Search Results (19)

Aldehyde dehydrogenase 2 (ALDH2) is a crucial detoxifying enzyme that eliminates toxic aldehydes. ALDH2 deficiency has been linked to various human diseases, including certain cancers. We have previously reported ALDH2 downregulation in human melanoma tissues. Here, we further investigated the biological significance of ALDH2 downregulation in this malignancy. Analysis of TCGA dataset revealed that low ALDH2 expression correlates with poorer survival in metastatic melanoma. Examination of human metastatic melanoma cell lines confirmed that most had ALDH2 downregulation (ALDH2-low) compared to primary melanocytes. In contrast, a small subset of metastatic melanoma cell lines exhibited normal ALDH2 levels (ALDH2-normal). CRISPR/Cas9-mediated ALDH2 knockout in ALDH2-normal A375 cells promoted tumor growth and MAPK/ERK activation. Given the pivotal role of MAPK/ERK signaling in melanoma and cellular response to acetaldehyde, we compared A375 with ALDH2-low SK-MEL-28 and 1205Lu cells. ALDH2-low cells were intrinsically resistant to BRAF and MEK inhibitors, whereas A375 cells were not. However, A375 cells acquired resistance upon ALDH2 knockout. Furthermore, melanoma cells with acquired resistance to these inhibitors displayed further ALDH2 downregulation. Our findings indicate that ALDH2 downregulation contributes to melanoma progression and therapy resistance in BRAF-mutated human metastatic melanoma cells, highlighting ALDH2 as a potential prognostic marker and therapeutic target in metastatic melanoma. Full article

Background/Objectives: Developing a treatment strategy that effectively prolongs the lives of people with brain tumors requires an accurate diagnosis of the condition. Therefore, improving the preoperative classification of meningiomas is a priority. Machine learning (ML) has made great strides thanks to the development of convolutional neural networks (CNNs) and computer-aided tumor detection systems. The deep convolutional layers automatically extract important and dependable information from the input space, in contrast to more traditional neural network layers. One recent and promising advancement in this field is ML. Still, there is a dearth of studies being carried out in this area. Methods: Therefore, starting with the analysis of magnetic resonance images, we have suggested in this research work a tried-and-tested and methodical strategy for real-time meningioma diagnosis by image segmentation using a very deep transfer learning CNN model or DNN model (VGG-16) with CUDA. Since the VGGNet CNN model has a greater level of accuracy than other deep CNN models like AlexNet, GoogleNet, etc., we have chosen to employ it. The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. Our VGGNet model takes in an sMRI FLAIR image input. The VGG’s convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the smallest possible size that still captures up/down and left/right. Moreover, there are also 1 × 1 convolution filters acting as a linear transformation of the input. This is followed by a ReLU unit. The convolution stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution. All the hidden layers in our VGG network also use ReLU. A dataset consisting of 264 3D FLAIR sMRI image segments from three different classes (meningioma, tuberculoma, and normal) was employed. The number of epochs in the Sequential Model was set to 10. The Keras layers that we used were Dense, Dropout, Flatten, Batch Normalization, and ReLU. Results: According to the simulation findings, our suggested model successfully classified all of the data in the dataset used, with a 99.0% overall accuracy. The performance metrics of the implemented model and confusion matrix for tumor classification indicate the model’s high accuracy in brain tumor classification. Conclusions: The good outcomes demonstrate the possibility of our suggested method as a useful diagnostic tool, promoting better understanding, a prognostic tool for clinical outcomes, and an efficient brain tumor treatment planning tool. It was demonstrated that several performance metrics we computed using the confusion matrix of the previously used model were very good. Consequently, we think that the approach we have suggested is an important way to identify brain tumors. Full article

Background: For patients undergoing abdominal surgery, postoperative pulmonary complications (PPCs) are a major source of morbidity and mortality. The use of point-of-care ultrasonography (POCUS), and specifically POCUS of the lungs, has seen many advancements in recent years. Objectives: We hypothesize that perioperative lung ultrasonography can be used as a predictor for PPCs. Methods: In a Single, 1000 beds, trauma level I medical center, patients presenting for elective intra-abdominal surgery with no severe pulmonary or cardiac diseases were evaluated preoperatively with a standardized 12-point lung ultrasound exam. A second identical exam was performed after surgery in the post-anesthesia care unit. PPCs were also documented. All lung ultrasound exams were presented to a blinded researcher and a lung ultrasound score (LUS) was calculated. Statistical analysis comparing pre- and postoperative LUS and PPC scores were performed. Results: A total of 61 patients were evaluated. The pre-surgery median LUS was 0 (in the range of 0–6) and the post-surgery median LUS was 3 (in the range of 0–14). The pre- to postsurgical LUS delta was 3.4 (standard deviation of 3.3). A postoperative LUS of 6 or more was defined as “high.” A High LUS did not correlate with prolonged post-anesthesia care unit or hospital stay, prolonged oxygen support, or number of desaturation events. Conclusion: For elective abdominal surgery in relatively healthy patients, preoperative LUS usually begins at a normal level and becomes worse after general anesthesia. However, this difference in LUS is not significantly associated with clinically relevant postoperative pulmonary complications such as prolonged oxygen therapy, pneumonia, and noninvasive or invasive mechanical ventilation. Trial registration: Clinicaltrials.gov identifier: NCT05502926. Summary: This paper explores the use of point-of-care ultrasonography as a predictor for postoperative pulmonary complications. The findings suggest that while the lung ultrasound score worsens with general anesthesia, the differences are not significantly associated with postoperative pulmonary complications. Full article

Few effective treatments are available for small cell lung cancer (SCLC), indicating the need to explore new therapeutic options. Here, we focus on an antibody–drug conjugate (ADC) targeting the L1 cell adhesion molecule (L1CAM). Several publicly available databases reveal that (1) L1CAM is expressed at higher levels in SCLC cell lines and tissues than in those of lung adenocarcinoma and (2) the expression levels of L1CAM are slightly higher in SCLC tissues than in adjacent normal tissues. We conducted a series of in vitro experiments using an anti-L1CAM monoclonal antibody (termed HSL175, developed in-house) and the recombinant protein DT3C, which consists of diphtheria toxin lacking the receptor-binding domain but containing the C1, C2, and C3 domains of streptococcal protein G. Our HSL175-DT3C conjugates theoretically kill cells only when the conjugates are internalized by the target (L1CAM-positive) cells through antigen–antibody interaction. The conjugates (an ADC analog) were effective against two SCLC-N (NEUROD1 dominant) cell lines, Lu-135 and STC-1, resulting in decreased viability. In addition, L1CAM silencing rendered the two cell lines resistant to HSL175-DT3C conjugates. These findings suggest that an ADC consisting of a humanized monoclonal antibody based on HSL175 and a potent anticancer drug would be effective against SCLC-N cells. Full article

Few efficacious treatment options are available for patients with small cell lung carcinoma (SCLC), indicating the need to develop novel therapeutic approaches. In this study, we explored kinesin family member 11 (KIF11), a potential therapeutic target in SCLC. An analysis of publicly available data suggested that KIF11 mRNA expression levels are significantly higher in SCLC tissues than in normal lung tissues. When KIF11 was targeted by RNA interference or a small-molecule inhibitor (SB743921) in two SCLC cell lines, Lu-135 and NCI-H69, cell cycle progression was arrested at the G2/M phase with complete growth suppression. Further work suggested that the two cell lines were more significantly affected when both KIF11 and BCL2L1, an anti-apoptotic BCL2 family member, were inhibited. This dual inhibition resulted in markedly decreased cell viability. These findings collectively indicate that SCLC cells are critically dependent on KIF11 activity for survival and/or proliferation, as well as that KIF11 inhibition could be a new strategy for SCLC treatment. Full article

To address challenges related to the inadequate representation and inaccurate discrimination of pedestrian attributes, we propose a novel method for person re-identification, which leverages global feature learning and classification optimization. Specifically, this approach integrates a Normalization-based Channel Attention Module into the fundamental ResNet50 backbone, utilizing a scaling factor to prioritize and enhance key pedestrian feature information. Furthermore, dynamic activation functions are employed to adaptively modulate the parameters of ReLU based on the input convolutional feature maps, thereby bolstering the nonlinear expression capabilities of the network model. By incorporating Arcface loss into the cross-entropy loss, the supervised model is trained to learn pedestrian features that exhibit significant inter-class variance while maintaining tight intra-class coherence. The evaluation of the enhanced model on two popular datasets, Market1501 and DukeMTMC-ReID, reveals improvements in Rank-1 accuracy by 1.28% and 1.4%, respectively, along with corresponding gains in the mean average precision (mAP) of 1.93% and 1.84%. These findings indicate that the proposed model is capable of extracting more robust pedestrian features, enhancing feature discriminability, and ultimately achieving superior recognition accuracy. Full article

Lung ultrasound (LUS) can detect lower respiratory tract involvement in children with acute SARS-CoV-2 infection. However, its role in follow-up assessments is still unclear. To describe LUS findings in children after SARS-CoV-2 infection, we conducted a prospective study in a population of pediatric patients referred to the post-COVID unit in a tertiary center during the study period from February 2021 to May 2022. Children were classified as recovered from acute infection or with persisting symptoms. LUS was performed in all children and a LUS score (ranging from 0 to 36 points) was calculated according to the Italian Academy of Thoracic Ultrasound. Six hundred forty-seven children (304 females, 47%) were enrolled. The median follow-up evaluation was two months. The median age was 7.9 (IQR: 6) years. At the follow-up evaluation, 251 patients (38.8%) had persistent symptoms, of whom 104 (16.1%) had at least one respiratory symptom. The median LUS level was 2 (IQR: 4). LUS findings and LUS scores did not differ in children with Long COVID compared to the group of children fully recovered from the initial infection. In conclusion, after SARS-CoV-2 infection, LUS was mostly normal or showed minimal artifacts in all groups of children. Full article

Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation for LFM signals under additive white Gaussian noise (AWGN) and additive symmetric alpha stable noise (SαSN). DNN is composed of input, output, and two hidden layers, where several nodes in the first and second hidden layers are 25 and 8, respectively. CNN is the content input layer; many hidden layers include convolution, batch normalization, ReLU, max pooling, fully connected, and dropout. The output layer consists of a fully connected softmax and classification layers. SαS distributions are impulsive noise disturbances found in many communication environments such as marine systems, their distribution lacks a closed-form probability density function (PDF), except for specific cases, and infinite second-order statistics, hence geometric SNR (GSNR) is used in this work to determine the effect of noise in a mixture of Gaussian and SαS noise processes. DNN is a machine learning classifier with few layers for reducing FE and SE complexity. CNN is a deep learning classifier, designed with many layers, and proved to be more accurate than DNN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful to the FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption as compared to the classical FE based on time–frequency analysis, which are important requirements for many systems, such as some Internet of Things (IoT) sensor applications. After training CNN for frequency and slope estimation of LFM signals, the performance of CNN (in terms of accuracy) can give good results at very low signal-to-noise ratios where time–frequency distribution (TFD) fails, giving more than 20 dB difference in the GSNR working range as compared to the classical spectrogram-based estimation, and over 15 dB difference with Viterbi-based estimate. Full article

Background: Lung ultrasound (LUS) is gaining consensus as a non-invasive diagnostic imaging method for the evaluation of pulmonary disease in children. Aim: To clarify what type of artifacts (e.g., B-lines, pleural irregularity) can be defined normal LUS findings in children and to evaluate the differences in children who did not experience COVID-19 and in those with recent, not severe, previous COVID-19. Methods: LUS was performed according to standardized protocols. Different patterns of normality were defined: pattern 1: no plural irregularity and no B-lines; pattern 2: only mild basal posterior plural irregularity and no B-lines; pattern 3: mild posterior basal/para-spine/apical pleural irregularity and no B-lines; pattern 4: like pattern 3 plus rare B-lines; pattern 5: mild, diffuse short subpleural vertical artifacts and rare B-lines; pattern 6: mild, diffuse short subpleural vertical artifacts and limited B-lines; pattern 7: like pattern 6 plus minimal subpleural atelectasis. Coalescent B-lines, consolidations, or effusion were considered pathological. Results: Overall, 459 healthy children were prospectively recruited (mean age 10.564 ± 3.839 years). Children were divided into two groups: group 1 (n = 336), those who had not had COVID-19 infection, and group 2 (n = 123), those who experienced COVID-19 infection. Children with previous COVID-19 had higher values of LUS score than those who had not (p = 0.0002). Children with asymptomatic COVID-19 had similar LUS score as those who did not have infections (p > 0.05), while those who had symptoms showed higher LUS score than those who had not shown symptoms (p = 0.0228). Conclusions: We report the pattern of normality for LUS examination in children. We also showed that otherwise healthy children who recovered from COVID-19 and even those who were mildly symptomatic had more “physiological” artifacts at LUS examinations. Full article

In lung ultrasound (LUS), the interactions between the acoustic pulse and the lung surface (including the pleura and a small subpleural layer of tissue) are crucial. Variations of the peripheral lung density and the subpleural alveolar shape and its configuration are typically connected to the presence of ultrasound artifacts and consolidations. COVID-19 pneumonia can give rise to a variety of pathological pulmonary changes ranging from mild diffuse alveolar damage (DAD) to severe acute respiratory distress syndrome (ARDS), characterized by peripheral bilateral patchy lung involvement. These findings are well described in CT imaging and in anatomopathological cases. Ultrasound artifacts and consolidations are therefore expected signs in COVID-19 pneumonia because edema, DAD, lung hemorrhage, interstitial thickening, hyaline membranes, and infiltrative lung diseases when they arise in a subpleural position, generate ultrasound findings. This review analyzes the structure of the ultrasound images in the normal and pathological lung given our current knowledge, and the role of LUS in the diagnosis and monitoring of patients with COVID-19 lung involvement. Full article

Spermatogenesis and folliculogenesis involve cell–cell interactions and gene expression orchestrated by luteinizing hormone (LH) and follicle-stimulating hormone (FSH). FSH regulates the proliferation and maturation of germ cells independently and in combination with LH. In humans, the requirement for high intratesticular testosterone (T) concentration in spermatogenesis remains both a dogma and an enigma, as it greatly exceeds the requirement for androgen receptor (AR) activation. Several data have challenged this dogma. Here we report our findings on a man with mutant LH beta subunit (LHβ) that markedly reduced T production to 1–2% of normal., but despite this minimal LH stimulation, T production by scarce mature Leydig cells was sufficient to initiate and maintain complete spermatogenesis. Also, in the LH receptor (LHR) knockout (LuRKO) mice, low-dose T supplementation was able to maintain spermatogenesis. In addition, in antiandrogen-treated LuRKO mice, devoid of T action, the transgenic expression of a constitutively activating follicle stimulating hormone receptor (FSHR) mutant was able to rescue spermatogenesis and fertility. Based on rodent models, it is believed that gonadotropin-dependent follicular growth begins at the antral stage, but models of FSHR inactivation in women contradict this claim. The complete loss of FSHR function results in the complete early blockage of folliculogenesis at the primary stage, with a high density of follicles of the prepubertal type. These results should prompt the reassessment of the role of gonadotropins in spermatogenesis, folliculogenesis and therapeutic applications in human hypogonadism and infertility. Full article

The potential role of ultrasound for the diagnosis of pulmonary diseases is a recent field of research, because, traditionally, lungs have been considered unsuitable for ultrasonography for the high presence of air and thoracic cage that prevent a clear evaluation of the organ. The peculiar anatomy of the pediatric chest favors the use of lung ultrasound (LUS) for the diagnosis of respiratory conditions through the interpretation of artefacts generated at the pleural surface, correlating them to disease-specific patterns. Recent studies demonstrate that LUS can be a valid alternative to chest X-rays for the diagnosis of pulmonary diseases, especially in children to avoid excessive exposure to ionizing radiations. This review focuses on the description of normal and abnormal findings during LUS of the most common pediatric pathologies. Current literature demonstrates usefulness of LUS that may become a fundamental tool for the whole spectrum of lung pathologies to guide both diagnostic and therapeutic decisions. Full article

Background: The usefulness of Lung Ultrasound (LUS) for the diagnosis of interstitial syndrome caused by COVID-19 has been broadly described. The aim of this study was to evaluate if LUS may predict the complications (hospital admission) of COVID-19 pneumonia in primary care patients. Methods: This observational study collects data from a cohort of 279 patients with clinical symptoms of COVID-19 pneumonia who attended the Balaguer Primary Health Care Area between 16 March 2020 and 30 September 2020. We collected the results of LUS scans reported by one general practitioner. We created a database and analysed the absolute and relative frequencies of LUS findings and their association with hospital admission. We found that different LUS patterns (diffuse, attenuated diffuse, and predominantly unilateral) were risk factors for hospital admission (p < 0.05). Additionally, an evolutionary pattern during the acute phase represented a risk factor (p = 0.0019). On the contrary, a normal ultrasound pattern was a protective factor (p = 0.0037). Finally, the presence of focal interstitial pattern was not associated with hospital admission (p = 0.4918). Conclusion: The lung ultrasound was useful to predict complications in COVID-19 pneumonia and to diagnose other lung diseases such as cancer, tuberculosis, pulmonary embolism, chronic interstitial pneumopathy, pleuropericarditis, pneumonia or heart failure. Full article

Introduction: There is debate as to whether lung-ultrasound (LUS) can replace lung-auscultation (LA) in the assessment of respiratory diseases. Methodology: The diagnostic validity, safety, and reliability of LA and LUS were analyzed in patients admitted in a pulmonary ward due to decompensated obstructive airway diseases, decompensated interstitial diseases, and pulmonary infections, in a prospective study. Standard formulas were used to calculate the diagnostic sensitivity, specificity, and accuracy. The interobserver agreement with respect to the LA and LUS findings was evaluated based on the Kappa coefficient (ᴋ). Results: A total of 115 patients were studied. LUS was more sensitive than the LA in evaluating pulmonary infections (93.59% vs. 77.02%; p = 0.001) and more specifically in the case of decompensated obstructive airway diseases (95.6% vs. 19.10%; p = 0.001). The diagnostic accuracy of LUS was also greater in the case of pulmonary infections (75.65% vs. 60.90%; p = 0.02). The sensitivity and specificity of the combination of LA and LUS was 95.95%, 50% in pulmonary infections, 76.19%, 100% in case of decompensated obstructive airway diseases, and (100%, 88.54%) in case of interstitial diseases. (ᴋ) was 0.71 for an A-pattern, 0.73 for pathological B-lines, 0.94 for condensations, 0.89 for pleural-effusion, 0.63 for wheezes, 0.38 for rhonchi, 0.68 for fine crackles, 0.18 for coarse crackles, and 0.29 for a normal LA. Conclusions: There is a greater interobserver agreement in the interpretation of LUS-findings compared to that of LA-noises, their combined use improves diagnostic performance in all diseases examined. Full article

Point-of-care lung ultrasound (LUS) is an attractive alternative to chest X-ray (CXR), but its diagnostic accuracy compared to CXR has not been well studied in coronavirus disease 2019 (COVID-19) patients. We conducted a prospective observational study to assess the correlation between LUS and CXR findings in COVID-19 patients. Ninety-six patients with a clinical diagnosis of COVID-19 underwent an LUS exam and CXR upon presentation. Physicians blinded to the CXR findings performed all LUS exams. Detection of pulmonary infiltrates by CXR versus LUS was compared between patients categorized as suspected or confirmed COVID-19 based on reverse transcriptase-polymerase chain reaction. Sensitivities and correlation by Kappa statistic were calculated between LUS and CXR. LUS detected pulmonary infiltrates more often than CXR in both suspected and confirmed COVID-19 subjects. The most common LUS abnormalities were discrete B-lines, confluent B-lines, and small subpleural consolidations. Most important, LUS detected unilateral or bilateral pulmonary infiltrates in 55% of subjects with a normal CXR. Substantial agreement was demonstrated between LUS and CXR for normal, unilateral or bilateral findings (Κ = 0.48 (95% CI 0.34 to 0.63)). In patients with suspected or confirmed COVID-19, LUS detected pulmonary infiltrates more often than CXR, including more than half of the patients with a normal CXR. Full article