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Introduction Primary cutaneous lymphoma (PCL) is a cutaneous non-Hodgkin’s lymphoma that originates in the skin and lacks extracutaneous spread upon initial diagnosis. The clinical management of secondary cutaneous lymphomas is different from that of PCLs, and earlier detection is associated with better prognosis. Accurate staging is necessary to determine the extent of disease and to choose the appropriate treatment. The aim of this review is to investigate the current and potential roles of 18F- fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG PET/CT) in the diagnosis, staging, and monitoring of PCLs. Methods A focused review of the scientific literature was performed using inclusion criteria to filter results pertaining to human clinical studies performed between 2015 and 2021 that analyzed cutaneous PCL lesions on 18F PET/CT imaging. Results & Conclusion A review of 9 clinical studies published after 2015 concluded that 18F-FDG PET/CT is highly sensitive and specific for aggressive PCLs and proved valuable for identifying extracutaneous disease. These studies found 18F-FDG PET/CT highly useful for guiding lymph node biopsy and that imaging results influenced therapeutic decision in many cases. These studies also predominantly concluded that 18F-FDG PET/CT is more sensitive than computed tomography (CT) alone for detection of subcutaneous PCL lesions. Routine revision of nonattenuation-corrected (NAC) PET images may improve the sensitivity of 18F-FDG PET/CT for detection of indolent cutaneous lesions and may expand the potential uses of 18F-FDG PET/CT in the clinic. Furthermore, calculating a global disease score from 18F-FDG PET/CT at every follow-up visit may simplify assessment of disease progression in the early clinical stages, as well as predict the prognosis of disease in patients with PCL.

Purpose As atherosclerosis is a prominent cause of morbidity and mortality, early detection of atherosclerotic plaques is vital to prevent complications. Imaging plays a significant role in this goal. Molecular imaging and structural imaging detect different phases of atherosclerotic progression. In this review, we explain the relation between these types of imaging with the physiopathology of plaques, along with their advantages and disadvantages. We also discuss in detail the most commonly used positron emission tomography (PET) radiotracers for atherosclerosis imaging. Method A comprehensive search was conducted to extract articles related to imaging of atherosclerosis in PubMed, Google Scholar, and Web of Science. The obtained papers were reviewed regarding precise relation with our topic. Among the search keywords utilized were "atherosclerosis imaging", "atherosclerosis structural imaging", "atherosclerosis CT scan" "positron emission tomography", "PET imaging", "18F-NaF", "18F-FDG", and "atherosclerosis calcification." Result Although structural imaging such as computed tomography (CT) offers essential information regarding plaque structure and morphologic features, these modalities can only detect macroscopic alterations that occur later in the disease’s progression, when the changes are frequently irreversible. Molecular imaging modalities like PET, on the other hand, have the advantage of detecting microscopic changes and allow us to treat these plaques before irreversible changes occur. The two most commonly used tracers in PET imaging of atherosclerosis are 18F-sodium fluoride (18F-NaF) and 18F-fluorodeoxyglucose (18F-FDG). While there are limitations in the use of 18F-FDG for the detection of atherosclerosis in coronary arteries due to physiological uptake in myocardium and high luminal blood pool activity of 18F-FDG, 18F-NaF PET is less affected and can be utilized to analyze the coronary arteries in addition to the peripheral vasculature. Conclusion Molecular imaging with PET/CT has become a useful tool in the early detection of atherosclerosis. 18F-NaF PET/CT shows promise in the early global assessment of atherosclerosis, but further prospective studies are needed to confirm its role in this area.

ImportanceDespite being one of the fastest-growing populations in the US, the Asian American population is often misrepresented in and omitted from health research and policy debate. There is a current lack of understanding of how Asian American populations are portrayed in medical school curricula.ObjectiveTo assess how Asian American populations and their subgroups are represented in medical school curricula.Design, Setting, and ParticipantsIn this qualitative study, the content of 632 lectures from all 19 courses of the preclinical curriculum at a single US institution from the academic year 2020 to 2021 was analyzed to identify and characterize unique mentions of race and ethnicity as well as granular ethnicity. Among the 632 lectures, we identified 256 nonrepetitive, unique mentions of race and ethnicity or granular ethnicity. These unique mentions were coded and analyzed for emerging patterns of use.Main Outcomes and MeasuresStudy outcomes included (1) the frequency of specific racial and ethnic categories mentioned in the curriculum, (2) the relative proportion of mentions of race and ethnicity that involved or included Asian American data by courses and context, and (3) key themes representing emerging patterns found from qualitative analysis of curriculum content for mentions of Asian American populations or lack thereof.ResultsAmong the 632 lectures, 256 nonrepetitive mentions of race and ethnicity or granular ethnicity were identified; of these, Asian American populations and/or their subgroups were mentioned in 79 of the instances (30.9%). The most common terms used to denote Asian American populations were Asian, with 36 mentions (45.6%); followed by Japanese, with 10 mentions (12.7%); and Chinese, with 8 mentions (10.1%). Overall, there were 26 mentions (10.2%) of American Indian or Alaska Native populations, 12 mentions (4.7%) of Asian and Pacific Islander or Asian American and Pacific Islander populations, 67 mentions (26.2%) of Asian or Asian American populations, 143 mentions (55.9%) of Black or African American populations, 62 mentions (24.2%) of Hispanic or Latino populations, 4 mentions (1.6%) of Native Hawaiian or Pacific Islander populations, and 154 mentions (60.2%) of White populations. During the analysis of the curriculum for representation of Asian American populations, the following 5 key themes emerged from the data: (1) omission, (2) aggregation, (3) inconsistent categorization, (4) misidentification of granular ethnicity, and (5) association of race and ethnicity with disease.Conclusions and RelevanceThis qualitative study suggests that the curriculum from a single US medical school largely mirrors the inappropriate use of race and ethnicity found in published health literature and clinical guidelines. Solutions with long-term results will require collaboration among diverse groups of interest to adopt inclusive research programs and design. Such solutions could better equip students in combating race-based medicine and could promote community outreach programs built based on trust.

Uterine leiomyomas (fibroids) are common and benign hormone-dependent tumors of the uterus, often coinciding with atypical menstrual bleeding, urinary incontinence, and lower abdominal pain. The PET tracer 18 F-NaF has been used to study metastatic and benign bone disorders, but its potential use in investigating the molecular alterations of extraosseous tissues and tumors has not been fully investigated. In this report, we present a calcifying uterine leiomyoma incidentally detected on 18 F-NaF PET/CT scans in a postmenopausal 61-year-old woman and follow-up image 2 years after, highlighting the potential of 18 F-NaF in monitoring both the molecular and structural progression of uterine leiomyomas.

Osteoclasts are bone-resorbing cells that undergo extensive changes in morphology throughout their differentiation. Altered osteoclast differentiation and activity lead to changes in pathological bone resorption. The mammalian target of rapamycin (mTOR) is a kinase, and aberrant mTOR complex 1 (mTORC1) signaling is associated with altered bone homeostasis. The activation of mTORC1 is biphasically regulated during osteoclastogenesis; however, the mechanism behind mTORC1-mediated regulation of osteoclastogenesis and bone resorption is incompletely understood. Here, we found that MYC coordinates the dynamic regulation of mTORC1 activation during osteoclastogenesis. MYC-deficiency blocked the early activation of mTORC1 and also reversed the decreased activity of mTORC1 at the late stage of osteoclastogenesis. The suppression of mTORC1 activity by rapamycin in mature osteoclasts enhances bone resorption activity despite the indispensable role of high mTORC1 activation in osteoclast formation in both mouse and human cells. Mechanistically, MYC induces Growth arrest and DNA damage-inducible protein (GADD34) expression and suppresses mTORC1 activity at the late phase of osteoclastogenesis. Taken together, our findings identify a MYC-GADD34 axis as an upstream regulator of dynamic mTORC1 activation in osteoclastogenesis and highlight the interplay between MYC and mTORC1 pathways in determining osteoclast activity.

Continued research on the epidermal electronic sensor aims to develop sophisticated platforms that reproduce key multimodal responses in human skin, with the ability to sense various external stimuli, such as pressure, shear, torsion, and touch. The development of such applications utilizes algorithmic interpretations to analyze the complex stimulus shape, magnitude, and various moduli of the epidermis, requiring multiple complex equations for the attached sensor. In this experiment, we integrate silicon piezoresistors with a customized deep learning data process to facilitate in the precise evaluation and assessment of various stimuli without the need for such complexities. With the ability to surpass conventional vanilla deep regression models, the customized regression and classification model is capable of predicting the magnitude of the external force, epidermal hardness and object shape with an average mean absolute percentage error and accuracy of

The acromioclavicular (AC) joint is a common site of injury and degenerative changes such as osteoarthritis (OA) of the shoulder. Physical manifestations of OA are preceded by molecular changes, detection of which may enhance early prophylaxis and monitoring of disease progression. In this study, we investigate the use of 18 F-FDG and 18 F-NaF-PET/CT to assess the effects of limb laterality and age on the inflammation and bone turnover of the AC shoulder joint.We analyzed FDG and NaF-PET/CT scans of 41 females (mean age of 43.9 ± 14.2 years) and 45 males (mean age of 44.5 ± 13.8 years) using a semiquantitative technique based on predefined ROI.There was a greater NaF uptake in the right side of the AC joint compared with the left in both females (left: 2.22 ± 1.00; right: 3.08 ± 1.18; P0.0001) and males (left: 2.57 ± 1.49; right: 2.99 ± 1.40; P = 0.003). No consistent correlation between age and NaF or FDG uptakes were found in both females and males. There was also a positive correlation between FDG and NaF uptakes in both left ( P = 0.01; r = 0.37) and right ( P = 0.0006; r = 0.53) AC joints of male subjects.Our study is the first to reveal the varying effect of right-left limb laterality and aging on FDG and NaF uptake at the AC joint. Future studies correlating the history of shoulder trauma, pain, and degenerative change with FDG and NaF-PET/CT findings will be critical in the adoption of molecular imaging in the clinical setting.

Prostatic calculi are common and usually asymptomatic calcified stones frequently found incidentally in imaging or during the evaluation of benign prostatic hyperplasia. Those associated with chronic prostatitis can lead to bacterial colonization, inflammation, and blockage of secretory ducts, resulting in pelvic pain and lower urinary tract symptoms. Although PET tracers such as 18 F-NaF and 18 F-FDG have been used to assess metastatic and benign bone disorders, their comparative avidity in the domain of extraosseous and prostate calcification remains to be fully explored. We present incidentally detected bilateral prostatic calcification in an asymptomatic 42-year-old man exhibiting coavidity of 18 F-NaF and 18 F-FDG, highlighting the molecular coupling of inflammation and microcalcification in the pathogenesis of prostate calculi.

Background: Cervical spondylosis is the degeneration of cervical spine often associated with aging and neck pain. As the degenerative changes are coupled with altered osteoblastic activity, imaging modalities sensitive to such molecular changes could be valuable for clinical assessment, disease prophylaxis, and monitoring early therapy response. In this study, we examined the role of 18F-sodium fluoride-positron emission tomography/computed tomography (18F-NaF-PET/CT) in detecting age-associated changes in the cervical spine of an adult population with broad age spectrum. Methods: In this retrospective cross-sectional study, we analyzed 18F-NaF-PET/CT scans of 88 control volunteers (43 females, 45 males) with age ranging from 21 to 75 years (mean =44.6, standard deviation, 14.0) divided into younger (21–45 years) and older (46–75 years) age groups. A semi-automated global assessment technique was used to measure 18F-NaF uptake in C2-C4 and C5-C7 vertebrae of the subjects. Furthermore, a CT-based scoring system was devised to measure the degree of structural degeneration. Results: There was a significant difference in 18F-NaF uptake of the younger and older groups at the C5-C7 vertebrae for both females (younger: mean =4.13, 95% CI: 3.72–4.55; older: mean = 4.80, 95% CI: 4.40–5.20; P=0.005) and males (younger: mean =3.66, 95% CI: 3.24–4.09; older: mean =4.22, 95% CI: 3.80–4.64; P=0.009), but not at the C2-C4 vertebrae. Furthermore, there was a positive correlation between the degree of degeneration and 18F-NaF uptake at both C2-C4 and C5-C7 spinal segments of both sexes. Conclusions: Aging is associated with increased 18F-NaF uptake in the cervical spine, which may be associated with osteoblastic activity coupled with degeneration. Our study alludes to the potential role of 18F-NaF-PET/CT in evaluating age-related degeneration and osteoarthritis of the spine. Keywords: Positron emission tomography/computed tomography (PET/CT); sodium fluoride; aging; cervical spine; cervical spondylosis

BACKGROUND: Cervical spondylosis is the degeneration of cervical spine often associated with aging and neck pain. As the degenerative changes are coupled with altered osteoblastic activity, imaging modalities sensitive to such molecular changes could be valuable for clinical assessment, disease prophylaxis, and monitoring early therapy response. In this study, we examined the role of (18)F-sodium fluoride-positron emission tomography/computed tomography ((18)F-NaF-PET/CT) in detecting age-associated changes in the cervical spine of an adult population with broad age spectrum. METHODS: In this retrospective cross-sectional study, we analyzed (18)F-NaF-PET/CT scans of 88 control volunteers (43 females, 45 males) with age ranging from 21 to 75 years (mean =44.6, standard deviation, 14.0) divided into younger (21–45 years) and older (46–75 years) age groups. A semi-automated global assessment technique was used to measure (18)F-NaF uptake in C2-C4 and C5-C7 vertebrae of the subjects. Furthermore, a CT-based scoring system was devised to measure the degree of structural degeneration. RESULTS: There was a significant difference in (18)F-NaF uptake of the younger and older groups at the C5-C7 vertebrae for both females (younger: mean =4.13, 95% CI: 3.72–4.55; older: mean = 4.80, 95% CI: 4.40–5.20; P=0.005) and males (younger: mean =3.66, 95% CI: 3.24–4.09; older: mean =4.22, 95% CI: 3.80–4.64; P=0.009), but not at the C2-C4 vertebrae. Furthermore, there was a positive correlation between the degree of degeneration and (18)F-NaF uptake at both C2-C4 and C5-C7 spinal segments of both sexes. CONCLUSIONS: Aging is associated with increased (18)F-NaF uptake in the cervical spine, which may be associated with osteoblastic activity coupled with degeneration. Our study alludes to the potential role of (18)F-NaF-PET/CT in evaluating age-related degeneration and osteoarthritis of the spine.

Hyperparameter optimization is a very difficult problem in developing deep learning algorithms. In this paper, a genetic algorithm was applied to solve this problem. The accuracy and the verification time were considered by conducting a fitness evaluation. The algorithm was evaluated by using a simple model that has a single convolution layer and a single fully connected layer. A model with three layers was used. The MNIST dataset and a motor fault diagnosis dataset were used to train the algorithm. The results show that the method is useful for reducing the training time.

Maintenance of an industrial electric motor is very important. The most commonly used algorithm for deep learning motor diagnosis using deep learning is CNN, which is one of the representative supervised learning algorithms. However, the failure diagnosis algorithm made with the CNN algorithm is vulnerable to this data. For this reason, an algorithm that complements this has been proposed, and that is to use the RNN and K-means algorithms. The method using RNN has a cyclic neural network structure, so it can grasp the similarity of data. K-means also uses the Euclidean distance method to grasp the similarity between data and classify the data using it. Due to the characteristics of these two algorithms, even if a disturbance is an input, if the similarity of data is high, it is determined as similar data. In this paper, two algorithms were used to perform fault diagnosis and two experiments were conducted to understand the differences and characteristics of the two algorithms. As a result of experiment 1 classifying only normal failures, experiment 2 experimented by increasing the number of failures to be classified. In the case of RNN, the results of experiments 1 and 2 showed similar accuracy. However, in the case of the algorithm using K-means, the accuracy decreased as the number of classifications increased.

Unlike motor failure diagnosis, motor failure prediction is more difficult to collect data and develop algorithms. The classic motor failure prediction is a method of determining the characteristics of a motor failure and finding a signal accordingly. However, this requires specialized knowledge of the motor, and this results in poor versatility. Also, it is not easy to classify the signal change due to the aging of the motor and the signal change due to the failure. To solve this problem, a correlation analysis algorithm is proposed between the motor steady-state signal and the collected signal. The proposed algorithm is composed of deep learning algorithms using the values rather than simple correlation analysis. It is data-driven, unlike the classic method based on prior knowledge. In addition, this algorithm can detect the change of the motor signal by FFT the motor vibration signal by 100 ms. Through the proposed algorithm, the classification of the signal of aging and the failure signal of the motor will proceed.