.

Very-Low-Dose Radiation and Clinical Molecular Nuclear Medicine.

Emerging molecular and precision medicine makes nuclear medicine a de facto choice of imaging, especially in the era of target-oriented medical care. Nuclear medicine is minimally invasive, four-dimensional (space and time or dynamic space), and functional imaging using radioactive biochemical tracers in evaluating human diseases on an anatomically configured image. Many radiopharmaceuticals are also used in therapies. However, there have been concerns over the emission of radiation from the radionuclides, resulting in wrongly neglecting the potential benefits against little or any risks at all of imaging to the patients. The sound concepts of radiation and radiation protection are critical for promoting the optimal use of radiopharmaceuticals to patients, and alleviating concerns from caregivers, nuclear medicine staff, medical colleagues, and the public alike.

Flavobacterium taihuense sp. nov., a bacterium isolated from lake sediment.

A novel bacterium, designated NAS39T, was isolated from the interfacial sediment of Taihu Lake in PR China and its taxonomic position was investigated by using a polyphasic approach. Cells of the isolate were Gram-stain-negative, aerobic, non-motile, catalase-positive, yellow and rod-shaped. Phylogenetic analyses based on 16S rRNA gene sequences supported that strain NAS39T formed a cluster within the genus Flavobacterium, and was most closely related to Flavobacterium laiguense LB2P30T (98.4 %), followed by Flavobacterium tiangeerense 0563T (97.4 %). The average nucleotide identity values between strain NAS39T and F. laiguense LB2P30T and F. tiangeerense 0563T were 82.5 and 75.3 %, respectively. The digital DNA-DNA hybridization values between strain NAS39T and F. laiguense LB2P30T and F. tiangeerense 0563T were 40.9 and 18.6 %, respectively. The genomic DNA G+C content was 34.1 mol%. The major respiratory quinone was menaquinone-6. The dominant cellular fatty acids were iso-C15 : 0 and summed feature 3 comprising C16 : 1 ω7c/C16 : 1 ω6c. The polar lipids comprised phosphatidyl ethanolamine, two amino lipids, three amino phospholipids and two unidentified lipids. Based on the phenotypic, chemotaxonomic, genotypic and phylogenetic characteristics, strain NAS39T (=MCCC 1K06094T=KACC 22328T) represents a novel species of the genus Flavobacterium, for which the name Flavobacterium taihuense sp. nov. is proposed.

Cortical excitatory and inhibitory correlates of the fronto-limbic circuit in major depression and differential effects of left frontal brain stimulation in a randomized sham-controlled trial.

BACKGROUND: Major depressive disorder (MDD), particularly treatment-resistant ones, is associated with abnormal fronto-limbic glucose metabolism. 10-Hz repetitive transcranial magnetic stimulation (rTMS) over left prefrontal cortex (PFC) is believed to normalize the abnormal metabolism to treat depression. However, the exact molecular mechanisms underlying the mood circuit of depressed brains and whether brain stimulation techniques regulate the underlying molecules remain elusive. METHODS: Whole-brain glucose metabolism and cortical excitatory and inhibitory markers including P30, N45, P60, N100, and LICI (long-interval cortical inhibition) of TMS-evoked potentials from left DLPFC were measured in 40 subjects with MDD patients. The neurophysiological markers were repeated immediately after 1st session of left PFC rTMS, intermittent theta-burst stimulation (iTBS), and sham (randomly assigned). RESULTS: Brain glucose metabolism in the limbic structures significantly correlated with left PFC P30 (mainly GABA-A and glutamate receptor mediated) and with LICI (mainly GABA-B receptor mediated inhibition) (FWE-corrected p < 0.001). Correlations between other neurophysiological markers (left PFC N45, P60, and N100) and posterior cingulate cortex, a key region in the default mode network, were also noted. One session of rTMS significantly decreased left PFC P60 (mainly glutamate receptor mediated), while a significant group effect was found for LICI (iTBS < sham). CONCLUSION: The first study showed that the underlying molecular mechanisms of fronto-limbic circuit of MDD brains involved glutamatergic excitation and GABAergic inhibition at specific time points. In addition, one session of rTMS mainly modulated glutamatergic neurotransmission at left PFC, while the mechanisms of iTBS might involve GABA-B receptor mediated inhibition. CLINICAL TRIALS REGISTRY NUMBER: UMIN000044951.

Role of Dopamine Transporter in the Relationship Between Plasma Cortisol and Cognition.

OBJECTIVE: Cortisol is associated with cognition in both healthy individuals and patients with neuropsychiatric disorders. Regarding the effects of cortisol on the dopamine system and the association between dopamine transporter (DAT) and cognition, DAT might be a central target linking cortisol and cognition. This study explored the role of striatal DAT in the cortisol-cognition relationship. METHODS: We recruited 33 patients with carbon monoxide poisoning and 33 age- and sex-matched healthy controls. All participants underwent cognitive assessments of attention, memory, and executive function. Single-photon emission computed tomography with 99mTc-TRODAT was used to determine striatal DAT availability. Plasma cortisol, tumor necrosis factor α, and interleukin-10 levels were measured using enzyme-linked immunosorbent assays. RESULTS: Compared with healthy controls, patients with carbon monoxide poisoning had lower cognitive performance, bilateral striatal DAT availability, and plasma tumor necrosis factor-α levels and higher cortisol and interleukin-10 levels. In all participants, plasma cortisol level and bilateral striatal DAT availability were negatively and positively related to cognition, respectively, including memory and executive function with ß from -0.361 (95% confidence interval [CI] = -0.633 to -0.090) to 0.588 (95% CI = 0.319 to 0.858). Moreover, bilateral striatal DAT mediated the cortisol-cognition relationship with indirect effects from -0.067 (95% CI = -0.179 to -0.001) to -0.135 (95% CI = -0.295 to -0.024). The cytokine levels did not influence the mediation effects. CONCLUSIONS: This is the first study to demonstrate that striatal DAT mediates the cortisol-cognition relationship. Future studies are needed to comprehensively evaluate the role of the dopamine system in cortisol-cognition associations and treatment implications.

Multiplexed Molecular Imaging Strategy Integrated with RNA Sequencing in the Assessment of the Therapeutic Effect of Wharton's Jelly Mesenchymal Stem Cell-Derived Extracellular Vesicles for Osteoporosis.

INTRODUCTION: Osteoporosis is a result of an imbalance in bone remodeling. Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) have been considered as a potentially promising treatment for osteoporosis. However, the therapeutic effect, genetic alterations, and in vivo behavior of exogenous EVs for osteoporosis in mice models remain poorly understood. METHODS: A multiplexed molecular imaging strategy was constructed by micro-positron emission tomography (µPET)/computed tomography (CT), µCT, and optical imaging modality which reflected the osteoblastic activity, microstructure, and in vivo behavior of EVs, respectively. RNA sequencing was used to analyze the cargo of EVs, and the bone tissues of ovariectomized (OVX) mice post EV treatment. RESULTS: The result of [18F]NaF µPET showed an increase in osteoblastic activity in the distal femur of EV-treated mice, and the bone structural parameters derived from µCT were also improved. In terms of in vivo behavior of exogenous EVs, fluorescent dye-labeled EVs could target the distal femur of mice, whereas the uptakes of bone tissues were not significantly different between OVX mice and healthy mice. RNA sequencing demonstrated upregulation of ECM-related genes, which might associate with the PI3K/AKT signaling pathway, in line with the results of microRNA analysis showing that mir-21, mir-29, mir-221, and let-7a were enriched in Wharton's jelly-MSC-EVs and correlated to the BMP and PI3K/AKT signaling pathways. CONCLUSION: The therapeutic effect of exogenous WJ-MSC-EVs in the treatment of osteoporosis was successfully assessed by a multiplexed molecular imaging strategy. The RNA sequencing demonstrated the possible molecular targets in the regulation of bone remodeling. The results highlight the novelty of diagnostic and therapeutic strategies of EV-based treatment for osteoporosis.

Fractionated deep-inspiration breath-hold ZTE Compared with Free-breathing four-dimensional ZTE for detecting pulmonary nodules in oncological patients underwent PET/MRI.

The zero echo time (ZTE) technique has improved the detection of lung nodules in PET/MRI but respiratory motion remains a challenge in lung scan. We investigated the feasibility and performance of fractionated deep-inspiration breath-hold (FDIBH) three-dimensional (3D) ZTE FDG PET/MRI for assessing lung nodules in patients with proved malignancy. Sixty patients who had undergone ZTE FDG PET/MRI and chest CT within a three-day interval were retrospectively included. Lung nodules less than 2 mm were excluded for analysis. Two physicians checked the adequacy of FDIBH ZTE and compared the lung nodule detection rates of FDIBH 3D ZTE and free-breathing (FB) four-dimensional (4D) ZTE, with chest CT as the reference standard. FDIBH resolved the effect of respiratory motion in 49 patients. The mean number and size of the pulmonary nodules identified in CT were 15 ± 31.3 per patient and 5.9 ± 4.6 mm in diameter. The overall nodule detection rate was 71% for FDIBH 3D ZTE and 70% for FB 4D ZTE (p = 0.73). FDIBH 3D ZTE significantly outperformed FB 4DZTE in detecting lung base nodules (72% and 68%; p = 0.03), especially for detecting those less than 6 mm (61% and 55%; p = 0.03). High inter-rater reliability for FDIBH 3D ZTE and FB 4D ZTE (k = 0.9 and 0.92) was noted. In conclusion, the capability of FDIBH 3D ZTE in respiratory motion resolution was limited with a technical failure rate of 18%. However, it could provide full expansion of the lung in a shorter scan time which enabled better detection of nodules (< 6 mm) in basal lungs, compared to FB 4D ZTE.

Evaluation of Class IIa Histone Deacetylases Expression and In Vivo Epigenetic Imaging in a Transgenic Mouse Model of Alzheimer's Disease.

Epigenetic regulation by histone deacetylase (HDAC) is associated with synaptic plasticity and memory formation, and its aberrant expression has been linked to cognitive disorders, including Alzheimer's disease (AD). This study aimed to investigate the role of class IIa HDAC expression in AD and monitor it in vivo using a novel radiotracer, 6-(tri-fluoroacetamido)-1-hexanoicanilide ([18F]TFAHA). A human neural cell culture model with familial AD (FAD) mutations was established and used for in vitro assays. Positron emission tomography (PET) imaging with [18F]TFAHA was performed in a 3xTg AD mouse model for in vivo evaluation. The results showed a significant increase in HDAC4 expression in response to amyloid-ß (Aß) deposition in the cell model. Moreover, treatment with an HDAC4 selective inhibitor significantly upregulated the expression of neuronal memory-/synaptic plasticity-related genes. In [18F]TFAHA-PET imaging, whole brain or regional uptake was significantly higher in 3xTg AD mice compared with WT mice at 8 and 11 months of age. Our study demonstrated a correlation between class IIa HDACs and Aßs, the therapeutic benefit of a selective inhibitor, and the potential of using [18F]TFAHA as an epigenetic radiotracer for AD, which might facilitate the development of AD-related neuroimaging approaches and therapies.

Effect of Cerenkov Radiation-Induced Photodynamic Therapy with 18F-FDG in an Intraperitoneal Xenograft Mouse Model of Ovarian Cancer.

Ovarian cancer (OC) metastases frequently occur through peritoneal dissemination, and they contribute to difficulties in treatment. While photodynamic therapy (PDT) has the potential to treat OC, its use is often limited by tissue penetration depth and tumor selectivity. Herein, we combined Cerenkov radiation (CR) emitted by 18F-FDG accumulated in tumors as an internal light source and several photosensitizer (PS) candidates with matched absorption bands, including Verteporfin (VP), Chlorin e6 (Ce6) and 5'-Aminolevulinic acid (5'-ALA), to evaluate the anti-tumor efficacy. The in vitro effect of CR-induced PDT (CR-PDT) was evaluated using a cell viability assay, and the efficiency of PS was assessed by measuring the singlet oxygen production. An intraperitoneal ES2 OC mouse model was used for in vivo evaluation of CR-PDT. Positron emission tomography (PET) imaging and bioluminescence-based imaging were performed to monitor the biologic uptake of 18F-FDG and the therapeutic effect. The in vitro studies demonstrated Ce6 and VP to be more effective PSs for CR-PDT. Moreover, VP was more efficient in the generation of singlet oxygen and continued for a long time when exposed to fluoro-18 (18F). Combining CR emitted by 18F-FDG and VP treatment not only significantly suppressed tumor growth, but also prolonged median survival times compared to either monotherapy.

Mesenchymal Stem Cell-Derived Exosomes Ameliorate Alzheimer's Disease Pathology and Improve Cognitive Deficits.

The accumulation of extracellular ß-amyloid (Aß) plaques within the brain is unique to Alzheimer's disease (AD) and thought to induce synaptic deficits and neuronal loss. Optimal therapies should tackle the core AD pathophysiology and prevent the decline in memory and cognitive functions. This study aimed to evaluate the therapeutic performance of mesenchymal stem cell-derived exosomes (MSC-exosomes), which are secreted membranous elements encapsulating a variety of MSC factors, on AD. A human neural cell culture model with familial AD (FAD) mutations was established and co-cultured with purified MSC-exosomes. 2-[18F]Fluoro-2-deoxy-d-glucose ([18F]FDG) and novel object recognition (NOR) testing were performed before/after treatment to evaluate the therapeutic effect in vivo. The AD-related pathology and the expression of neuronal memory/synaptic plasticity-related genes were also evaluated. The results showed that MSC-exosomes reduced Aß expression and restored the expression of neuronal memory/synaptic plasticity-related genes in the cell model. [18F]FDG-PET imaging and cognitive assessment revealed a significant improvement in brain glucose metabolism and cognitive function in AD transgenic mice. The phase of neurons and astrocytes in the brain of AD mice were also found to be regulated after treatment with MSC-exosomes. Our study demonstrates the therapeutic mechanism of MSC-exosomes and provides an alternative therapeutic strategy based on cell-free MSC-exosomes for the treatment of AD.