Objective:To detect the differences in types and levels of amino acids in the peripheral serum of patients with laryngeal squamous cell carcinoma and non-tumor patients, and explore their relationship with clinical parameters of laryngeal squamous cell carcinoma as well as their clinical value in diagnosis. Methods:High-performance liquid chromatography-tandem mass spectrometry（HPLC-MS） was employed to detect the serum amino acid contents and levels of 62 patients diagnosed with laryngeal carcinoma and 141 non-tumor patients at the First Affiliated Hospital of Jinzhou Medical University between September 2018 and February 2021. The study compared the differences in 22 non-essential and essential amino acids found in the serum between the experimental group and the control group. An ROC curve and risk scoring formula of multivariate linear logic regression model was utilized to evaluate the efficiency of serum amino acids in the early diagnosis of laryngeal carcinoma. Results:There were significant differences in the contents of fourteen types of amino acids between the experimental and control groups, with thirteen amino acids showing higher levels in the experimental group（P<0.05）. Seven of these amino acids were essential, including phenylalanine, threonine, leucine, valine, histidine, tyrosine, and citrulline. The other six amino acids were non-essential, including arginine, asparagine, cysteine, glycine, ornithine, and proline. Interestingly, the content of homocysteine in the experimental group was lower than that in the control group（P=0.024）. Further analysis showed that patients with laryngeal squamous cell carcinoma in TNM stage Ⅰ and Ⅱ had higher serum methionine levels compared to those in stages Ⅲ and Ⅳ（P=0.026）. In addition, the content of serum histidine was higher in patients with poorly differentiated squamous cell carcinoma compared to those with well-differentiated squamous cell carcinoma（P=0.041）. The level of asparagine in the serum of patients with laryngeal squamous cell carcinoma older than 64 years old was lower than that in patients younger than 64 years old（P=0.033）. The level of tryptophan in the serum of patients with a smoking history was lower than that in patients without a smoking history（P=0.033）. The level of citrulline in the serum of patients with a history of alcohol consumption was higher than that in patients with no history of alcohol consumption（P=0.003）. ROC curve analysis showed that out of the 14 different amino acids between the experimental and control groups, citrulline and cysteine were relatively effective as independent factors in the diagnosis of laryngeal squamous cell carcinoma, with an AUC of 0.856 and 0.850, respectively. Arginine was the most sensitive factor in the diagnosis of laryngeal squamous cell carcinoma（AUC=0.855）. However, citrulline alone had the highest specificity（0.830） in the diagnosis of laryngeal squamous cell carcinoma, and the combination of 12 amino acids significantly improved the diagnostic efficiency of laryngeal squamous cell carcinoma, with an AUC of 0.946, sensitivity of 0.887, and specificity of 0.894. A risk score formula for a multivariate logistic regression model was established based on the differential amino acid content in the serum. The risk score of laryngeal squamous cell carcinoma group was higher than that of the non-tumor group（P<0.001）. The AUC of risk score in the diagnosis of laryngeal squamous cell carcinoma was 0.953 with sensitivity and specificity of 0.957 and 0.855. Conclusion:This study found that there are differences in the contents of 14 amino acids among which 13 amino acids were increased in serum of patients with laryngeal squamous cell carcinoma, and were associated with age, clinical stage, pathological differentiation, smoking, and drinking. Combined detection of 12 amino acids can improve the diagnostic efficiency of laryngeal squamous cell carcinoma and serve as potential markers for the auxiliary diagnosis of laryngeal squamous cell carcinoma using peripheral blood samples. Additionally, the established risk score model was found to be more effective in the diagnosis of laryngeal squamous cell carcinoma, indicating its important potential value as an auxiliary diagnostic tool.
目的：检测喉鳞状细胞癌患者与非肿瘤患者外周血清中氨基酸种类及含量差异，探讨他们与喉鳞癌各临床参数之间的关系以及在诊断中的临床价值。 方法：采用高效液相色谱-串联质谱（HPLC-MS）法检测锦州医科大学附属第一医院2018年9月-2021年2月初诊的62例喉鳞癌患者（实验组）和同时期的141例非肿瘤患者（对照组）血清22种非必需氨基酸及必需氨基酸种类与含量差异，应用ROC曲线及多元线性逻辑回归模型对差异性氨基酸在喉鳞癌早期诊断中的效能进行分析。 结果：实验组较对照组血清中有14种氨基酸含量存在差异，其中13种氨基酸含量显著高于对照组（P<0.05），包括7种必需氨基酸：苯丙氨酸、苏氨酸、亮氨酸、缬氨酸、组氨酸、酪氨酸、瓜氨酸，以及6种非必需氨基酸：精氨酸、天冬酰胺、半胱氨酸、甘氨酸、鸟氨酸、脯氨酸。而同型半胱氨酸含量明显低于对照组（P＝0.024）。进一步分析表明临床早期（Ⅰ期+Ⅱ期）喉鳞癌患者血清中甲硫氨酸含量显著高于临床晚期（Ⅲ期+Ⅳ期）患者（P＝0.026）。高分化鳞状细胞癌患者血清中组氨酸含量显著高于低分化鳞状细胞癌患者（P=0.041）。年龄>64岁的喉鳞癌患者血清中天冬酰胺的含量高于≤64岁的喉鳞癌患者（P=0.033），有吸烟史的喉鳞癌患者血清中色氨酸含量均低于无吸烟史患者（P=0.033），有饮酒史的喉鳞癌患者血清中瓜氨酸含量高于无饮酒史的喉鳞癌患者（P=0.003）。ROC曲线分析表明，实验组与对照组间存在差异的14种氨基酸中，除同型半胱氨酸和鸟氨酸不具备统计学意义（P>0.05），瓜氨酸和半胱氨酸作为独立因素诊断喉鳞癌的效能相对较高（AUC数值分别为0.856及0.850）；而精氨酸作为单独诊断喉鳞癌因素时的灵敏度则最高（0.855）；但单独使用瓜氨酸诊断喉鳞癌时其特异度最高（0.830）；将12种氨基酸联合应用于检测可显著提高喉鳞癌的诊断效能，AUC为0.946，灵敏度为0.887，特异度为0.894。基于血清差异氨基酸含量建立多元线性逻辑回归模型的风险评分公式，喉鳞癌组的风险评分高于非肿瘤组（P<0.001），风险评分诊断喉鳞癌的AUC为0.953（敏感度和特异度分别为0.957和0.855）。 结论：喉鳞癌患者与非肿瘤患者比较，14种氨基酸的含量存在差异，其中13种氨基酸的含量在喉鳞癌患者血清中升高，并与年龄、临床分期、病理分化程度、吸烟以及饮酒具有相关性。联合其中12种氨基酸进行检测可提高喉鳞癌的诊断效能，具备外周血辅助诊断喉鳞癌的潜力。同时建立风险评分模型发现其对于喉鳞癌的诊断效能更高，表明该模型对于辅助诊断喉鳞癌有重要潜在价值。.

OBJECTIVE: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL).
METHODS: This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians\' readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts.
RESULTS: The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system\'s diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency.
CONCLUSIONS: The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention.
CONCLUSIONS: The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications.
CONCLUSIONS: The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS. This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan. 94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.

OBJECTIVE: To assess the accuracy of Raman spectroscopy in distinguishing between patients with leukemia and healthy individuals.
METHODS: PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases were searched for relevant articles published from inception of the respective database to November 1, 2023. The pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), were calculated along with their corresponding 95 % confidence intervals (CI). A summary comprehensive receiver operating characteristic curve (SROC) was constructed and the area under the curve (AUC) was calculated. The degree of heterogeneity was tested and analyzed.
RESULTS: Fifteen groups of original studies from 13 articles were included. The pooled SEN and SPE were 0.93 (95 % CI, [0.92 -0.93]) and 0.91(95 % CI, [0.90-0.92]), respectively. The DOR was 613.01 (95 %CI, [270.79-1387.75]), and the AUC was 0.99. The Deeks\' funnel plot asymmetry test indicated no significant publication bias among the included studies (bias coefficient, 40.80; P = 0.13 < 0.10). The meta-regression analysis findings indicated that the observed heterogeneity could be attributed to variations in sample categories and Raman spectroscopy techniques.
CONCLUSIONS: We confirmed that Raman spectroscopy has good accuracy in differentiating patients with leukemia from healthy individuals, and may become a means of leukemia screening in clinical practice. In the case of analysis based on live cells using surface-enhanced Raman spectroscopy (SERS) improved diagnostic efficacy was observed.

UNASSIGNED: Ever since the GALAD (gender-age-Lens culinaris agglutinin-reactive alpha-fetoprotein-alpha-fetoprotein-des-gamma-carboxy prothrombin) logistic regression model was established to diagnose hepatocellular carcinoma (HCC), there has been no high-level evidence that evaluates and summarizes it.
UNASSIGNED: This meta-analysis was performed to assess the diagnostic ability of the GALAD model.
UNASSIGNED: The following databases were systematically searched for original diagnostic studies on HCC: PubMed, Embase, Medline, the Web of Science, Cochrane Library, China National Knowledge Infrastructure Wanfang (China), Wiper and the Chinese BioMedical Literature Database. After screening the search results according to our criteria, the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to evaluate the methodologic qualities, and statistical software were used to output the statistics.
UNASSIGNED: Ultimately, 10 studies were included and analyzed. The results revealed the pooled sensitivity and specificity of the GALAD model to be 0.86 (95% confidence interval [CI]: 0.82, 0.90) and 0.90 (95% CI: 0.87, 0.92), respectively, for all-stage HCC. The area under the curve (AUC) was 0.94. For early-stage HCC, the pooled sensitivity and specificity of the GALAD model were 0.83 (95% CI: 0.78, 0.87) and 0.81 (95% CI: 0.78, 0.83), respectively. The AUC was 0.90.
UNASSIGNED: This meta-analysis confirmed that the GALAD model has excellent diagnostic performance for early-stage and all-stage HCC and can maintain high sensitivity and specificity in early-stage HCC. Therefore, the GALAD model is qualified for screening early-stage canceration from chronic liver disease.

OBJECTIVE: To explore the diagnostic value of retinol binding protein (RBP), C-reactive protein (CRP) and urine microalbumin (UMA) for ischemic cerebrovascular disease (ICD) in patients with chronic kidney disease (CKD).
METHODS: In this study, a total of 118 patients with CKD were selected and grouped into two groups: a group of patients who were complicated with ICD (CKD+ICD group, n=58), and a group of patients with CKD only (CKD group, n=60). Then, the patients in the CKD+ICD group were further classified into a good prognosis group and a bad prognosis group according their modified Rankin scale score at sixth months after discharge. Serum RBP, CRP and urine UMA levels were compared between the CKD group and CKD+ICD group. The diagnostic efficiency of serum RBP, CRP and urine UMA levels for ICD in patients with CKD was analyzed. The receiver operating characteristic (ROC) curve was used to assess their prognostic performance. Logistic regression analysis was used to evaluate the risk factors for poor prognosis of patients with CKD and ICD.
RESULTS: The levels of RBP, CRP, and UMA in the CKD+ICD group were significantly higher than those in the CKD group (all P<0.05). RBP demonstrated the highest diagnostic accuracy and sensitivity for ICD in CKD patients, while CRP and UMA exhibited equivalent specificity, surpassing that of RBP. ROC curves showed that the areas under the curve (AUCs) of RBP and CRP were significantly greater than that of UMA (P<0.05) and there was no significant difference for AUCs between RBP and CRP. In addition, the levels of RBP, CRP and UMA in the poor prognosis group were significantly higher than those in the good prognosis group (all P<0.05). Logistic regression analysis showed that RBP, CRP and UMA were independent risk factors for the poor prognosis of patients with CKD and ICD (Odds ratios =2.507, 3.677 and 1.919, respectively; all P<0.05).
CONCLUSIONS: The assessment of RBP, CRP and UMA is recommended for diagnosis of ICD in CKD patients. RBP, CRP and UMA are independent risk factors for poor prognosis of CKD patients with ICD.

BACKGROUND: Electrocardiography (ECG) and 24 hours Holter monitoring (24 h-Holter) provided valuable information for premature ventricular and supraventricular contractions (PVC and PSVC). Currently, artificial intelligence (AI) based 2 hours single-lead Holter (2 h-Holter) monitoring may provide an improved strategy for PSVC/PVC diagnosis.
OBJECTIVE: AI combined with single-lead Holter monitoring improves PSVC/PVC detection.
METHODS: In total, 170 patients were enrolled between August 2022 and 2023. All patients wore both devices simultaneously; then, we compared diagnostic efficiency, including the sensitivity/specificity/positive predictive-value (PPV) and negative predictive-value (NPV) in detecting PSVC/PVC by 24 h-Holter and 2 h-Holter.
RESULTS: The PPV and NPV in patients underwent 2 h-Holter were 76.00%/87.50% and 96.35%/98.55, respectively, and the sensitivity and specificity were 79.17%/91.30%, and 95.65%/97.84% in PSVC/PVC detection compared with 24 h-Holter. The areas under the ROC curves (AUCs) for PSVC and PVC were 0.885 and 0.741, respectively (p < .0001).
CONCLUSIONS: The potential advantages of the 2 h-Holter were shortened wearing period, improved convenience, and excellent consistency of diagnosis.

Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is a means to procure adequate specimens for histological and cytologic analysis. The ideal EUS-FNA should be safe, accurate, and have a high sample adequacy rate and low adverse events rate. In recent years, many guidelines and trials on EUS-FNA have been published. The purpose of this article is to provide an update on the influence of some of the main factors on the diagnostic efficiency of EUS-FNA as well as a rare but serious complication known as needle tract seeding.

UNASSIGNED: Neonatal screening for inherited metabolic diseases (IMDs) has been revolutionized by tandem mass spectrometry (MS/MS). This study aimed to enhance neonatal screening for IMDs using machine learning (ML) techniques.
UNASSIGNED: The study involved the analysis of a comprehensive dataset comprising 309,102 neonatal screening records collected in the Ningbo region, China. An advanced ML system model, encompassing nine distinct algorithms, was employed for the purpose of predicting the presence of 31 different IMDs. The model was compared with traditional cutoff schemes to assess its diagnostic efficacy. Additionally, 180 suspected positive cases underwent further evaluation.
UNASSIGNED: The ML system exhibited a significantly reduced positive rate, from 1.17% to 0.33%, compared to cutoff schemes in the initial screening, minimizing unnecessary recalls and associated stress. In suspected positive cases, the ML system identified 142 true positives with high sensitivity (93.42%) and improved specificity (78.57%) compared to the cutoff scheme. While false negatives emerged, particularly in heterozygous carriers, our study revealed the potential of the ML system to detect asymptomatic cases.
UNASSIGNED: This research provides valuable insights into the potential of ML in pediatric medicine for IMD diagnosis through neonatal screening, emphasizing the need for accurate carrier detection and further research in this domain.

OBJECTIVE: This study aimed to evaluate the clinical feasibility of early 30-minute dynamic 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) scanning protocol for patients with lung lesions in comparison to the standard 65-minute dynamic FDG-PET scanning as a reference.
METHODS: Dynamic 18F-FDG PET images of 146 patients with 181 lung lesions (including 146 lesions confirmed by histology) were analyzed in this prospective study. Dynamic images were reconstructed into 28 frames with a specific temporal division protocol for the scan data acquired 65 min post-injection. Ki images and quantitative parameters Ki based on two different acquisition durations [the first 30 min (Ki-30 min) and 65 min (Ki-65 min)] were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. The two acquisition durations were compared for Ki image quality (including visual score analysis and number of lesions detected) and Ki value (including accuracy of Ki, the value of differential diagnosis of lung lesions and prediction of PD-L1 status) by Wilcoxon\'s rank sum test, Spearman\'s rank correlation analysis, receiver operating characteristic (ROC) curve, and the DeLong test. The significant testing level (alpha) was set to 0.05.
RESULTS: The quality of the Ki-30 min images was not significantly different from the Ki-65 min images based on visual score analysis (P > 0.05). In terms of Ki value, among 181 lesions, Ki-65 min was statistically higher than Ki-30 min (0.027 ± 0.017 ml/g/min vs. 0.026 ± 0.018 ml/g/min, P < 0.05), while a very high correlation was obtained between Ki-65 min and Ki-30 min (r = 0.977, P < 0.05). In the differential diagnosis of lung lesions, ROC analysis was performed on 146 histologically confirmed lesions, the area under the curve (AUC) of Ki-65 min, Ki-30 min, and SUVmax was 0.816, 0.816, and 0.709, respectively. According to the Delong test, no significant differences in the diagnostic accuracies were found between Ki-65 min and Ki-30 min (P > 0.05), while the diagnostic accuracies of Ki-65 min and Ki-30 min were both significantly higher than that of SUVmax (P < 0.05). In 73 (NSCLC) lesions with definite PD-L1 expression results, the Ki-65 min, Ki-30 min, and SUVmax in PD-L1 positivity were significantly higher than that in PD-L1 negativity (P < 0.05). And no significant differences in predicting PD-L1 positivity were found among Ki-65 min, Ki-30 min, and SUVmax (AUC = 0.704, 0.695, and 0.737, respectively, P > 0.05), according to the results of ROC analysis and Delong test.
CONCLUSIONS: This study indicates that an early 30-minute dynamic FDG-PET acquisition appears to be sufficient to provide quantitative images with good-quality and accurate Ki values for the assessment of lung lesions and prediction of PD-L1 expression. Protocols with a shortened early 30-minute acquisition time may be considered for patients who have difficulty with prolonged acquisitions to improve the efficiency of clinical acquisitions.

Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.