Due to its high sensitivity and non-destructive nature, Raman spectroscopy has become an essential analytical tool in biopharmaceutical analysis and drug development. Despite of the computational demands, data requirements, or ethical considerations, artificial intelligence (AI) and particularly deep learning algorithms has further advanced Raman spectroscopy by enhancing data processing, feature extraction, and model optimization, which not only improves the accuracy and efficiency of Raman spectroscopy detection, but also greatly expands its range of application. AI-guided Raman spectroscopy has numerous applications in biomedicine, including characterizing drug structures, analyzing drug forms, controlling drug quality, identifying components, and studying drug-biomolecule interactions. AI-guided Raman spectroscopy has also revolutionized biomedical research and clinical diagnostics, particularly in disease early diagnosis and treatment optimization. Therefore, AI methods are crucial to advancing Raman spectroscopy in biopharmaceutical research and clinical diagnostics, offering new perspectives and tools for disease treatment and pharmaceutical process control. In summary, integrating AI and Raman spectroscopy in biomedicine has significantly improved analytical capabilities, offering innovative approaches for research and clinical applications.

Objective To investigate the interaction between serum uric acid(SUA)and islet β cell function,and its influence in the development of diabetic peripheral neuropathy(DPN)in patients with type 2 diabetes mellitus(T2DM).Methods A total of 122 patients with T2DM who visited General Medicine Department of our hospital from January 2019 to May 2023 were enrolled in this study and divided into two groups according to whether they had DPN:T2DM group(n=30)and DPN group(n=92).Patients in DPN group were further divided into three groups according to the severity of DPN:mild DPN group(L-DPN,n=30),moderate DPN group(M-DPN,n=36)and severe DPN group(H-DPN,n=26).The clinical data and biochemical indexes were compared and analyzed among the four groups.Logistic multivariate analysis was used to analyze the relationship between SUA and islet β cell secretory function and severe DPN.Multivariate logistic regression was used to analyze the risk factors for the severity of DPN.Stepwise regression method was used to screen the most important related factors for the severity of DPN,and a nomogram model was constructed and validated.The interaction between SUA and islet β cell function,and its influence in the development of DPN in T2DM patients were analyzed.Results Multivariate logistic regression analysis showed that age,SUA,aspartate aminotransferase,HDL-C,HbAlc,islet β cell secretion function(C2/C0)and insulin resistance index(HOMA-IR)were all independent influencing factors for the severity of DPN(P<0.05).The C indices of SUA,C2/C0,HOMA-IR,HbAlc and HDL-C prediction models were 0.776 and 0.769 on the training set and validation set respectively,which were most correlated with the aggravation of DPN degree.The area under the curve of the nomogram model for predicting the risk of DPN severity was 0.928(95%CI 0.856～0.986)and 0.917(95%CI 0.856～0.986)before and after validation respectively.The mean absolute error of the calibration curve was 0.013,which could be used as a risk tool to predict the risk of DPN aggravation.The results of interaction analysis showed that there were multiplicative and additive interactions between SUA and islet β cell secretion.Conclusions There is an interaction between SUA and islet β cell secretion function,which is a risk factor for the aggravation of DPN.The risk factors can be early warned according to the nomogram model,so as to carry out targeted prevention and treatment for T2DM patients.

Purpose To observe the CT imaging features of dermatomyosistis patients with anti-melanoma differentiation related genes 5(MDA-5),and to analyze the relationship between CT imaging indicators and short-term prognosis of patients.Materials and Methods Sixty-seven MDA-5-positive dermatomyosistis patients admitted to China-Japan Friendship Hospital from January 2017 to December 2018 were retrospectively included.Prognosis of included patients in one year were followed-up.Analysis of the relationship between chest CT imaging and short-term poor prognosis.Results Among 67 patients,nine patients died in one year.There was a statistically significant difference between the two groups(the death group and the survivor group)in the interstitial lung disease(ILD)imaging types(χ2=0.198,P=0.025)and pulmonary/aortic diameter ratios(U=103.0,P=0.004).CT imaging of dermatomyosistis-ILD with anti-MDA5 antibody positive patients showed organizing pneumonia.Mortality of diffuse alveolar damage type was significantly higher than that of other types.Logistic regression analysis showed that pulmonary/aortic diameter ratio(OR:4.208,P=0.002)was a strong independent risk factor for the death of patients with anti-MDA5 antibody.Conclusion Most patients with anti-MDA5 antibody positive dermatomyositis show ILD,with the main feature of organic pneumonia.Patients with poor prognosis within 1 year have different types of ILD,while the pulmonary/aortic diameter ratio is a strong independent risk factor for the death of dermatomyosistis-ILD with anti-MDA5 antibody positive patients.

Objective:To understand the epidemiological characteristics of human respiratory syncytial virus (HRSV) among acute respiratory infection (ARI) cases in 16 provinces of China from 2009 to 2023.Methods:The data of this study were collected from the ARI surveillance data from 16 provinces in China from 2009 to 2023, with a total of 28 278 ARI cases included in the study. The clinical specimens from ARI cases were screened for HRSV nucleic acid from 2009 to 2023, and differences in virus detection rates among cases of different age groups, regions, and months were analyzed.Results:A total of 28 278 ARI cases were enrolled from January 2009 to September 2023. The age of the cases ranged from<1 month to 112 years, and the age M ( Q1, Q3) was 3 years (1 year, 9 years). Among them, 3 062 cases were positive for HRSV nucleic acid, with a total detection rate of 10.83%. From 2009 to 2019, the detection rate of HRSV was 9.33%, and the virus was mainly prevalent in winter and spring. During the Corona Virus Disease 2019 (COVID-19) pandemic, the detection rate of HRSV fluctuated between 6.32% and 18.67%. There was no traditional winter epidemic peak of HRSV from the end of 2022 to the beginning of 2023, and an anti-seasonal epidemic of HRSV occurred from April to May 2023. About 87.95% (2 693/3 062) of positive cases were children under 5 years old, and the difference in the detection rate of HRSV among different age groups was statistically significant ( P<0.001), showing a decreasing trend of HRSV detection rate with the increase of age ( P<0.001). Among them, the HRSV detection rate (25.69%) was highest in children under 6 months. Compared with 2009-2019, the ranking of HRSV detection rates in different age groups changed from high to low between 2020 and 2023, with the age M (Q1, Q3) of HRSV positive cases increasing from 1 year (6 months, 3 years) to 2 years (11 months, 3 years). Conclusion:Through 15 years of continuous HRSV surveillance analysis, children under 5 years old, especially infants under 6 months old, are the main high-risk population for HRSV infection. During the COVID-19 pandemic, the prevalence and patterns of HRSV in China have changed.

Background::Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods::This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Na?ve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis.Results::The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726. Conclusions::Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.

Objective:To understand the epidemiological characteristics of human respiratory syncytial virus (HRSV) among acute respiratory infection (ARI) cases in 16 provinces of China from 2009 to 2023.Methods:The data of this study were collected from the ARI surveillance data from 16 provinces in China from 2009 to 2023, with a total of 28 278 ARI cases included in the study. The clinical specimens from ARI cases were screened for HRSV nucleic acid from 2009 to 2023, and differences in virus detection rates among cases of different age groups, regions, and months were analyzed.Results:A total of 28 278 ARI cases were enrolled from January 2009 to September 2023. The age of the cases ranged from<1 month to 112 years, and the age M ( Q1, Q3) was 3 years (1 year, 9 years). Among them, 3 062 cases were positive for HRSV nucleic acid, with a total detection rate of 10.83%. From 2009 to 2019, the detection rate of HRSV was 9.33%, and the virus was mainly prevalent in winter and spring. During the Corona Virus Disease 2019 (COVID-19) pandemic, the detection rate of HRSV fluctuated between 6.32% and 18.67%. There was no traditional winter epidemic peak of HRSV from the end of 2022 to the beginning of 2023, and an anti-seasonal epidemic of HRSV occurred from April to May 2023. About 87.95% (2 693/3 062) of positive cases were children under 5 years old, and the difference in the detection rate of HRSV among different age groups was statistically significant ( P<0.001), showing a decreasing trend of HRSV detection rate with the increase of age ( P<0.001). Among them, the HRSV detection rate (25.69%) was highest in children under 6 months. Compared with 2009-2019, the ranking of HRSV detection rates in different age groups changed from high to low between 2020 and 2023, with the age M (Q1, Q3) of HRSV positive cases increasing from 1 year (6 months, 3 years) to 2 years (11 months, 3 years). Conclusion:Through 15 years of continuous HRSV surveillance analysis, children under 5 years old, especially infants under 6 months old, are the main high-risk population for HRSV infection. During the COVID-19 pandemic, the prevalence and patterns of HRSV in China have changed.

Objective:To analyze the curative effect of sandwich cheiloplasty for the treatment of macrocheilia secondary to port-wine stain (PWS).Methods:A total of 43 patients, including 20 males and 23 females, aged 37.0 years (ranged from 4 to 69 years), who underwent sandwich cheiloplasty for the treatment of macrocheilia secondary to PWS from March 2008 to March 2022 were retrospectively enrolled. According to the location of the lesions, they were divided into two groups: upper lip group (21 cases) and lower lip group (22 cases). Pathological examinations were performed in all excised specimens. Postoperative attentions should be paid to keeping incisions locally clean, observing the blood supplying, and taking care of fluid accumulation or incision split. After discharge, the lip appearance and function were evaluated every six months in the outpatient department, and the long-term efficacy was further divided into three levels: grade Ⅰ (poor), grade Ⅱ (moderate), and grade Ⅲ (good). The comparison of grade data named long-term efficacy between the two groups was conducted by Mann-Whitney U test.Results:All 43 patients underwent sandwich cheiloplasty, of which 40 patients received one operation, whereas the other 3 received two due to recurrences. One-stage incision healing was achieved in 43 cases, whereas partial mucosal necrosis appeared in 2 cases, and slight incision dehiscence occurred in 1 case, which healed well after local dressing change. The pathological examination results of 43 excised tissue specimens all showed capillary malformations. After follow-up for 1 to 10 years, long-term efficacy evaluation was made up as follows: 36 cases were evaluated as grade Ⅲ, 7 cases as grade Ⅱ, and 0 case as grade Ⅰ. There were 18 cases of grade Ⅲ, and 3 cases of grade Ⅱ in group A, compared to 18 cases of grade Ⅲ, and 4 cases of grade Ⅱ in group B. By rank sum test, there was no significant difference in overal efficacy between the two groups ( Z=0.342, P>0.05). Conclusions:Sandwich cheiloplasty for the treatment of macrocheilia secondary to PWS effectively removes malformed vascular lesions, and consequently, it can achieve a good long-term therapeutic effect.

Humans ; Hospital Mortality ; Pulmonary Embolism ; Comorbidity ; Registries ; Neoplasms/complications*

For wild natural medicine, unanticipated biodiversity as species or varieties with similar morphological characteristics and sympatric distribution may co-exist in a single batch of medical materials, which affects the efficacy and safety of clinical medication. DNA barcoding as an effective species identification tool is limited by its low sample throughput nature. In this study, combining DNA mini-barcode, DNA metabarcoding and species delimitation method, a novel biological sources consistency evaluation strategy was proposed, and high level of interspecific and intraspecific variations were observed and validated among 5376 Amynthas samples from 19 sampling points regarded as "Guang Dilong" and 25 batches of proprietary Chinese medicines. Besides Amynthas aspergillum as the authentic source, 8 other Molecular Operational Taxonomic Units (MOTUs) were elucidated. Significantly, even the subgroups within A. aspergillum revealed here differ significantly on chemical compositions and biological activity. Fortunately, this biodiversity could be controlled when the collection was limited to designated areas, as proved by 2796 "decoction pieces" samples. This batch biological identification method should be introduced as a novel concept regarding natural medicine quality control, and to offer guidelines for in-situ conservation and breeding bases construction of wild natural medicine.

Objective:To understand the common viral infection among the surveillance cases of fever respiratory syndrome (FRS) in nine provinces in China.Methods:The research data were obtained from nine provinces (Anhui, Beijing, Guangdong, Hebei, Hunan, Jilin, Shandong, Shaanxi and Xinjiang) in the "Infectious Disease Surveillance Technology Platform Information Management System" of the Chinese Center for Disease Control and Prevention from January 2009 to June 2021. Finally, 8 243 FRS cases with nucleic acid detection results of eight viruses [human influenza virus (HIFV), human respiratory syncytial virus (HRSV), human adenovirus (HAdV), human parainfluenza virus (HPIV), human rhinovirus (HRV), human metapneumovirus (HMPV), human coronavirus (HCoV) and human Boca virus (HBoV)] were included in the study. The χ 2 test/Fisher exact probability method was used to analyze the difference of virus detection rate in different age groups, regions and seasons. Results The M ( Q1, Q3) age of 8 243 FRS cases was 4 (1, 18) years old, and 56.56% (4 662 cases) were children under 5 years old. Males accounted for 58.1% (4 792 cases) of all cases. All cases were from outpatient/emergency department (2 043 cases) and inpatient department (6 200 cases). The virus detection rates of FRS cases from high to low were HRSV, HIFV, HPIV, HRV, HAdV, HMPV, HCoV and HBoV. Two or more viruses were detected simultaneously in 524 cases, accounting for 15.66% of virus-positive cases. The difference of the virus detection rate in different age groups was statistically significant (all P values<0.05), and the virus detection rate in children<5 years old was higher (49.96%). The positive rate of any virus in south China was higher than that in north China ( P<0.001). The virus-positive FRS cases were detected throughout the year. The detection rate of HRSV was higher in autumn and winter. The detection rate of HIFV was higher in winter. The detection rate of HMPV was higher in winter and spring. The detection rates of HPIV, HRV, HCoV and HBoV were higher in summer and autumn, while there was no significant difference in the detection rate of HAdV in different seasons. Compared with 2009-2019, the detection rate of any virus in 2020-2021 decreased from 41.37% to 37.86%. The detection rate of HIFV decreased sharply from 10.62% to 1.37%. The detection rate of HPIV decreased from 8.24% to 5.88%. The detection rate of HRV and HBoV increased from 5.43% and 1.79% to 9.67% and 3.19%, respectively. Conclusion:HRSV and HIFV infections are more common among FRS cases in nine provinces in China from 2009 to 2021, and the epidemiological characteristics of eight common respiratory viruses vary in different age groups, regions and seasons.