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In recent years, silicon photomultipliers (SiPMs) have emerged as preferred photoelectric conversion devices in positron emission tomography (PET) due to their outstanding performance. SiPMs possess single-photon resolution capability and time resolution below 100 ps, enabling precise photon arrival time measurements. These advances paved the way for emerging applications such as time-of-flight PET (TOF-PET), photon counting CT, and positron emission lifetime imaging, presenting new challenges to SiPM performance, the advancing of which to their physical limits has become a key focus area in next-generation SiPM research. In traditional SiPM architectures, signal processing and analog-to-digital conversion introduce noise and degrade time performance, thereby limiting the full SiPM potential. With the recent and rapid development of semiconductor manufacturing processes, SiPMs could be manufactured on standard CMOS process nodes, which marks a significant breakthrough in the SiPM field, allowing for the integration of digital logic within SiPM devices. This advancement opens the possibility of achieving more precise time, energy, and position information within a single SiPM, thereby providing potential possibilities to push SiPMs to their performance limits. In this study, we reviewed the development history, working principles, and performance parameters of SiPMs. We analyzed the limitations of traditional SiPMs, outlined key aspects of digital SiPM research, and introduced various current digital SiPM architectures. Finally, we summarized and anticipated key technologies in digital SiPMs.

The digital positron emission tomography (PET) innovation technology system, which gradually evolved from the Multi-Voltage Threshold (MVT) sampling method, has driven the development of many fields, including key materials, core devices, electronics, intelligent algorithms, and industry standards, forming a new PET industry ecology characterized by modular hardware and intelligent software. In 2010, the world's first All-Digital PET scientific instrument, based on the MVT method, was successfully developed and showed an obvious spatial resolution advantage. With the modular characteristics of the All-Digital PET’s detector, it has successively developed digital PET scientific instruments with different apertures, different fields of view (FOV), and different geometric structures for small animals, large animals, primates, and plants, and has been put into various scientific research applications. In 2015, the world's first All-Digital PET medical device prototype for human scanning was successfully developed. In the following 7 years, several clinical All-Digital PET products have been developed and put into the market. These All-Digital PET products break the fixed architecture of conventional whole body scanning in the past and use modular digital detectors to build various medical devices, such as site-dedicated PET systems, PET-insert system, wearable PET, and proton PET. It has helped promote the rapid expansion of PET applications from tumor diagnosis to new application fields such as nervous system diseases, unexplained fever, and proton precision treatment. The vigorous development of All-Digital PET ecology has also brought innovation to the industrial division of labor. The new scintillation crystal materials, the new silicon photomultiplier (SiPM), and the modular PET detector are developing independently and rapidly, forming their own standardized component or interface. The synchronous progress of upstream and downstream technologies has promoted the development of the entire PET industry chain and innovation chain. With the rapid development of All-Digital PET, the proposal of a variety of PET digital technology routes has also put forward new challenges to industry technical standards and regulatory systems, such as technical requirements for the digitization of PET medical devices and the registration guidelines that have been updated and developed simultaneously. This paper reviewed the dynamic progress of All-Digital PET in scientific instruments, medical devices, the industrial chain, and regulatory science in the past 20 years and looked forward to the future ecological development of All-Digital PET.

Utilizing the Multi-Voltage Threshold method, All-Digital PET technology, characterized by "All-Digital" and "accurate sampling," facilitates hardware and software decoupling and modular "LEGO-like" system construction. This innovation addresses the limitations of traditional PET system architectures, enabling a broader range of innovative applications. This paper reviews recent advancements in application-specific All-Digital PET systems, highlighting their applications and advantages in proton therapy monitoring, clinical and basic brain science research, and plant metabolism studies. Furthermore, we explore the development trends of All-Digital PET technology to meet the increasing demands of PET imaging applications.

Lutetium yttrium silicate (LYSO) has become the most prominent scintillation crystal material in positron emission tomography (PET) because of its outstanding comprehensive performance. In recent years, the emerging All-Digital PET technology based on the Multi-Voltage Threshold (MVT) method has digitized the origin of scintillation pulses, thereby improving key metrics such as spatial resolution and system sensitivity; this advancement has also given rise to new applications like proton therapy monitoring and positron lifetime spectroscopy. Unlike traditional time interval sampling methods, MVT represents a longitudinal sampling technique based on voltage-time, offering inherent advantages in rapidly varying pulse signal sampling domains. Consequently, tailoring the scintillation luminescence properties of LYSO crystals to adapt to the MVT sampling method becomes a new development direction for LYSO scintillation crystals under the demand of digital PET applications. This paper reviews the scintillation principles, performance modulation, and growth techniques of LYSO crystals. It outlines strategies for adjusting key properties of LYSO crystals, such as light output, decay time, and uniformity to align with the sampling characteristics of All-Digital PET. Furthermore, the paper presents the research progress of fast-decaying and highly uniform digitally modified LYSO crystals developed by the research team to meet the demands of All-Digital PET. Finally, based on the current research status of LYSO and the new demand for digital PET, the future development direction of LYSO scintillation crystals is discussed.

Traditional positron emission tomography (PET) systems apply analog circuits for signal processing and achieve coincidence event detection through coincident circuitry. However, these systems suffer from a significant loss of information during projection and image data processing, leading to poor algorithm scalability. In contrast, All-Digital PET systems leverage precise sampling and comprehensive digital processing to simplify hardware design while maximizing software capabilities. These advanced systems rely on software algorithms to handle more operations, thereby providing complete process data, abundant algorithms, and convenient optimization for replacement. By treating detectors and algorithms as fundamental “building blocks,” the plug and imaging (PnI) platform constructs imaging protocols, algorithms, and equipment layers to define the imaging process, extend algorithmic capabilities, and support the construction of diverse animal or clinical PET system types. Moreover, integrating PnI into All-Digital PET systems allows for early information mining from scintillation pulse data levels rather than at the image level, facilitating flexible additional algorithm integration. Based on the aforementioned background, in this study, we critically investigated PnI software-related research advancements, precisely elucidating the comprehensive structure of All-Digital PET systems, architectural flowcharting of the imaging process, and exemplary application cases. Our primary objective was to enhance flexibility for scientists involved in constructing All-Digital PET research instruments while providing valuable references for the expeditious and efficient development of diverse PET systems alongside software algorithmic enhancements.

Analog-to-digital converters (ADCs) transform the analog signals of time-variant physical quantity into digital signals for storing and processing. As the bridge between natural science and information science, ADCs are indispensable in modern industry and scientific research. Since the 1920s, uniform time-domain sampling has become the basic principle in the field of ADCs with the establishment of its electronic implementation and mathematical theory. In the following decades, applications such as positron emission tomography (PET), nuclear fusion neutron spectrum, neutrino detection, etc., which require sampling of many high-speed signals, have emerged one after another. In these applications, ADCs based on uniform time-domain sampling show disadvantages of high power consumption and high cost, so the signal has to be pre-processed before digitalization with losing the original information of the signal. Based on value-domain sampling, the Multi-Voltage Threshold (MVT) method digitizes the signal through several voltage thresholds and then reconstructs the signal with a computer using prior information. The MVT method makes it possible to accurately digitize a large number of high-speed signals. At present, the MVT method has been applied in PET, X-ray security inspection, neutron logging, proton therapy monitoring, etc. This paper outlines the principle of the MVT method, introduces the research progress of MVT electronics in recent years, and further provides an outlook of the MVT research trend.

Abstract Gliomas are highly heterogenous diseases with poor prognosis. Precise survival prediction could benefit further clinical decision‐making, clinical trial incursion, and health economics. Recent research has emphasized the prognostic value of magnetic resonance imaging, pathological specimens, and circulating biomarkers. However, the integrative potential and efficacy of these modalities require to be further validated. After incorporating 218 patients of The Cancer Genome Atlas glioma datasets of and 54 patients of the Huashan cohort with complementary prognostic information, we established a squeeze‐and‐excitation deep learning feature extractor for T1‐contrast enhanced images and histological slides and explored to screen significant circulating 5‐hydroxymethylcytosines (5hmC) profiles for glioma survival by least absolute shrinkage and selection operator‐Cox regression. Therefore, a prognostication predictive model with high efficiency was obtained through survival support vector machine multimodal integration of radiologic imaging, histopathologic imaging features, genome‐wide 5hmC in circulating cell‐free DNA and clinical variables, suggesting a valid strategy (concordance‐index = 0.897; Brier score = 0.118) for improved survival risk stratification of glioma patients.

Abstract Background Parkinson’s Disease (PD) is the second most frequent degenerative disorder, the risk of which increases with age. A preclinical PD diagnostic test does not exist. We identify PD blood metabolites and metabolic pathways significantly correlated with age to develop personalized age-dependent PD blood biomarkers. Results We found 33 metabolites producing a receiver operating characteristic (ROC) area under the curve (AUC) value of 97%. PCA revealed that they belong to three pathways with distinct age-dependent behavior: glycine, threonine and serine metabolism correlates with age only in PD patients; unsaturated fatty acids biosynthesis correlates with age only in a healthy control group; and, finally, tryptophan metabolism characterizes PD but does not correlate with age. Conclusions The targeted analysis of the blood metabolome proposed in this paper allowed to find specific age-related metabolites and metabolic pathways. The model offers a promising set of blood biomarkers for a personalized age-dependent approach to the early PD diagnosis.

Abstract Objectives To develop a diffusion-weighted imaging (DWI) based radiomic signature for predicting early recurrence (ER) (i.e., recurrence within 1 year after surgery), and to explore the potential value for individualized adjuvant chemotherapy. Methods A total of 124 patients with intrahepatic cholangiocarcinoma (ICC) were randomly divided into the training (n = 87) and the validation set (n = 37). Radiomic signature was built using radiomic features extracted from DWI with random forest. An integrated radiomic nomogram was constructed with multivariate logistic regression analysis to demonstrate the incremental value of the radiomic signature beyond clinicopathological-radiographic factors. A clinicopathological-radiographic (CPR) model was constructed as a reference. Results The radiomic signature showed a comparable discrimination performance for predicting ER to CPR model in the validation set (AUC, 0.753 vs. 0.621, p = 0.274). Integrating the radiomic signature with clinicopathological-radiographic factors further improved prediction performance compared with CPR model, with an AUC of 0.821 (95%CI 0.684–0.959) in the validation set (p = 0.01). The radiomic signature succeeded to stratify patients into distinct survival outcomes according to their risk index of ER, and remained an independent prognostic factor in multivariable analysis (disease-free survival (DFS), p

PurposeTo investigate the incremental prognostic value of preoperative apparent diffusion coefficient (ADC) histogram analysis in patients with high-risk prostate cancer (PCa) who received adjuvant hormonal therapy (AHT) after radical prostatectomy (RP).MethodsSixty-two PCa patients in line with the criteria were enrolled in this study. The 10th, 50th, and 90th percentiles of ADC (ADC10, ADC50, ADC90), the mean value of ADC (ADCmean), kurtosis, and skewness were obtained from the whole-lesion ADC histogram. The Kaplan–Meier method and Cox regression analysis were used to analyze the relationship between biochemical recurrence-free survival (BCR-fs) and ADC parameters and other clinicopathological factors. Prognostic models were constructed with and without ADC parameters.ResultsThe median follow-up time was 53.4 months (range, 41.1-79.3 months). BCR was found in 19 (30.6%) patients. Kaplan−Meier curves showed that lower ADCmean, ADC10, ADC50, and ADC90 and higher kurtosis could predict poorer BCR-fs (all p

Abstract Background The efficacy of computed tomography-based multiple body composition parameters in assessing disease behavior and prognosis has not been comprehensively evaluated in Crohn’s disease. This study aimed to assess the association of body composition parameters with disease behavior and outcomes in Crohn’s disease and to compare the efficacies of indexes derived from body and lumbar spinal heights in body composition analysis. Results One hundred twenty-two patients with confirmed Crohn’s disease diagnoses and abdominal computed tomography scans were retrospectively included in this study. Skeletal muscle, visceral, and subcutaneous fat indexes were calculated by dividing each type of tissue area by height2 and lumbar spinal height2. Parameters reflecting the distribution of adiposity were also assessed. Principal component analysis was used to deal with parameters with multicollinearity. Patients were grouped according to their disease behavior (inflammatory vs. structuring/penetrating) and outcomes. Adverse outcome included need for intestinal surgery or anti-TNF therapy. Predictors of disease course from multiple parameters were evaluated using multivariate analysis. Indexes derived from body and lumbar spinal heights were strongly correlated (r, 0.934–0.995; p

Abstract Sensitive and fast detection of neutrons and gamma rays is vital for homeland security, high‐energy physics, and proton therapy. Fast‐neutron detectors rely on light organic scintillators, and γ‐ray detectors use heavy inorganic scintillators and semiconductors. Efficient mixed‐field detection using a single material is highly challenging due to their contradictory requirements. Here we report hybrid perovskites (C8H12N)2Pb(Br1−xClx)4 that combine light organic cations and heavy inorganic skeletons at a molecular level to achieve unprecedented performance for mixed‐field radiation detection. High neutron absorption due to a high density of hydrogen, strong radiative recombination within the highly confined [PbX6]4− layer, and sub‐nanometer distance between absorption sites and radiative centers, enable a light yield of 41 000 photons/MeV, detection pulse width of 2.97 ns and extraordinary linearity response toward both fast neutrons and γ‐rays, outperforming commonly used fast‐neutron scintillators. Neutron energy spectrum, time‐of‐flight based fast‐neutron/γ‐ray discrimination and neutron yield monitoring were all successfully achieved using (C8H12N)2Pb(Br0.95Cl0.05)4 detectors. We further demonstrate the monitoring of reaction kinetics and total power of a nuclear fusion reaction. We envision that molecular hybridized scintillators open a new avenue for mixed‐field radiation detection and imaging.

Time activity curve (TAC) signal processing in plant positron emission tomography (PET) is a frontier nuclear science technique to bring out the quantitative fluid dynamic (FD) flow parameters of the plant vascular system and generate knowledge on crops and their sustainable management, facing the accelerating global climate change. The sparse space-time sampling of the TAC signal impairs the extraction of the FD variables, which can be determined only as averaged values with existing techniques. A data-driven approach based on a reliable FD model has never been formulated. A novel sparse data assimilation digital signal processing method is proposed, with the unique capability of a direct computation of the dynamic evolution of noise correlations between estimated and measured variables, by taking into explicit account the numerical diffusion due to the sparse sampling. The sequential time-stepping procedure estimates the spatial profile of the velocity, the diffusion coefficient and the compartmental exchange rates along the plant stem from the TAC signals. To illustrate the performance of the method, we report an example of the measurement of transport mechanisms in zucchini sprouts.

Abstract Background When Alzheimer’s disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [ $$^{18}$$ 18 F]florbetaben PET biomarkers sensitive to differences between early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD). We conducted [ $$^{18}$$ 18 F]florbetaben PET/CT scans of 43 newly diagnosed AD subjects. We calculated 93 textural parameters for each of the 83 Hammers areas. We identified 41 independent principal components for each brain region, and we studied their Spearman correlation with the age of AD onset, by taking into account multiple comparison corrections. Finally, we calculated the probability that EOAD and LOAD patients have different amyloid- $$\beta$$ β ( $$A\beta$$ A β ) deposition by comparing the mean and the variance of the significant principal components obtained in the two groups with a 2-tailed Student’s t-test. Results We found that four principal components exhibit a significant correlation at a 95% confidence level with the age of onset in the left lateral part of the anterior temporal lobe, the right anterior orbital gyrus of the frontal lobe, the right lateral orbital gyrus of the frontal lobe and the left anterior part of the superior temporal gyrus. The data are consistent with the hypothesis that EOAD patients have a significantly different [ $$^{18}$$ 18 F]florbetaben uptake than LOAD patients in those four brain regions. Conclusions Early-onset AD implies a very irregular pattern of $$A\beta$$ A β deposition. The authors suggest that the identified textural features can be used as quantitative biomarkers for the diagnosis and characterization of EOAD patients.

The reduction of the cerebral glucose metabolism is closely related to the activation of the NOD-like receptor protein 3 (NLRP3) inflammasome in Alzheimer’s disease (AD); however, its underlying mechanism remains unclear. In this paper, 18F-flurodeoxyglucose positron emission tomography was used to trace cerebral glucose metabolism in vivo, along with Western blotting and immunofluorescence assays to examine the expression and distribution of associated proteins. Glucose and insulin tolerance tests were carried out to detect insulin resistance, and the Morris water maze was used to test the spatial learning and memory ability of the mice. The results show increased NLRP3 inflammasome activation, elevated insulin resistance, and decreased glucose metabolism in 3×Tg-AD mice. Inhibiting NLRP3 inflammasome activation using CY-09, a specific inhibitor for NLRP3, may restore cerebral glucose metabolism by increasing the expression and distribution of glucose transporters and enzymes and attenuating insulin resistance in AD mice. Moreover, CY-09 helps to improve AD pathology and relieve cognitive impairment in these mice. Although CY-09 has no significant effect on ferroptosis, it can effectively reduce fatty acid synthesis and lipid peroxidation. These findings provide new evidence for NLRP3 inflammasome as a therapeutic target for AD, suggesting that CY-09 may be a potential drug for the treatment of this disease.

Abstract Background We investigated the image quality of 11C, 68Ga, 18F and 89Zr, which have different positron fractions, physical half-lifes and positron ranges. Three small animal positron emission tomography/computed tomography (PET/CT) systems were used in the evaluation, including the Siemens Inveon, RAYCAN X5 and Molecubes β-cube. The evaluation was performed on a single scanner level using the national electrical manufacturers association (NEMA) image quality phantom and analysis protocol. Acquisitions were performed with the standard NEMA protocol for 18F and using a radionuclide-specific acquisition time for 11C, 68Ga and 89Zr. Images were assessed using percent recovery coefficient (%RC), percentage standard deviation (%STD), image uniformity (%SD), spill-over ratio (SOR) and evaluation of image quantification. Results 68Ga had the lowest %RC ( 85%) and lowest %STD for the 5 mm rod across all systems. For 11C and 89Zr, the maximum %RC was close (> 76%) to the %RC with 18F. A larger SOR were measured in water with 11C and 68Ga compared to 18F on all systems. SOR in air reflected image reconstruction and data correction performance. Large variation in image quantification was observed, with maximal errors of 22.73% (89Zr, Inveon), 17.54% (89Zr, RAYCAN) and − 14.87% (68Ga, Molecubes). Conclusions The systems performed most optimal in terms of NEMA image quality parameters when using 18F, where 11C and 89Zr performed slightly worse than 18F. The performance was least optimal when using 68Ga, due to large positron range. The large quantification differences prompt optimization not only by terms of image quality but also quantification. Further investigation should be performed to find an appropriate calibration and harmonization protocol and the evaluation should be conducted on a multi-scanner and multi-center level.

Positron Emission Tomography is a non-disruptive and high-sensitive digital imaging technique which allows to measure in-vivo and non invasively the changes of metabolic and transport mechanisms in plants. When it comes to the early assessment of stress-induced alterations of plant functions, plant PET has the potential of a major breakthrough. The development of dedicated plant PET systems faces a series of technological and experimental difficulties, which make conventional clinical and preclinical PET systems not fully suitable to agronomy. First, the functional and metabolic mechanisms of plants depend on environmental conditions, which can be controlled during the experiment if the scanner is transported into the growing chamber. Second, plants need to be imaged vertically, thus requiring a proper Field Of View. Third, the transverse Field of View needs to adapt to the different plant shapes, according to the species and the experimental protocols. In this paper, we perform a simulation study, proposing a novel design of dedicated plant PET scanners specifically conceived to address these agronomic issues. We estimate their expected sensitivity, count rate performance and spatial resolution, and we identify these specific features, which need to be investigated when realizing a plant PET scanner. Finally, we propose a novel approach to the measurement and verification of the performance of plant PET systems, including the design of dedicated plant phantoms, in order to provide a standard evaluation procedure for this emerging digital imaging agronomic technology.

Chronic sleep insufficiency is becoming a common issue in the young population nowadays, mostly due to life habits and work stress. Studies in animal models of neurological diseases reported that it would accelerate neurodegeneration progression and exacerbate interstitial metabolic waste accumulation in the brain. In this paper, we study whether chronic sleep insufficiency leads to neurodegenerative diseases in young wild-type animals without a genetic pre-disposition. To this aim, we modeled chronic sleep fragmentation (SF) in young wild-type mice. We detected pathological hyperphosphorylated-tau (Ser396/Tau5) and gliosis in the SF hippocampus. 18F-labeled fluorodeoxyglucose positron emission tomography scan (18F-FDG-PET) further revealed a significant increase in brain glucose metabolism, especially in the hypothalamus, hippocampus and amygdala. Hippocampal RNAseq indicated that immunological and inflammatory pathways were significantly altered in 1.5-month SF mice. More interestingly, differential expression gene lists from stress mouse models showed differential expression patterns between 1.5-month SF and control mice, while Alzheimer's disease, normal aging, and APOEε4 mutation mouse models did not exhibit any significant pattern. In summary, 1.5-month sleep fragmentation could generate AD-like pathological changes including tauopathy and gliosis, mainly linked to stress, as the incremented glucose metabolism observed with PET imaging suggested. Further investigation will show whether SF could eventually lead to chronic neurodegeneration if the stress condition is prolonged in time.

Ionic migration degrades not only the characteristics of halide perovskite solar cells, but also those of perovskite X-ray detectors. Here Yang et al. employ heteroepitaxial BiOBr to passivate Cs2AgBiBr6 double perovskite, which suppresses ionic migration and obtain high performance X-ray detectors.

Silicon photomultipliers produced using standard complementary metal oxide semiconductor (CMOS) processes are at the basis of modern applications of sensors for weak photon fluxes. They allow in fact to integrate transistor-based electronic components within sensors and provide intelligent read-out strategies. In this paper, we investigate the scalability of a 0.35-μm CMOS process to large area devices. We report the design and characterization of SiPMs with a total area of 1, 4, and 9 mm2. Cross talk, photon detection efficiency at 420 nm, gain at 2.5 V overvoltage and breakdown voltage temperature coefficient do not depend on the total area of the sensor and are 10%, 35%, 2.5 x 106, and 35 mV/K, respectively. The dark count rate scales with the total area of the device as 180 kHz/mm2. The total output capacitance, the decay time of the single photon signal, and the single photon time resolution depend on the area of the device. We obtain a capacitance of 66.9, 270.2, and 554.0 pF, a decay time of (27.1±0.1) ns, (50.8±0.1) ns, and (78.2±0.1) ns and a single photon time resolution of (77.97±0.51) ps, (201.67 ± 0.98) ps, and (282.28 ± 0.86) ps for the 1, 4, and 9 mm2 SiPMs, respectively.

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.

Purpose: The purpose of the study is to develop and validate a novel clinical–radiomics nomogram model for pre-operatively predicting the stone-free rate of flexible ureteroscopy (fURS) in kidney stone patients.Patients and Methods: Altogether, 2,129 fURS cases with kidney stones were retrospectively analyzed, and 264 patients with a solitary kidney stone were included in a further study. For lower calyx calculi, a radiomics model was generated in a primary cohort of 99 patients who underwent non-contrast-enhanced computed tomography (NCCT). Radiomics feature selection and signature building were conducted by using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was employed to build a model incorporating radiomics and potential clinical factors. Model performance was evaluated by its discrimination, calibration, and clinical utility. The model was internally validated in 43 patients.Results: The overall success rate of fURS was 72%, while the stone-free rate (SFR) for lower calyx calculi and non-lower calyx calculi was 56.3 and 90.16%, respectively. On multivariate logistic regression analysis of the primary cohort, independent predictors for SFR were radiomics signature, stone volume, operator experience, and hydronephrosis level, which were all selected into the nomogram. The area under the curve (AUC) of clinical–radiomics model was 0.949 and 0.947 in the primary and validation cohorts, respectively. Moreover, the calibration curve showed a satisfactory predictive accuracy, and the decision curve analysis indicated that the nomogram has superior clinical application value.Conclusion: In this novel clinical–radiomics model, the radiomics scores, stone volume, hydronephrosis level, and operator experience were crucial for the flexible ureteroscopy strategy.

Alzheimer’s disease (AD) is a widely distributed neurodegenerative disease characterized clinically by cognitive deficits and pathologically by formation of amyloid-β (Aβ) plaque and neurofibrillary tangles (NFTs) in the brain. Vanadium is a biological trace element that has a function to mimic insulin for diabetes. Bis(ethylmaltolato) oxidovanadium (IV) (BEOV) has been reported to have a hypoglycemic property, but its effect on AD remains unclear. In this study, BEOV was supplemented at doses of 0.2 and 1.0 mmol/L to the AD model mice APPSwe/PS1dE9 for 3 months. The results showed that BEOV substantially ameliorated glucose metabolic disorder as well as synaptic and behavioral deficits of the AD mice. Further investigation revealed that BEOV significantly reduced Aβ generation by increasing the expression of peroxisome proliferator-activated receptor gamma and insulin-degrading enzyme and by decreasing β-secretase 1 in the hippocampus and cortex of AD mice. BEOV also reduced tau hyperphosphorylation by inhibiting protein tyrosine phosphatase-1B and regulating the pathway of insulin receptor/insulin receptor substrate-1/protein kinase B/glycogen synthase kinase 3 beta. Furthermore, BEOV could enhance autophagolysosomal fusion and restore autophagic flux to increase the clearance of Aβ deposits and phosphorylated tau in the brains of AD mice. Collectively, the present study provides solid data for revealing the function and mechanism of BEOV on AD pathology.

The possibility to design a silicon photomultiplier (SiPM) using standard CMOS processes represents the frontier of current low photon flux detectors. It allows an integrated development of both sensor and intelligent read-out electronics on the same technology line and enables to create intelligent devices with on-chip signal processing. We report the design and characterization of an SiPM composed of 20×20 microcells with size 50×50 μm2. The device exhibits 200-kHz/mm2 dark rate, 10% cross talk probability, 1.5% afterpulsing probability, and 5.35 × 106 intrinsic gain at 29-V operational voltage. It is obtained at a 0.35-μm CMOS technology node, which is compatible with the development of integrated electronics. In order to verify the potential application of the device to optical and radiation detection systems, we measure its photon detection efficiency and its response to LED light and scintillation light from an LySO crystal.

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by decreased glucose metabolism and increased neuroinflammation. Hexokinase (HK) is the key enzyme of glucose metabolism and is associated with mitochondria to exert its function. Recent studies have demonstrated that the dissociation of HK from mitochondria is enough to activate the NOD-like receptor protein 3 (NLRP3) inflammasome and leads to the release of interleukin-1β (IL-1β). However, the effect of increased IL-1β on the expression of HK is still unclear in AD. In this paper, we used positron emission tomography (PET), Western blotting and immunofluorescence to study the glucose metabolism, and the expression and distribution of HK in AD. Furthermore, we used lipopolysaccharide (LPS), nigericin (Nig), CY-09 and lonidamine (LND) to treat N2a and N2a-sw cells to investigate the link between IL-1β and HK in AD. The results show decreased expression of HK and the dissociation of HK from mitochondria in AD. Furthermore, a reduction of the expression of IL-1β could increase the expression of HK in AD. These results suggest that inhibiting inflammation may help to restore glucose metabolism in AD.

How emotions affect memory is an open question. Here the authors establish learnt hopeful and learnt helpless mouse models, and find that posterior basolateral amygdala to ventral hippocampal CA1 monosynaptic glutamatergic inputs link emotions to spatial memory performance.

One of the most challenging areas of sensor development for nuclear medicine is the design of proton therapy monitoring systems. Sensors are operated in a high detection rate regime in beam-on conditions. We realized a prototype of a monitoring system for proton therapy based on the technique of positron emission tomography. We used the Plug and Imaging (P&I) technology in this application. This sensing system includes LYSO/silicon photomultiplier (SiPM) detection elements, fast digital multi voltage threshold (MVT) readout electronics and dedicated image reconstruction algorithms. In this paper, we show that the P&I sensor system has a uniform response and is controllable in the experimental conditions of the proton therapy room. The prototype of PET monitoring device based on the P&I sensor system has an intrinsic experimental spatial resolution of approximately 3 mm (FWHM), obtained operating the prototype both during the beam irradiation and right after it. The count-rate performance of the P&I sensor approaches 5 Mcps and allows the collection of relevant statistics for the nuclide analysis. The measurement of both the half life and the relative abundance of the positron emitters generated in the target volume through irradiation of 10 10 protons in approximately 15 s is performed with 0.5% and 5 % accuracy, respectively.

We use the 180 nm GLOBALFOUNDRIES (GF) BCDLite CMOS process for the production of a silicon photomultiplier prototype. We study the main characteristics of the developed sensor in comparison with commercial SiPMs obtained in custom technologies and other SiPMs developed with CMOS-compatible processes. We support our discussion with a transient modeling of the detection process of the silicon photomultiplier as well as with a series of static and dynamic experimental measurements in dark and illuminated environments.

Positron emission tomography (PET) is an important imaging modality for clincial use. Conventionally, the PET scanner is generally built to provide a roomy enough transverse field-of-view (FOV) for imaging most adults' torsos. However, in many cases, the region-of-interest (ROI) for imaging is usually a small area inside the human body. Therefore, to fulfill a PET system which provides an FOV comparable in size to the target ROI seems appealing and more cost effective. Meanwhile, such a PET system has the potential for portable or bedside application with the reduced system size. In this work, we have investigated the feasibility of using dual-headed panel-detectors to build an ROI-focused PET scanner. A novel windowed list-mode ordered subset expectation maximization method was developed to perform the ROI image reconstruction. With this method, the ROI of the object can be reconstructed from the coincidences whose position determined by time-of-flight (TOF) measurements was inside the ROI. Monte Carlo simulation demonstrates the feasibility of detecting lesions not less than 1 cm in diameter, with a 300 ps full width at half maximum timing resolution. As a critical system performance, the impact of TOF information on image quality has been studied and the required TOF capability was assessed. With enhanced timing resolution, the distortions and artifacts were reduced effectively. The further improved TOF capability also shows a noticeable improvement of detection performance for low uptake lesions, as well as the recovery speed of lesion contrast, which is of practical significance in the lesion detection task.

With favorable pharmacokinetics and binding affinity for α(v)β(3) integrin, (18)F-labeled dimeric cyclic RGD peptide ([(18)F]FPPRGD2) has been intensively used as a PET imaging probe for lesion detection and therapy response monitoring. A recently introduced kit formulation method, which uses an (18)F-fluoride-aluminum complex labeled RGD tracer ([(18)F]AlF-NOTA-PRGD2), provides a strategy for simplifying the labeling procedure to facilitate clinical translation. Meanwhile, an easy-to-prepare (68)Ga-labeled NOTA-PRGD2 has also been reported to have promising properties for imaging integrin α(v)β(3). The purpose of this study is to quantitatively compare the pharmacokinetic parameters of [(18)F]FPPRGD2, [(18)F]AlF-NOTA-PRGD2, and [(68)Ga]Ga-NOTA-PRGD2. U87MG tumor-bearing mice underwent 60-min dynamic PET scans following the injection of three tracers. Kinetic parameters were calculated using Logan graphical analysis with reference tissue. Parametric maps were generated using voxel-level modeling. All three compounds showed high binding potential (Bp(ND) = k(3)/k(4)) in tumor voxels. [(18)F]AlF-NOTA-PRGD2 showed comparable Bp(ND) value (3.75±0.65) with those of [(18)F]FPPRGD2 (3.39±0.84) and [(68)Ga]Ga-NOTA-PRGD2 (3.09±0.21) (p>0.05). Little difference was found in volume of distribution (V(T)) among these three RGD tracers in tumor, liver and muscle. Parametric maps showed similar kinetic parameters for all three tracers. We also demonstrated that the impact of non-specific binding could be eliminated in the kinetic analysis. Consequently, kinetic parameter estimation showed more comparable results among groups than static image analysis. In conclusion, [(18)F]AlF-NOTA-PRGD2 and [(68)Ga]Ga-NOTA-PRGD2 have comparable pharmacokinetics and quantitative parameters compared to those of [(18)F]FPPRGD2. Despite the apparent difference in tumor uptake (%ID/g determined from static images) and clearance pattern, the actual specific binding component extrapolated from kinetic modeling appears to be comparable for all three dimeric RGD tracers.

We present a new method for measuring the temperature of magnetic nanoparticles that can also be adapted to immobilized particles. The Néel relaxation mechanism, which dominates the dynamic magnetization process of immobilized magnetic nanoparticles, can be used as an intermediate parameter in a sensing model to obtain temperature information. In this paper, we use the nonlinear response properties of magnetic nanoparticles to derive an analytical expression for the relationship between the phase of cubic susceptibility and temperature. We also consider dipole-dipole interactions and the dependence on field amplitude. Under experimental conditions at selected frequencies and field amplitudes, we compare temperature measurements of magnetic nanoparticles obtained with the proposed thermometry model with those obtained from existing nonlinear dielectric relaxation models. The results show that the temperature measurements obtained from the proposed model are closer to the reference temperatures in the temperature range of 308-353 K, with a standard deviation of less than 0.1 K in the temperature measurement. This new method successfully applies the nonlinear properties of magnetic nanoparticles to high-precision dynamic temperature measurements. It extends the applicability range of temperature measurement methods to conditions with strong interactions or large ac field amplitudes. This new method is expected to be applicable in anti-magnetic environments, for example, in biochemical temperature measurements of magnetically labeled cells in vivo. [ABSTRACT FROM AUTHOR]