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The development of a highly selective and sensitive method for detecting chlortetracycline (CTC) is crucial for safeguarding public health and food safety. Herein, a novel ratiometric fluorescence sensor called SiC@ZIF-8@MIP was constructed to specifically recognize and sensitively detect CTC. The sensor has the advantages of fast response speed (7 min), wide linear range (0.1-18 µg mL-1), and low limit of detection (4.56 ng mL-1). With the addition of CTC, the fluorescence of SiC@ZIF-8@MIP is quenched at 410 nm due to the internal filtration effect (IFE) and a new fluorescence signal is generated at 515 nm by CTC due to the aggregation induced emission effect (AIE). Additionally, for rapid on-site detection of CTC, a smartphone is applied to digitize fluorescence images of SiC@ZIF-8@MIP, helping individuals read and analyze the images. This detection method is a promising strategy for on-site assessments of food safety and public health safety.
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Background: Angiotensin II receptor blockers (ARBs) are utilized for the management of hypertension and diabetes. Previous meta-analyses suggested that azilsartan medoxomil (AZL-M) improved blood pressure (BP) reduction, but there were no safety findings or suggestions for patients with hypertension or diabetes. Methods: We performed an efficacy and safety meta-analysis of randomized controlled trials (RCTs) evaluating AZL-M therapy for reducing BP in patients with hypertension. Patients with hypertension complicated by diabetes were analyzed. The relevant literature was searched in English and Chinese databases for RCTs involving AZL-M in hypertension. Efficacy variables included the change from baseline in the 24-h mean systolic/diastolic BP measured by ambulatory BP monitoring, the change from baseline in clinic systolic/diastolic BP, and responder rates. Safety variables included total adverse events (AEs), serious AEs, AEs leading to discontinuation, and AEs related to the study drug. The raw data from the included studies were utilized to calculate the odds ratio (OR) for dichotomous data and the mean difference (MD) for continuous data, accompanied by 95% confidence intervals (CIs). Statistical analysis was performed using R software. Results: A total of 11 RCTs met the inclusion criteria, representing 7,608 patients, 5 of whom had diabetes. Pooled analysis suggested a reduction in BP among patients randomized to 40â mg of AZL-M vs. control therapy [24-h ambulatory blood pressure monitoring (ABPM) mean systolic blood pressure (SBP) (MD: -2.85â mmHg), clinic SBP (MD: -3.48â mmHg), and clinic diastolic blood pressure (DBP) (MD: -1.96â mmHg)] and for 80â mg of AZL-M vs. control therapy [24-h ABPM mean SBP (MD: -3.59â mmHg), 24-h ABPM mean DBP (MD: -2.62â mmHg), clinic SBP (MD: -4.42â mmHg), clinic DBP (MD: -3.09â mmHg), and responder rate (OR: 1.46)]. There was no difference in the reduction of risks, except for dizziness (OR: 1.56) in the 80-mg AZL-M group or urinary tract infection (OR: 1.82) in the 40-mg AZL-M group. Analysis of patients with diabetes revealed that AZL-M can provide superior management, while safety and tolerability were similar to those of control therapy. Conclusions: AZL-M appears to reduce BP to a greater extent than dose-control therapy and does not increase the risk of adverse events in patients with hypertension and diabetes compared with placebo. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=464284, identifier PROSPERO CRD42023464284.
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Objective: To systematically review the efficacy and safety of nonsteroidal mineralocorticoid receptor antagonists (MRAs) in chronic kidney disease (CKD). Methods: We systematically searched six databases to identify randomized controlled trials (RCTs) about nonsteroidal MRAs for CKD, from inception to 22 August 2023. Two reviewers independently screened the retrieved articles, extracted data, and assessed the risk of bias of included RCTs using the Cochrane risk of bias tool. We then conducted meta-analysis of the data using Stata 17.0 software. Results: 11 RCTs (n = 15,817) were included in this meta-analysis. Compared with placebo, nonsteroidal MRAs significantly reduced the proportion of patients with ≥40% decline in estimated glomerular filtration rate (eGFR) from baseline [RR = 0.85, 95% CI (0.78, 0.92), p < 0.001], although the magnitude of eGFR reduction was greater [WMD = -2.83, 95% CI (-3.95, -1.72), p < 0.001]. The experimental group also had lower incidence of composite renal outcome [RR = 0.86, 95% CI (0.79, 0.93), p < 0.001] and greater reduction in urine albumin-to-creatinine ratio (UACR) from baseline [WMD = -0.41, 95% CI (-0.49, -0.32), p < 0.001], as well as reduced cardiovascular events [RR = 0.88, 95% CI (0.80, 0.95), p = 0.003]. MRAs did not increase any adverse events compared to placebo [RR = 1.00, 95% CI (0.99, 1.01), p = 0.909], but had higher incidence of hyperkalemia [RR = 2.05, 95% CI (1.85, 2.280), p < 0.001]. Compared with eplerenone, there was no significant difference in the proportion of patients with ≥40% decline in eGFR [RR = 0.57, 95% CI (0.18, 1.79), p = 0.335] or hyperkalemia [RR = 0.95, 95%CI (0.48, 1.88), p = 0.875]. Conclusion: Nonsteroidal MRAs can reduce the incidence of end-stage renal disease and cardiovascular adverse events in patients. Although there was still a risk of hyperkalemia compared to placebo, there was no significant difference in any adverse events compared to either placebo or eplerenone. It has become a new option for drug treatment of CKD patients, but more clinical trials are still needed to verify its efficacy and safety. Especially further direct comparison of the nonsteroidal MRAs to eplerenone in view of the relatively small number of patients reviewed are needed.
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BACKGROUND: Patient-based real-time quality control (PBRTQC) has gained attention because of its potential to continuously monitor the analytical quality in situations wherein internal quality control (IQC) is less effective. Therefore, we tried to investigate the application of PBRTQC method based on an artificial intelligence monitoring (AI-MA) platform in quality risk monitoring of Down syndrome (DS) serum screening. METHODS: The DS serum screening item determination data and relative IQC data from January 4 to September 7 in 2021 were collected. Then, PBRTQC exponentially weighted moving average (EWMA) and moving average (MA) procedures were built and optimized in the AI-MA platform. The efficiency of the EWMA and MA procedures with intelligent and traditional control rules were compared. Next, the optimal EWMA procedures that contributed to the quality assurance of serum screening were run and generated early warning cases were investigated. RESULTS: Optimal EWMA and MA procedures on the AI-MA platform were built. Comparison results showed the EWMA procedure with intelligent QC rules but not traditional quality rules contained the best efficiency. Based on the AI-MA platform, two early warning cases were generated by using the optimal EWMA procedure, which finally found were caused by instrument failure. Moreover, the EWMA procedure could truly reflect the detection accuracy and quality in situations wherein traditional IQC products were unstable or concentrations were inappropriate. CONCLUSIONS: The EWMA procedure built by the AI-MA platform could be a good complementary control tool for the DS serum screening by truly and timely reflecting the detection quality risks.
Subject(s)
Artificial Intelligence , Down Syndrome , Humans , Down Syndrome/diagnosis , Quality ControlABSTRACT
Introduction: Due to the inefficiency and high cost of the current healthcare supply chain mode, in order to adapt to the great changes in the global economy and public health, it is urgent to choose an effective mode for sustainable development of healthcare supply chain. The aim of this paper is to use artificial intelligence systems to make intelligent decisions for healthcare supply chain mode selection. Methods: Firstly, according to the economic benefits, social benefits and environmental benefits of healthcare supply chain, this paper identifies different healthcare supply chain modes in combination with artificial intelligence technology. Secondly, this paper presents the intelligent choice optimization method of healthcare supply chain mode based on deep reinforcement learning algorithm. Finally, the effect of artificial intelligence in healthcare supply chain mode selection is verified by simulation experiment. Results and Discussion: The experimental results show that healthcare supply chain mode selected by artificial intelligence is basically consistent with the target mode, while healthcare supply chain mode selected by the basic selection method, BP neural network method and big data method is different from the target mode, which indicates that AI has more advantages in the selection of medical supply chain mode. Therefore, we recommend the application of artificial intelligence to healthcare supply chain management. This study not only makes up for the ineffective problems of existing methods, but also makes up for the gaps in the application of AI technology in the field of healthcare supply chain. The scientific value of this paper is that the proposed framework and the artificial intelligence algorithm enrich the relevant theories of healthcare supply chain research and provide methodological guidance for intelligent decision-making of healthcare supply chain. At the same time, for medical enterprises, this research provides a new practical guideline for the application of artificial intelligence in the sustainable development and modern management of healthcare supply chain.
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Algorithms , Artificial Intelligence , Neural Networks, Computer , Research , Computer SimulationABSTRACT
BACKGROUND: Autoimmune glial fibrillary acidic protein astrocytopathy (AGA) is a relatively novel disease. The early diagnosis of this inflammatory central nervous system (CNS) disorder remains challenging. OBJECTIVES: We aimed to explore the imaging manifestations and evolution of AGA to facilitate its successful diagnosis and monitoring. METHODS: From January 2016 to January 2021, a total of 15 consecutive patients, who were hospitalised in our institution and confirmed AGA clinically, were enrolled in this IRB-approved retrospective study. All clinical features and MRI manifestations were analysed cross-sectionally and longitudinally. Distribution, morphology, enhancement pattern and evolution of AGA lesions were assessed visually and evaluated semi-quantitatively by calculating the burden of disease (BOD) and the signal intensity ratio (SIR). Chi-square, Mann-Whitney U and Kolmogorov-Smirnov Z tests were performed for lesion patterns' statistical comparison. RESULTS: Fever, limb weakness, headache and cognitive impairment were the most common symptoms in AGA. 14 patients were initially misdiagnosed as infection (n = 9), demyelination (n = 4) or infarction (n = 1). The median time interval from onset to confirmed AGA diagnosis was 46 days. Both BOD and SIR progressed in the natural course and were relieved after immunotherapy on MRI. Meningeal enhancement, one of the most common MRI findings in patients with AGA (100%), relieved faster than intraparenchymal enhancement (p <0.001) and periventricular radial linear (PVRL) enhancement (p <0.001) after the initiation of immunotherapy. Non-enhanced lesions had lower BOD (p <0.001) and were relieved slower (p <0.001) than enhanced ones. CONCLUSIONS: MRI provides valuable neuroradiological indicators for the diagnosis and follow-up of AGA. The CNS enhancement patterns (meningeal / PVRL) facilitate the early diagnosis and treatment response monitoring of AGA, while lesion manifestation in the spinal cord contributes to the follow-up. The evolution inconsistency of AGA lesions in different regions may be attributed to the discrepancy within glial fibrillary acidic protein subtypes.
Subject(s)
Autoimmune Diseases of the Nervous System , Astrocytes/pathology , Autoantibodies/metabolism , Autoimmune Diseases of the Nervous System/diagnostic imaging , Autoimmune Diseases of the Nervous System/therapy , Cross-Sectional Studies , Glial Fibrillary Acidic Protein , Humans , Longitudinal Studies , Magnetic Resonance Imaging , Retrospective StudiesABSTRACT
BACKGROUND: Intracerebral hemorrhage (ICH), a severe disorder with the high death rate, high recurrence rate and high disability rate, affected the quality of human life. Community-based rehabilitation (CBR) helps disabled people at both community and family levels. However, the effect of CBR on the recovery of people after ICH remains unclear. METHODS: Patients were treated with the CBR training program, subsequently, medication compliance test, clinical neural impairment measurements, functional comprehensive assessments, improved Barthel index score, and life qualities assessments were to performed at 3-month or 6-month intervention of CBR to evaluate the influence of CBR on the medication compliance, physical function and life quality of patients after ICH. RESULTS: After the treatment of CBR, we observed that, the rate of medication compliance, motor function, functional comprehensive rating scale score, modified Barthel index score, and generic quality of life inventory-74 in the CBR-treated group were significantly higher than that in the control group; the neural impairment measure score in the CBR-treated group was significantly decreased in comparison to the control group. CONCLUSION: CBR increased the medication compliance, promoted the recovery of the neurological function and improved the life qualities of ICH patients.
Subject(s)
Disabled Persons , Quality of Life , Cerebral Hemorrhage , Disabled Persons/rehabilitation , Humans , Recovery of FunctionABSTRACT
Background: Conventional gadolinium (Gd)-enhanced MRI is currently used for stratifying the lesion activity of multiple sclerosis (MS) despite limited correlation with disability and disease activity. The stratification of MS lesion activity needs further improvement to better support clinics. Purpose: To investigate if the novel proton exchange rate (k ex ) MRI combined with quantitative susceptibility mapping (QSM) may help to further stratify non-enhanced (Gd-negative) MS lesions. Materials and methods: From December 2017 to December 2020, clinically diagnosed relapsing-remitting MS patients who underwent MRI were consecutively enrolled in this IRB-approved retrospective study. The customized MRI protocol covered conventional T2-weighted, T2-fluid-attenuated-inversion-recovery, pre- and post-contrast T1-weighted imaging, and quantitative sequences, including k ex MRI based on direct-saturation removed omega plots and QSM. Each MS lesion was evaluated based on its Gd-enhancement as well as its susceptibility and k ex elevation compared to the normal appearing white matter. The difference and correlation concerning lesion characteristics and imaging contrasts were analyzed using the Mann-Whitney U test or Kruskal-Wallis test, and Spearman rank analysis with p < 0.05 considered significant. Results: A total of 322 MS lesions from 30 patients were identified with 153 Gd-enhanced and 169 non-enhanced lesions. We found that the k ex elevation of all lesions significantly correlated with their susceptibility elevation (r = 0.30, p < 0.001). Within the 153 MS lesions with Gd-enhancement, ring-enhanced lesions showed higher k ex elevation than the nodular-enhanced ones' (p < 0.001). Similarly, lesions with ring-hyperintensity in QSM also had higher k ex elevation than the lesions with nodular-QSM-hyperintensity (p < 0.001). Of the 169 Gd-negative lesions, three radiological patterns were recognized according to lesion manifestations on the k ex map and QSM images: Pattern I (k ex + and QSM+, n = 114, 67.5%), Pattern II (only k ex + or QSM+, n = 47, 27.8%) and Pattern III (k ex - and QSM-, n = 8, 4.7%). Compared to Pattern II and III, Pattern I had higher k ex (p < 0.001) and susceptibility (p < 0.05) elevation. The percentage of Pattern I of each subject was negatively correlated with the disease duration (r = -0.45, p = 0.015). Conclusion: As a potential imaging biomarker for inflammation due to oxidative stress, in vivo k ex MRI combined with QSM is promising in extending the clinical classification of MS lesions beyond conventional Gd-enhanced MRI.
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As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning system with deep learning techniques to predict COVID-19 malignant progression. Our method leverages CT scans and the clinical data of outpatients and achieves an AUC of 0.920 in the single-center study. We also propose a domain adaptation approach to improve the generalization of our model and achieve an average AUC of 0.874 in the multicenter study. Moreover, our model automatically identifies crucial indicators that contribute to the malignant progression, including Troponin, Brain natriuretic peptide, White cell count, Aspartate aminotransferase, Creatinine, and Hypersensitive C-reactive protein.
Subject(s)
COVID-19 , Deep Learning , Humans , SARS-CoV-2 , Tomography, X-Ray ComputedABSTRACT
Pleural fibrosis is defined as an excessive deposition of extracellular matrix that results in destruction of the normal pleural tissue architecture and compromised function. Tuberculous pleurisy, asbestos injury, and rheumatoid pleurisy are main causes of pleural fibrosis. Pleural mesothelial cells (PMCs) play a key role in pleural fibrosis. However, detailed mechanisms are poorly understood. Serine/arginine-rich protein SRSF6 belongs to a family of highly conserved RNA-binding splicing-factor proteins. Based on its known functions, SRSF6 should be expected to play a role in fibrotic diseases. However, the role of SRSF6 in pleural fibrosis remains unknown. In this study, SRSF6 protein was found to be increased in cells of tuberculous pleural effusions (TBPE) from patients, and decellularized TBPE, bleomycin, and TGF-ß1 were confirmed to increase SRSF6 levels in PMCs. In vitro, SRSF6 mediated PMC proliferation and synthesis of the main fibrotic protein COL1A2. In vivo, SRSF6 inhibition prevented mouse experimental pleural fibrosis. Finally, activated SMAD2/3, increased SOX4, and depressed miRNA-506-3p were associated with SRSF6 upregulation in PMCs. These observations support a model in which SRSF6 induces pleural fibrosis through a cluster pathway, including SRSF6/WNT5A and SRSF6/SMAD1/5/9 signaling. In conclusion, we propose inhibition of the splicing factor SRSF6 as a strategy for treatment of pleural fibrosis.
Subject(s)
Fibrosis/metabolism , Phosphoproteins , Pleura/metabolism , Pleural Diseases/metabolism , Serine-Arginine Splicing Factors , Animals , Humans , Male , Mice , Mice, Inbred C57BL , Phosphoproteins/genetics , Phosphoproteins/metabolism , Serine-Arginine Splicing Factors/genetics , Serine-Arginine Splicing Factors/metabolism , Signal TransductionABSTRACT
Brachial plexus avulsion (BPA), a severe acute peripheral nerve injury in adults, results in total loss of the motor function in the upper limb. Although immediate re-implantation surgery is widely performed to repair this lesion, the motor function cannot be fully restored. The main cause is that the growth velocity of axon is extremely slow in order to re-innervate the target muscles before atrophy develops. Therefore, the survival of spinal motoneurons (MNs) is considered to be a prerequisite for the recovery of motor function. The introduction of survival-proactive agents with anti-oxidative stress and anti-inflammation properties has emerged as a new approach to the motor function recovery following BPA. In the current review, we summarized the treatments of BPA in both mouse and rat models following re-implantation surgery. Furthermore, the pain treatment options following BPA were discussed.
Subject(s)
Brachial Plexus , Animals , Axons , Mice , Motor Neurons , Rats , Recovery of FunctionABSTRACT
BACKGROUND: Currently available radiological methods do not completely capture the diversity of multiple sclerosis (MS) lesion subtypes. This lack of information hampers the understanding of disease progression and potential treatment stratification. For example, inflammation persists in some lesions after gadolinium (Gd) enhancement resolves. Novel metabolic and molecular imaging methods may improve the current assessments of MS pathophysiology. PURPOSE: To compare the in vivo proton exchange rate (kex ) MRI with Gd-enhanced MRI for characterizing MS lesions. STUDY TYPE: Retrospective. SUBJECTS: Sixteen consecutively diagnosed relapsing-remitting multiple sclerosis (RRMS) patients. FIELD STRENGTH/SEQUENCE: 3.0T MRI with T2 -weighted imaging, postcontrast T1 -weighted imaging, and single-slice chemical exchange saturation transfer imaging. ASSESSMENT: MS lesions in white matter were assessed for Gd enhancement and kex elevation compared to normal-appearing white matter (NAWM). STATISTICAL TESTS: Student's t-test was used for analyzing the difference of kex values between lesions and NAWM, with statistical significance set at 0.05. RESULTS: Of all 153 MS lesions, 78 (51%) lesions were Gd-enhancing and 75 (49%) were Gd-negative. Without exception, all 78 Gd-enhancing lesions showed significantly elevated kex values compared to NAWM (924 ± 130 s-1 vs. 735 ± 61 s-1 , P < 0.05). Of 75 Gd-negative lesions, 18 lesions (24%) showed no kex elevation (762 ± 29 s-1 vs. 755 ± 28 s-1 , P = 0.47) and 57 (76%) showed significant kex elevation (950 ± 124 s-1 vs. 759 ± 48 s-1 , P < 0.05) compared to NAWM. MS lesions with kex elevation appeared nodular (118, 87.4%), ring-like (15, 11.1%), or irregular-shaped (2, 1.5%). DATA CONCLUSION: For Gd-enhancing lesions, kex MRI is highly consistent with Gd-enhanced images by showing 100% of elevated kex . For all Gd-negative lesions, the discrepancy on kex MRI may further differentiate active slowly expanding lesions or chronic inactive lesions, supporting kex as an imaging biomarker for tissue oxidative stress and inflammation. Level of Evidence 2 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:408-415.
Subject(s)
Multiple Sclerosis , Brain/diagnostic imaging , Gadolinium , Humans , Magnetic Resonance Imaging , Multiple Sclerosis/diagnostic imaging , Protons , Retrospective StudiesABSTRACT
OBJECTIVE: To evaluate temporal lung changes in coronavirus disease 2019 (COVID-19) in high-resolution computed tomography (HRCT) and to determine the appropriate computed tomographic (CT) follow-up time. METHODS: Eighty-six patients with two or more HRCT scans who were diagnosed with COVID-19 were included. The CT score and major CT findings were evaluated. RESULTS: Eighty-two (95.3%) patients had lesions on the initial HRCT scans. Most scans showed bilateral, multifocal lung lesions, with multiple lobes involved and diffuse distribution. For fifty-seven patients with type I (progress compared with the initial CT score), the CT score reached a peak at 12 days and the nadir at 36 days. For twenty-nine patients with type II (no progress compared with the initial CT score), the lowest CT score was reached at 23 days. On the final HRCT scans (>21 days), patients with a reticular pattern were older than those without a reticular pattern. CONCLUSION: The appropriate follow-up time of CT scans is during the second week (approximately 12 days) and the fourth to fifth weeks (approximately 23-36 days) from the onset of illness. These times could help reduce the CT radiation dose and show timely changes in the course of the disease by CT.