Objective To evaluate the performance of two molecular point-of-care testing(POCT)prod-ucts in the diagnosis of influenza A virus(Flu A)and influenza B virus(Flu B)of clinical samples,and pre-liminarily evaluate the clinical diagnostic value of the changes of infection-related indicators in peripheral blood.Methods A total of 491 oropharyngeal swabs from patients with influenza-like symptoms who were treated in the hospital were recruited into this study from November 1,2019 to June 30,2023.These swabs were collected using reverse transcription real-time quantitative fluorescent polymerase chain reaction(RT-qPCR),and two POCT molecular products,XpertTM Xpress Flu/RSV and EasyNAT? Flu Assay,respectively.The diagnostic performance of two POCT molecular products was analyzed using RT-qPCR reaction as a standard.According to the results of RT-qPCR method,the subjects were divided into Flu A positive group,Flu B positive group and negative group(both Flu A and Flu B were negative).The levels of indicators in pe-ripheral blood of the three groups were compared to evaluate the value of these indicators in the clinical diag-nosis of Flu A and Flu B.Results Among the 491 patient specimens,the XpertTM Xpress Flu/RSV assay showed the sensitivity for Flu A was 96.88%,and the specificity was 99.75%,and the sensitivity for Flu B was 100.00%,and the specificity was 100.00%.EasyNAT? Flu Assay assay showed the sensitivity for Flu A was 94.79%,and the specificity was 96.81%,and the sensitivity for Flu B was 100.00%,and the specificity was 100.00%.And two POCT molecular methods performed well consistency(Kappa value was 0.974).There was no significant difference in the levels of C-reactive protein and serum amyloid A among the negative group,Flu A positive group,and Flu B positive group(P>0.05).But the levels of white blood cell count in the negative group were higher than those in the Flu A positive group and Flu B positive group(P<0.01).Conclusion In this paper,two typical molecular POCT products are studied.Their sensitivity and specificity are highly consistent with the results of RT-qPCR.Molecular POCT products have the advantages of flexibil-ity and rapidity,which are of great value for the improvement of clinical diagnosis and treatment.Molecular detection combined with peripheral blood infection related indicators is helpful for the early diagnosis of influ-enza virus infectious diseases.

OBJECTIVE To analyze the compositional differences between Fructus Tritici Levis and Triticum aestivum， and to provide reference for identification and quality control of both. METHODS Twenty batches of Fructus Tritici Levis and three batches of T. aestivum were collected， and their fingerprints were acquired by high-performance liquid chromatography and the similarities were evaluated by the Evaluation System of Similarity of Chromatographic Fingerprints of Traditional Chinese Medicine （2012 version）. Cluster analysis （CA）， principal component analysis （PCA） and orthogonal partial least squares-discriminant analysis （OPLS-DA） were performed to analyze the difference of Fructus Tritici Levis and T. aestivum from different regions， and the differential components were screened. The contents of the six identified components in Fructus Tritici Levis and T. aestivum were determined. RESULTS The similarities of the fingerprints of Fructus Tritici Levis ranged from 0.928 to 0.996， and the relative similarities of T. aestivum with Fructus Tritici Levis ranged from 0.761 to 0.773. A total of 19 common peaks were calibrated， and six components including linolenic acid， linoleic acid， 5-heptadecylresorcinol， 5-nonadodecylresorcinol， 5- heneicosylresorcinol， and 5-tricosylresorcinol were identified. The results of CA and PCA showed that Fructus Tritici Levis and T. aestivum could be clearly distinguished； the distribution of Fructus Tritici Levis from Anhui province was relatively concentrated. The results of OPLS-DA showed that linolenic acid， linoleic acid， and other six unknown compounds were the differential components between Fructus Tritici Levis and T. aestivum. The average contents of the six identified components in Fructus Tritici Levis were 0.100 9， 1.094 0， 0.005 1， 0.030 9， 0.098 2，and 0.024 8 mg/g， respectively； the contents of linolenic acid and linoleic acid in Fructus Tritici Levis were significantly higher than those in T. aestivum （P＜0.05）.CONCLUSIONS The established qualitative and quantitative methods are simple and reliable， and can be used for the identification and quality evaluation of Fructus Tritici Levis and T. aestivum. The identified differential components， such as linolenic acid and linoleic acid， can also provide clues for the differentiation and pharmacological study of Fructus Tritici Levis and T. aestivum.

OBJECTIVE To analyze the compositional differences between Fructus Tritici Levis and Triticum aestivum， and to provide reference for identification and quality control of both. METHODS Twenty batches of Fructus Tritici Levis and three batches of T. aestivum were collected， and their fingerprints were acquired by high-performance liquid chromatography and the similarities were evaluated by the Evaluation System of Similarity of Chromatographic Fingerprints of Traditional Chinese Medicine （2012 version）. Cluster analysis （CA）， principal component analysis （PCA） and orthogonal partial least squares-discriminant analysis （OPLS-DA） were performed to analyze the difference of Fructus Tritici Levis and T. aestivum from different regions， and the differential components were screened. The contents of the six identified components in Fructus Tritici Levis and T. aestivum were determined. RESULTS The similarities of the fingerprints of Fructus Tritici Levis ranged from 0.928 to 0.996， and the relative similarities of T. aestivum with Fructus Tritici Levis ranged from 0.761 to 0.773. A total of 19 common peaks were calibrated， and six components including linolenic acid， linoleic acid， 5-heptadecylresorcinol， 5-nonadodecylresorcinol， 5- heneicosylresorcinol， and 5-tricosylresorcinol were identified. The results of CA and PCA showed that Fructus Tritici Levis and T. aestivum could be clearly distinguished； the distribution of Fructus Tritici Levis from Anhui province was relatively concentrated. The results of OPLS-DA showed that linolenic acid， linoleic acid， and other six unknown compounds were the differential components between Fructus Tritici Levis and T. aestivum. The average contents of the six identified components in Fructus Tritici Levis were 0.100 9， 1.094 0， 0.005 1， 0.030 9， 0.098 2，and 0.024 8 mg/g， respectively； the contents of linolenic acid and linoleic acid in Fructus Tritici Levis were significantly higher than those in T. aestivum （P＜0.05）.CONCLUSIONS The established qualitative and quantitative methods are simple and reliable， and can be used for the identification and quality evaluation of Fructus Tritici Levis and T. aestivum. The identified differential components， such as linolenic acid and linoleic acid， can also provide clues for the differentiation and pharmacological study of Fructus Tritici Levis and T. aestivum.

Lung cancer is the most common malignant disease and the leading cause of cancer death in China.Non-small cell lung cancer(NSCLC)accounts for over 80％of all lung cancers,and the probability of NSCLC gene mutations is high,with a wide variety of types.With the development of next-generation sequencing(NGS)detection technology,more and more patients with rare fusion gene mutations are detected.Neuregulin 1(NRG1)gene is a rare oncogenic driver that can lead to activation of human epidermal growth factor receptor 3(Her3/ErbB3)mediated pathway,resulting in tumor forma-tion.In this article,we reported a case of mixed NSCLC with CRISPLD2-NRG1 fusion detected by RNA-based NGS,who re-sponsed to Afatinib well after 1 month of treatment,and magnetic resonance imaging(MRI)showed shrinkage of intracranial lesions.Meanwhile,we also compiled previously reported NSCLC patients with NRG1 rare gene fusion mutation,in order to provide effective references for clinical diagnosis and treatment.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

Objective : To survey the current situation and analyze the relevant influencing factors of sleep quality in adolescent patients with temporomandibular joint anterior disc displacement. Methods: This study has been reviewed and approved by the Ethics Committee, and informed consent has been obtained from patients. A convenience sampling method was used to investigate 120 adolescent patients with temporomandibular joint anterior disc displacement in the outpatient department of stomatology in a grade A tertiary hospital in Shanghai using the general data questionnaire, the Pittsburgh sleep quality index scale (PSQI), the Chinese middle school student mental health scale (MMHI-60) and the pain visual analog scale (VAS). Descriptive analysis, single factor analysis, correlation analysis and multiple regression analysis were used to explore the relevant influencing factors. Results: The PSQI score of adolescent patients with temporomandibular joint anterior disc displacement in this study was 7.77 ± 4.63. There was a statistically significant difference in sleep quality among patients with different academic pressures and levels of sleep bruxism (P<0.05). The sleep quality score was positively correlated with the pain score (r = 0.45, P<0.001) and positively correlated with the psychological score (r = 0.74, P<0.001). The degree of pain can affect the patient's sleep quality, and those with good mental health have better sleep quality. The results of regression analysis showed that academic stress (OR = 2.511, 95% CI =1.307 ~ 4.828), bruxism (OR = 3.694, 95% CI = 1.394 ~ 9.791), pain score (OR = 2.104, 95% CI =1.095 ~ 4.041) and psychological score (OR = 1.039, 95% CI = 1.021 ~ 1.058) were statistically significant. Conclusion The sleep quality of adolescent patients with temporomandibular joint anterior disc displacement is generally poor. Academic pressure, sleep bruxism, pain and mental health are the influencing factors of sleep quality.