A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields.

Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models.

Consistency between self-reported disease diagnosis and clinical assessment and under-reporting for chronic conditions: data from a community-based study in Xi'an, China.

Aims: The current study aims to investigate the consistency between the surveyees' self-reported disease diagnosis and clinical assessment of eight major chronic conditions using community-based survey data collected in Xi'an, China in 2017. With a focus on under-reporting patients, we aim to explore its magnitude and associated factors, to provide an important basis for disease surveillance, health assessment and resource allocation, and public health decision-making and services. Methods: Questionnaires were administered to collect self-reported chronic condition prevalence among the study participants, while physical examinations and laboratory tests were conducted for clinical assessment. For each of the eight chronic conditions, the sensitivity, specificity, under-reporting, over-reporting, and agreement were calculated. Log-binomial regression analysis was employed to identify potential factors that may influence the consistency of chronic condition reporting. Results: A total of 2,272 participants were included in the analysis. Four out of the eight chronic conditions displayed under-reporting exceeding 50%. The highest under-reporting was observed for goiter [85.93, 95% confidence interval (CI): 85.25-86.62%], hyperuricemia (83.94, 95% CI: 83.22-84.66%), and thyroid nodules (72.89, 95% CI: 72.02-73.76%). Log-binomial regression analysis indicated that senior age and high BMI were potential factors associated with the under-reporting of chronic condition status in the study population. Conclusion: The self-reported disease diagnosis by respondents and clinical assessment data exhibit significant inconsistency for all eight chronic conditions. Large proportions of patients with multiple chronic conditions were under-reported in Xi'an, China. Combining relevant potential factors, targeted health screenings for high-risk populations might be an effective method for identifying under-reporting patients.

Association between first-line antidepressant use and risk of dementia in older adults: a retrospective cohort study.

BACKGROUND: Prior studies suggested that antidepressant use is associated with an increased risk of dementia compared to no use, which is subject to confounding by indication. We aimed to compare the dementia risk among older adults with depression receiving first-line antidepressants (i.e., SSRI/SNRI) versus psychotherapy, which is also considered the first-line therapy for depression. METHODS: This retrospective cohort study was conducted using the US Medical Expenditure Panel Survey from 2010 to 2019. We included adults aged ≥ 50 years diagnosed with depression who initiated SSRI/SNRI or psychotherapy. We excluded patients with a dementia diagnosis before the first record of SSRI/SNRI use or psychotherapy. The exposure was the patient's receipt of SSRI/SNRI (identified from self-report questionnaires) or psychotherapy (identified from the Outpatient Visits or Office-Based Medical Provider Visits files). The outcome was a new diagnosis of dementia within 2 years (i.e., survey panel period) identified using ICD-9/ICD-10 codes from the Medical Conditions file. Using a multivariable logistic regression model, we reported adjusted odds ratios (aORs) with 95% confidence intervals (CIs). We also conducted subgroup analyses by patient sex, age group, race/ethnicity, severity of depression, combined use of other non-SSRI/SNRI antidepressants, and presence of underlying cognitive impairment. RESULTS: Among 2,710 eligible patients (mean age = 61 ± 8, female = 69%, White = 84%), 89% used SSRIs/SNRIs, and 11% received psychotherapy. The SSRI/SNRI users had a higher crude incidence of dementia than the psychotherapy group (16.4% vs. 11.8%), with an aOR of 1.36 (95% CI = 1.06-1.74). Subgroup analyses yielded similar findings as the main analyses, except no significant association for patients who were aged < 65 years (1.23, 95% CI = 0.93-1.62), male (1.34, 95% CI = 0.95-1.90), Black (0.76, 95% CI = 0.48-1.19), had a higher PHQ-2 (1.39, 95% CI = 0.90-2.15), and had underlying cognitive impairment (1.06, 95% CI = 0.80-1.42). CONCLUSIONS: Our findings suggested that older adults with depression receiving SSRIs/SNRIs were associated with an increased dementia risk compared to those receiving psychotherapy.

Prioritization of risk genes for Alzheimer's disease: an analysis framework using spatial and temporal gene expression data in the human brain based on support vector machine.

Background: Alzheimer's disease (AD) is a complex disorder, and its risk is influenced by multiple genetic and environmental factors. In this study, an AD risk gene prediction framework based on spatial and temporal features of gene expression data (STGE) was proposed. Methods: We proposed an AD risk gene prediction framework based on spatial and temporal features of gene expression data. The gene expression data of providers of different tissues and ages were used as model features. Human genes were classified as AD risk or non-risk sets based on information extracted from relevant databases. Support vector machine (SVM) models were constructed to capture the expression patterns of genes believed to contribute to the risk of AD. Results: The recursive feature elimination (RFE) method was utilized for feature selection. Data for 64 tissue-age features were obtained before feature selection, and this number was reduced to 19 after RFE was performed. The SVM models were built and evaluated using 19 selected and full features. The area under curve (AUC) values for the SVM model based on 19 selected features (0.740 [0.690-0.790]) and full feature sets (0.730 [0.678-0.769]) were very similar. Fifteen genes predicted to be risk genes for AD with a probability greater than 90% were obtained. Conclusion: The newly proposed framework performed comparably to previous prediction methods based on protein-protein interaction (PPI) network properties. A list of 15 candidate genes for AD risk was also generated to provide data support for further studies on the genetic etiology of AD.

Association between first-line antidepressant use and risk of dementia in older adults: a retrospective cohort study.

Background: Prior studies suggested that antidepressant use is associated with an increased risk of dementia compared to no use, which is subject to confounding by indication. We aimed to compare the dementia risk among older adults with depression receiving first-line antidepressants (i.e., SSRI/SNRI) versus psychotherapy, which is also considered the first-line therapy for depression. Methods: This retrospective cohort study was conducted using the US Medical Expenditure Panel Survey from 2010 to 2019. We included adults aged ≥50 years diagnosed with depression who initiated SSRI/SNRI or psychotherapy. We excluded patients with a dementia diagnosis before the first record of SSRI/SNRI use or psychotherapy. The exposure was the patient's receipt of SSRI/SNRI (identified from self-report questionnaires) or psychotherapy (identified from the Outpatient Visits or Office-Based Medical Provider Visits files). The outcome was a new diagnosis of dementia within 2 years (i.e., survey panel period) identified using ICD-9/ICD-10 codes from the Medical Conditions file. Using a multivariable logistic regression model, we reported adjusted odds ratios (aORs) with 95% confidence intervals (CIs). We also conducted subgroup analyses by patient sex, age group, race, severity of depression, combined use of other non-SSRI/SNRI antidepressants, and presence of underlying cognitive impairment. Results: Among 2,710 eligible patients (mean age= 61±8, female=69%, white=84%), 89% used SSRIs/SNRIs, and 11% received psychotherapy. The SSRI/SNRI users had a higher crude incidence of dementia than the psychotherapy group (16.1% vs. 12.7%), with an aOR of 1.39 (95% CI=1.21-1.59). Subgroup analyses yielded similar findings as the main analyses, except no significant association for patients who were black (0.75, 95% CI=0.55-1.02), had a higher PHQ-2 (1.08, 95% CI=0.82-1.41), had concomitant non-SSRI/SNRI antidepressants (0.75, 95% CI=0.34-1.66), and had underlying cognitive impairment (0.84, 95% CI=0.66-1.05). Conclusions: Our findings suggested that older adults with depression receiving SSRIs/SNRIs were associated with an increased dementia risk compared to those receiving psychotherapy.

Aphid cluster recognition and detection in the wild using deep learning models.

Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community.

Low-dose antimony exposure promotes prostate cancer proliferation by inhibiting ferroptosis via activation of the Nrf2-SLC7A11-GPX4 pathway.

Antimony (Sb) is a typical environmental pollutant. With the development of industrialization, antimony is widely used in daily life and enters the human body through the food chain, water source, air pollution, and other channels. The risk of antimony exposure has emerged as one of the public's major health concerns. Current research on antimony shows that antimony has certain biological toxicity, and antimony exposure may be one of the carcinogenic risk factors for bladder cancer, prostate cancer (PCa), and other cancers. But the molecular mechanism of antimony exposure in PCa is still unclear. Our results showed that serum antimony levels were significantly higher in PCa patients than in benign prostatic hyperplasia (BPH), and high levels of serum antimony were associated with poorer prognosis in PCa. We demonstrate that antimony exposure promotes PCa progression in vivo and in vitro. In addition, our results also showed that low-dose antimony exposure resulted in increased GSH, increased GPX4 expression, and decreased Fe2+. Since GPX4 and Fe2+ are important molecular features in the mechanism of ferroptosis, we further found that low-dose antimony exposure can inhibit RSL3-induced ferroptosis and promote PCa proliferation. Finally, our study demonstrates that low-dose antimony exposure promotes Nrf2 expression, increases the expression level of SLC7A11, and then increases the expression of GPX4, inhibits ferroptosis, and promotes PCa progression. Taken together, our experimental results suggest that low-dose antimony exposure promotes PCa cell proliferation by inhibiting ferroptosis through activation of the Nrf2-SLC7A11-GPX4 pathway. These findings highlight the link between low-dose antimony exposure and the Nrf2-SLC7A11-GPX4 ferroptosis pathway, providing a new potential direction for the prevention and treatment of PCa.

Identification of PROK2 gene polymorphisms as predictors of methamphetamine use disorder risk and indicators of craving scale in the Chinese Han population.

Background: Methamphetamine use disorder (MUD) has become a global problem due to the highly addictive nature of methamphetamine. Earlier research have demonstrated that PROK2 functions as a compensatory and protective response against neurotoxic stress by stimulating astrocyte reactivity. The aim of our study was to evaluate the correlation between the PROK2 gene and both MUD risk susceptibility and craving scale in the Chinese Han population. Methods: A total of 5,282 participants (1,796 MUD patients and 3,486 controls) were recruited. Seven tag SNPs of the PROK2 gene were chosen and genotyped in the samples. Genetic association analyses were performed to capture the significant SNPs. To investigate the relationship between PROK2 levels and craving scores with the associated-SNP genotypes, we conducted a linear model. Results: SNP rs75433452 was significantly linked with MUD risk (p-value = 1.54 × 10-8), with the A allele being positively correlated with an increased risk of MUD. Moreover, the average serum level of PROK2 decreased when more copies of the A allele were presented in both MUD patients (p-value = 4.57 × 10-6) and controls (p-value = 1.13 × 10-5). Furthermore, the genotypes of SNP rs75433452 were strongly correlated with the craving scores in MUD patients (p-value = 4.05 × 10-4). Conclusion: Our study identified a significant association signal of the PROK2 gene with MUD risk susceptibility and methamphetamine craving scores in the Chinese Han population, providing potential valuable insights into the underlying mechanisms of METH dependence.

Diphtheria toxin-derived, anti-PD-1 immunotoxin, a potent and practical tool to selectively deplete PD-1+ cells.

Programmed death-1 (PD-1), an immune checkpoint receptor, is expressed on activated lymphocytes, macrophages, and some types of tumor cells. While PD-1+ cells have been implicated in outcomes of cancer immunity, autoimmunity, and chronic infections, the exact roles of these cells in various physiological and pathological processes remain elusive. Molecules that target and deplete PD-1+ cells would be instrumental in defining the roles unambiguously. Previously, an immunotoxin has been generated for the depletion of PD-1+ cells though its usage is impeded by its low production yield. Thus, a more practical molecular tool is desired to deplete PD-1+ cells and to examine functions of these cells. We designed and generated a novel anti-PD1 diphtheria immunotoxin, termed PD-1 DIT, targeting PD-1+ cells. PD-1 DIT is comprised of two single chain variable fragments (scFv) derived from an anti-PD-1 antibody, coupled with the catalytic and translocation domains of the diphtheria toxin. PD-1 DIT was produced using a yeast expression system that has been engineered to efficiently produce protein toxins. The yield of PD-1 DIT reached 1-2 mg/L culture, which is 10 times higher than the previously reported immunotoxin. Flow cytometry and confocal microscopy analyses confirmed that PD-1 DIT specifically binds to and enters PD-1+ cells. The binding avidities between PD-1 DIT and two PD-1+ cell lines are approximately 25 nM. Moreover, PD-1 DIT demonstrated potent cytotoxicity toward PD-1+ cells, with a half maximal effective concentration (EC50 ) value of 1 nM. In vivo experiments further showed that PD-1 DIT effectively depleted PD-1+ cells and enabled mice inoculated with PD-1+ tumor cells to survive throughout the study. Our findings using PD-1 DIT revealed the critical role of pancreatic PD-1+ T cells in the development of type-1 diabetes (T1D). Additionally, we observed that PD-1 DIT treatment ameliorated relapsing-remitting experimental autoimmune encephalomyelitis (RR-EAE), a mouse model of relapsing-remitting multiple sclerosis (RR-MS). Lastly, we did not observe significant hepatotoxicity in mice treated with PD-1 DIT, which had been reported for other immunotoxins derived from the diphtheria toxin. With its remarkable selective and potent cytotoxicity toward PD-1+ cells, coupled with its high production yield, PD-1 DIT emerges as a powerful biotechnological tool for elucidating the physiological roles of PD-1+ cells. Furthermore, the potential of PD-1 DIT to be developed into a novel therapeutic agent becomes evident.

B1 mapping using pre-learned subspaces for quantitative brain imaging.

PURPOSE: To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging. THEORY AND METHODS: A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data. RESULTS: In both phantom and healthy subject data, the proposed method produced high-quality B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues. CONCLUSION: This work presents a novel method to estimate B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.

Establishment of a potent weighted risk model for determining the progression of diabetic kidney disease.

BACKGROUND: Diabetic kidney disease (DKD) is a severe complication of diabetes. Currently, no effective measures are available to reduce the risk of DKD progression. This study aimed to establish a weighted risk model to determine DKD progression and provide effective treatment strategies. METHODS: This was a hospital-based, cross-sectional study. A total of 1104 patients with DKD were included in this study. The random forest method was used to develop weighted risk models to assess DKD progression. Receiver operating characteristic curves were used to validate the models and calculate the optimal cutoff values for important risk factors. RESULTS: We developed potent weighted risk models to evaluate DKD progression. The top six risk factors for DKD progression to chronic kidney disease were hemoglobin, hemoglobin A1c (HbA1c), serum uric acid (SUA), plasma fibrinogen, serum albumin, and neutrophil percentage. The top six risk factors for determining DKD progression to dialysis were hemoglobin, HbA1c, neutrophil percentage, serum albumin, duration of diabetes, and plasma fibrinogen level. Furthermore, the optimal cutoff values of hemoglobin and HbA1c for determining DKD progression were 112 g/L and 7.2%, respectively. CONCLUSION: We developed potent weighted risk models for DKD progression that can be employed to formulate precise therapeutic strategies. Monitoring and controlling combined risk factors and prioritizing interventions for key risk factors may help reduce the risk of DKD progression.

Gender, Smoking History, and Age Prediction from Laryngeal Images.

Flexible laryngoscopy is commonly performed by otolaryngologists to detect laryngeal diseases and to recognize potentially malignant lesions. Recently, researchers have introduced machine learning techniques to facilitate automated diagnosis using laryngeal images and achieved promising results. The diagnostic performance can be improved when patients' demographic information is incorporated into models. However, the manual entry of patient data is time-consuming for clinicians. In this study, we made the first endeavor to employ deep learning models to predict patient demographic information to improve the detector model's performance. The overall accuracy for gender, smoking history, and age was 85.5%, 65.2%, and 75.9%, respectively. We also created a new laryngoscopic image set for the machine learning study and benchmarked the performance of eight classical deep learning models based on CNNs and Transformers. The results can be integrated into current learning models to improve their performance by incorporating the patient's demographic information.

Interpretable Computer Vision to Detect and Classify Structural Laryngeal Lesions in Digital Flexible Laryngoscopic Images.

OBJECTIVE: To localize structural laryngeal lesions within digital flexible laryngoscopic images and to classify them as benign or suspicious for malignancy using state-of-the-art computer vision detection models. STUDY DESIGN: Cross-sectional diagnostic study SETTING: Tertiary care voice clinic METHODS: Digital stroboscopic videos, demographic and clinical data were collected from patients evaluated for a structural laryngeal lesion. Laryngoscopic images were extracted from videos and manually labeled with bounding boxes encompassing the lesion. Four detection models were employed to simultaneously localize and classify structural laryngeal lesions in laryngoscopic images. Classification accuracy, intersection over union (IoU) and mean average precision (mAP) were evaluated as measures of classification, localization, and overall performance, respectively. RESULTS: In total, 8,172 images from 147 patients were included in the laryngeal image dataset. Classification accuracy was 88.5 for individual laryngeal images and increased to 92.0 when all images belonging to the same sequence (video) were considered. Mean average precision across all four detection models was 50.1 using an IoU threshold of 0.5 to determine successful localization. CONCLUSION: Results of this study showed that deep neural network-based detection models trained using a labeled dataset of digital laryngeal images have the potential to classify structural laryngeal lesions as benign or suspicious for malignancy and to localize them within an image. This approach provides valuable insight into which part of the image was used by the model to determine a diagnosis, allowing clinicians to independently evaluate models' predictions.

Pulsed electromagnetic fields as a promising therapy for glucocorticoid-induced osteoporosis.

Glucocorticoid-induced osteoporosis (GIOP) is considered the third type of osteoporosis and is accompanied by high morbidity and mortality. Long-term usage of glucocorticoids (GCs) causes worsened bone quality and low bone mass via their effects on bone cells. Currently, there are various clinical pharmacological treatments to regulate bone mass and skeletal health. Pulsed electromagnetic fields (PEMFs) are applied to treat patients suffering from delayed fracture healing and non-unions. PEMFs may be considered a potential and side-effect-free therapy for GIOP. PEMFs inhibit osteoclastogenesis, stimulate osteoblastogenesis, and affect the activity of bone marrow mesenchymal stem cells (BMSCs), osteocytes and blood vessels, ultimately leading to the retention of bone mass and strength. However, the underlying signaling pathways via which PEMFs influence GIOP remain unclear. This review attempts to summarize the underlying cellular mechanisms of GIOP. Furthermore, recent advances showing that PEMFs affect bone cells are discussed. Finally, we discuss the possibility of using PEMFs as therapy for GIOP.

The effect of sodium hyaluronate on tear film stability in patients with dry eye syndrome after cataract surgery.

BACKGROUND: This study aimed to observe the changes in the ocular surface after phacoemulsification in patients with age-related cataracts with respect to the addition of varying concentrations of hyaluronate. METHODS: Patients with dry eye syndrome were treated with 0.3% and 0.1% sodium hyaluronate eye drops to evaluate the clinical improvement in each treatment group. A total of 73 patients (91 eyes) with age-related cataracts suffering from dry eye syndrome after phacoemulsification were divided into treatment group A (30 eyes), undergoing conventional therapy and treatment with 0.3% sodium hyaluronate; treatment group B (31 eyes), undergoing conventional therapy and treatment with 0.1% sodium hyaluronate; and the control group (group C; 30 eyes), undergoing conventional therapy only. Two groups were given different concentrations of sodium hyaluronate eye drops four times a day (should be completed between 8 AM and 8 PM), one drop at a time. RESULTS: Seven days, 2 weeks, 1 month, and 2 months postoperatively, there were significant differences in the Schirmer I test (SIt), first noninvasive tear film break-up time (NIBUTf), average noninvasive tear film break-up time (NIBUTav), tear meniscus height (TMH), and irregularity (when the refractive force of different parts of different meridians on the same meridian is different. The main manifestation is that the two meridians on the anterior surface of the cornea do not show a 90-degree vertical distribution, which cannot be corrected by conventional astigmatism lenses) between the three groups (p < 0.05). When compared with group C, there were significant differences in the SIt, NIBUTf, NIBUTav, TMH, and irregularity of group A and group B (p < 0.05). When compared with group B, there were significant improvements in the SIt, NIBUTf, NIBUTav, and TMH in group A (p < 0.05). CONCLUSIONS: In the early stage after phacoemulsification, the stability of the tear film is reduced. Adding sodium hyaluronate eye drops can restore tear film structure and improve corneal surface regularity, and a 0.3% solution of sodium hyaluronate eye drops is more effective than a 0.1% solution.

Characteristics of cerebral blood flow in an Eastern sample of multiple sclerosis patients: A potential quantitative imaging marker associated with disease severity.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that is rare in China. At present, there are no widespread quantitative imaging markers associated with disease severity in MS. Despite several previous studies reporting cerebral blood flow (CBF) changes in MS, no consensus has been reached. In this study, we enrolled 30 Eastern MS patients to investigate CBF changes in different brain regions using the arterial spin labeling technique and their relationship with disease severity. The average CBF in MS patients were higher than those in health controls in various brain regions except cerebellum. The results indicated that MS patients with strongly increased CBF showed worse disease severity, including higher Expanded Disability Status Scale (EDSS) scores and serum neurofilament light chain (sNfL) values than those with mildly increased CBF in the parietal lobes, temporal lobes, basal ganglia, and damaged white matter (DWM). From another perspective, MS patients with worse disease severity (higher EDSS score and sNfL values, longer disease duration) showed increased CBF in parietal lobes, temporal lobes, basal ganglia, normal-appearing white matter (NAWM), and DWM. Correlation analysis showed that there was a strong association among CBF, EDSS score and sNfL. MS patients with strongly increased CBF in various brain regions had more ratio in relapsing phase than patients with mildly increased CBF. And relapsing patients showed significantly higher CBF in some regions (temporal lobes, left basal ganglia, right NAWM) compared to remitting patients. In addition, MS patients with cognitive impairment had higher CBF than those without cognitive impairment in the right parietal lobe and NAWM. However, there were no significant differences in CBF between MS patients with and without other neurologic dysfunctions (e.g., motor impairment, visual disturbance, sensory dysfunction). These findings expand our understanding of CBF in MS and imply that CBF could be a potential quantitative imaging marker associated with disease severity.

Inflammaging: The ground for sarcopenia?

Sarcopenia is a progressive skeletal muscle disease that occurs most commonly in the elderly population, contributing to increased costs and hospitalization. Exercise and nutritional therapy have been proven to be effective for sarcopenia, and some drugs can also alleviate declines in muscle mass and function due to sarcopenia. However, there is no specific pharmacological treatment for sarcopenia at present. This review will mainly discuss the relationship between inflammaging and sarcopenia. The increased secretion of proinflammatory cytokines with aging may be because of cellular senescence, immunosenescence, alterations in adipose tissue, damage-associated molecular patterns (DAMPs), and gut microbes due to aging. These sources of inflammaging can impact the sarcopenia process through direct or indirect pathways. Conversely, sarcopenia can also aggravate the process of inflammaging, creating a vicious cycle. Targeting sources of inflammaging can influence muscle function, which could be considered a therapeutic target for sarcopenia. Moreover, not only proinflammatory cytokines but also anti-inflammatory cytokines can influence muscle and inflammation and participate in the progression of sarcopenia. This review focuses on the effects of TNF-α, IL-6, and IL-10, which can be detected in plasma. Therefore, clearing chronic inflammation by targeting proinflammatory cytokines (TNF-α, IL-1, IL-6) and the inflammatory pathway (JAK/STAT, autophagy, NF-κB) may be effective in treating sarcopenia.

Evaluation of Adenosine A2A receptor gene polymorphisms as risk factors of methamphetamine use disorder susceptibility and predictors of craving degree.

The adenosine A2A receptor (ADORA2A) is highly expressed in the central nervous system and plays vital roles in drug addiction. In this study, we aimed to explore the susceptibility of ADORA2A to methamphetamine use disorder (MUD) and the craving degree based on a two-stage association analysis. A total of 3,542 (1,216 patients with MUD and 2,326 controls) and 1,740 participants (580 patients with MUD and 1,160 controls) were recruited in discovery and replication stage, respectively. Significant SNPs identified in the discovery stage were genotyped in the replication samples. Serum levels of ADORA2A were measured using enzyme-linked immunosorbent assay kits. The genetic association signal of each SNP was examined using Plink. A linear model was fitted to investigate the relationship between craving scores and genotypes of significant SNPs. SNP rs5751876 was significantly associated with MUD in the discovery samples and this association signal was then further replicated in the replication samples. Significant associations were also identified between serum levels of ADORA2A and the genotypes of rs5751876 (P = 0.0002). The craving scores in patients with MUD were strongly correlated with rs5751876 genotypes. Our results suggest that polymorphisms of the ADORA2A gene could affect the susceptibility to MUD and its craving degree.

Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs.

Label assignment plays a significant role in modern object detection models. Detection models may yield totally different performances with different label assignment strategies. For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold. Early object detectors simply utilize the fixed threshold for all training samples, while recent detection algorithms focus on adaptive thresholds based on the distribution of the IoUs to the ground truth boxes. In this paper, we introduce a simple while effective approach to perform label assignment dynamically based on the training status with predictions. By introducing the predictions in label assignment, more high-quality samples with higher IoUs to the ground truth objects are selected as the positive samples, which could reduce the discrepancy between the classification scores and the IoU scores, and generate more high-quality boundary boxes. Our approach shows improvements in the performance of the detection models with the adaptive label assignment algorithm and lower bounding box losses for those positive samples, indicating more samples with higher-quality predicted boxes are selected as positives.