Objective:To explore the expression and mechanism of lncRNA MALAT1 in negative sputum for tuberculous bac-terium.Methods:Expression of lncRNA MALAT1 in peripheral blood mononuclear cells(PBMC)of patients with positive sputum bacteria(Positive group)and negative sputum bacteria(Negative group)and healthy volunteers(HC group)was detected by RT-PCR.ELISA was used to detect expression levels of TNF-α,IL-1β and IL-6 in plasma.Expression of lncRNA MALAT1 in mice macro-phages RAW264.7 was silenced by siRNA interference,and RAW264.7 cells were infected with mycobacterium tuberculosis(MTB)H37Rv.Cells were divided into four groups:Control group,Control+MTB group,MTB+si-NC group and MTB+si-MALAT1 group.Proliferation activity of RAW264.7 cells in each group was detected by CCK-8 method.The number of MTB in each group was detected by CFU.Expressions of TNF-α,IL-1β and IL-6 in supernatant of RAW264.7 cells were detected by ELISA.Results:Compared with HC group,expressions of lncRNA MALAT1 in PBMC,and TNF-α,IL-1β and IL-6 in plasma were significantly increased in Positive group and Negative group(P<0.01).Compared with Control group,expression level of lncRNA MALAT1,proliferation activity,CFU value,and concentrations of TNF-α,IL-1β and IL-6 in supernatant of Control+MTB group,MTB+si-NC group and MTB+si-MALAT1 group were significantly increased(P<0.05).Compared with MTB+si-NC group,the above detection indexes in MTB+si-MALAT1 group were significantly decreased(P<0.05),while there was no significant difference in Control+MTB group(P>0.05).Conclusion:The significantly increased expression of MALAT1 in patients with negative sputum for tuberculous bacterium is positively correlated with expression of plasma inflammatory factors,while the silence of MALAT1 expression can reduce MTB induced inflammatory response by inhibiting the proliferation and phagocytosis of MTB infected macrophages.

Objective To investigate the correlation of sarcoplasmic/endoplasmic reticulum Ca2+ATPase 2a(SERCA2a)and blood urea nitrogen(BUN)to albumin(ALB)ratio(BAR)with cardiac function and prognosis in patients with chronic heart failure(CHF).Methods A total of 200 CHF patients admitted to this hospital from January 2021 to January 2023 were selected as the CHF group and 100 healthy people un-dergoing physical examination as the control group.CHF patients were divided into class Ⅰ group(38 cases),class Ⅱ group(40 cases),class Ⅲ group(54 cases)and class Ⅳ group(68 cases)according to the New York Heart Association(NYHA)cardiac function classification after admission.According to the prognosis,they were divided into poor prognosis group(60 cases)and good prognosis group(140 cases).Serum SERCA2a BUN,ALB levels were measured and BAR was calculated.Spearman rank correlation coefficient was used to analyze the correlation between serum SERCA2a,BAR and NYHA cardiac function classification in CHF pa-tients.Taking the prognosis of CHF patients as the dependent variable,a multivariate unconditional Logistic regression model was established to determine its influencing factors.Receiver operating characteristic curve was drawn to evaluate the predictive value of serum SERCA2a and BAR for poor prognosis of CHF patients.Results Compared with the control group,the serum SERCA2a level was decreased and BAR was increased in the CHF group(P＜0.05).The level of SERCA2a in class Ⅰ group,class Ⅱ group,class Ⅲ group and classⅣ group decreased and BAR increased successively(P＜0.05).The NYHA classification was negatively cor-related with serum SERCA2a level(rs=-0.756,P＜0.05),and positively correlated with BAR(rs=0.722,P＜0.05).After 1-year follow-up,the incidence of poor prognosis in 200 CHF patients was 30.00％(60/200).Increased NYHA class(OR=1.936,95％CI:1.115-3.364),atrial fibrillation(OR=3.269,95％CI:1.078-9.913),N-terminal pro-B-type natriuretic peptide(OR=1.002,95％CI:1.001-1.009)and BAR(OR=1.169,95％CI:1.082-1.263)were independent risk factors for poor prognosis in patients with CHF(P＜0.05),increased left ventricular ejection fraction(OR=0.810,95％CI:0.716-0.916)and SERCA2a(OR=0.964,95％CI:0.947-0.981)were independent protective factors(P＜0.05).The area under the curve of serum SERCA2a combined with BAR to predict the poor prognosis of CHF patients was 0.865(95％CI:0.810-0.910),which was larger than 0.784(95％CI:0.720-0.839)and 0.777(95％CI:0.713-0.833)predicted by serum SERCA2a and BAR levels alone,and the difference was statistically significant(Z=2.944,3.250,P＜0.05).Conclusion The decrease of SERCA2a and the increase of BAR are closely re-lated to cardiac function and prognosis in patients with CHF.Serum SERCA2a combined with BAR has a higher value in predicting poor prognosis in patients with CHF.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To explore the expression and mechanism of lncRNA MALAT1 in negative sputum for tuberculous bac-terium.Methods:Expression of lncRNA MALAT1 in peripheral blood mononuclear cells(PBMC)of patients with positive sputum bacteria(Positive group)and negative sputum bacteria(Negative group)and healthy volunteers(HC group)was detected by RT-PCR.ELISA was used to detect expression levels of TNF-α,IL-1β and IL-6 in plasma.Expression of lncRNA MALAT1 in mice macro-phages RAW264.7 was silenced by siRNA interference,and RAW264.7 cells were infected with mycobacterium tuberculosis(MTB)H37Rv.Cells were divided into four groups:Control group,Control+MTB group,MTB+si-NC group and MTB+si-MALAT1 group.Proliferation activity of RAW264.7 cells in each group was detected by CCK-8 method.The number of MTB in each group was detected by CFU.Expressions of TNF-α,IL-1β and IL-6 in supernatant of RAW264.7 cells were detected by ELISA.Results:Compared with HC group,expressions of lncRNA MALAT1 in PBMC,and TNF-α,IL-1β and IL-6 in plasma were significantly increased in Positive group and Negative group(P<0.01).Compared with Control group,expression level of lncRNA MALAT1,proliferation activity,CFU value,and concentrations of TNF-α,IL-1β and IL-6 in supernatant of Control+MTB group,MTB+si-NC group and MTB+si-MALAT1 group were significantly increased(P<0.05).Compared with MTB+si-NC group,the above detection indexes in MTB+si-MALAT1 group were significantly decreased(P<0.05),while there was no significant difference in Control+MTB group(P>0.05).Conclusion:The significantly increased expression of MALAT1 in patients with negative sputum for tuberculous bacterium is positively correlated with expression of plasma inflammatory factors,while the silence of MALAT1 expression can reduce MTB induced inflammatory response by inhibiting the proliferation and phagocytosis of MTB infected macrophages.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

ObjectiveLife education and hospice & palliative care (HPC) involve many aspects of medicine, humanities and education, which is not only an important public health issue, but also an important livelihood issue and education issue widely concerned at home and abroad. This study aims to explore the design and implementation of integrated life education and HPC small-class teaching curriculum for medical students, and to guide students to build correct attitudes towards life education and HPC in terms of knowledge, ability and emotion. MethodsA general course of Life Education and HPC for College Students was set up in a medical college in Shanghai using online and offline teaching models. The practical evaluation system and semi-structured outline were studied and constructed. The teaching effect data were collected by questionnaire survey and semi-structured qualitative interview. The Wilcoxon rank sum test was used to quantitatively analyze the objective teaching effect while the thematic analysis method was used to evaluate the subjective learning effect of students. ResultsThrough the integrated teaching practice, the quantitative analysis found that students’ overall evaluation of the course was higher. Learning effectiveness and course satisfaction ratings were both higher online than offline (both P<0.01). The qualitative analysis obtained three major themes: the understanding of life and HPC, the development prospect of HPC and its impact on the future life and career, and eight corresponding sub-themes: the value of life, the significance of HPC, human kindness, the promotion of HPC concept, the improvement of HPC services, the acceptance of HPC, the sense of responsibility as medical students, and the enrichment of life experience. ConclusionThe integrated course of life education and HPC for medical students in a medical college in Shanghai has achieved the teaching goals in terms of knowledge, ability and emotion. The study suggests that the integrated teaching model should be further improved by combining the advantages of online and offline teaching, and the construction of a professional curriculum system should be accelerated, so as to include life education and HPC into the required general courses for undergraduates in medical colleges to help students establish a correct outlook on life and values, establish the concept and will of HPC service, and guide the professional quality improvement and career development of future medical career.

Objective:It aimed to improve pilot programs and explore medical insurance payment methods tailored to the characteristics of Traditional Chinese Medicine(TCM)services by summarizing practical experiences,analyzing common issues,and proposing strategic suggestions.Methods:It conducted a comprehensive review of literature and policy documents to analyze the progress of TCM advantageous condition and payment modality reforms,prevalent conditions,and service types.Results:It revealed various payment modalities for TCM,including remuneration for advantageous conditions,establishment of TCM Diagnosis Related Groups(DRG),and formulation of differential adjustment coefficients for TCM-focused healthcare institutions.Reforms predominantly involve expanding condition-based remuneration scopes,individual-condition remunerations,and remuneration based on therapeutic efficacy.The effect of the reform is mainly reflected in the recognition of the value of TCM,the support of hospitals,and the benefit of patients.Predominant conditions include bone injuries,rheumatic ailments,and anorectal disorders,with hospitalization being the primary service modality.Conclusion:Current payment modalities for TCM advantageous conditions face challenges such as limited reform scope and service coverage,coding system standardization issues,performance incentive deficiencies,and irrational cost estimation.Recommendations include exploring integrated TCM advantageous condition service models,establishing a management paradigm for TCM conditions,addressing transient policy-related deficits,leveraging data analytics and AI algorithms,and formulating clinical pathway standards.

Objective:In the background of the national promotion of DIP insurance payment reform,using empirical data to evaluate the initial effects of payment reforms in typical traditional Chinese medicine hospitals.Methods:It collected inpatient case data from a Chinese medicine hospital in S city,encompassing a total of 207 500 cases from 2016 to 2022,and the differences between before and after the reform were analyzed using key indicators such as medical costs.Interrupted time series was applied to analyze the initial effects of the reform based on three intervention time points:reform implementation,epidemic development and static management.Logistic regression was applied to explore the main influencing factors,and recommendations for refinement were made based on the results of the key informant interviews.Results:It was found that the percentage of the high hospitalization cost group,high self-pay cost group and high bed-day cost group increased after the reform.There was an overall increasing trend in bed-day costs after the reform.Age,being married,operation times and average grade of operations,and death were the common influences.Conclusion:The initial stage of the DIP insurance payment reform was not effective in regulating medical costs.Demographic changes,strong intervention in epidemics,and the tendency of Chinese medicine hospitals to perform surgeries all tend to lead to an increase in hospitalization costs.At the same time,factors such as the shortage of policy interpretation and the lack of incentives for medical staff have affected the effectiveness of the reform.It was suggested that the DIP insurance payment reform of the Chinese medicine hospitals should be further promoted by strengthening the interpretation of policies and changing the solidification model,establishing positive incentives for medical staff,and adding indicators of traditional Chinese medicine characteristics.This will further fully leverage the advantages of Chinese medicine characteristics and enhance the substance and quality of Chinese medicine services.

Objective It constructs a new culture index system for high-quality development of public hospitals to provide scientific tool for evaluating the level of new culture construction of public hospitals and promoting high-quali-ty development.Methods A pool of indicators was established based on policy combing and literature research.Then,an indicator system was formed through Delphi expert consultation and demonstration,and weighting coefficients were assigned using the hierarchical analysis method.Results The indicator system of the new culture in public hospi-tals contains four primary indicators,eight secondary indicators and 23 specific entries.The four primary indicators are hospital culture system construction,strengthening the orientation of patients'needs,caring for medical staff and distinctive hospital culture.The effectiveness of implementation as an additional dimension contains two secondary indicators and eight specific entries.The weights of the primary indicators are balanced.Among the sec-ondary indicators,institutional planning(0.250),staff care(0.170),medical services(0.130),discipline branding(0.125)and professionalism(0.125)have higher weights.Conclusion The new culture index system for high-quality development of public hospitals is in line with Xi Jinping's cultural thought and the requirements of the new culture for high-quality development of public hospitals.It needs to pay attention to the planning of the cultural system,em-phasises the care of employees and the cultivation of professionalism,and focuses on the construction of medical services and disciplinary brands,so as to promote the construction of the new culture in public hospitals in line with the requirements of the national strategies.

Objective It developed and tested the reliability and validity of the Hospital Culture Perception and Experi-ence Scale for medical staff to provide a scientific tool for assessing medical staff's perceptions and experiences of hospital culture.Methods The scale was constructed with reference to the Dennison model of organisational culture and refined based on policy documents,literature and expert consultation.A hospital was selected to conduct the questionnaire survey.It used item analysis,factor analysis,and Cronbach's α to test scale reliability and validity.Results The total scale contains 43 entries in 4 subscales:institutional initiatives,practitioner perception,manage-ment training and mission commitment.Item analysis results showed that the scale had good differentiation;the Cronbach's αof the total scale was 0.992,and the Spearman-Brown was 0.979,with good reliability.Exploratory factor analysis results suggested that the institutional initiative,practitioner perception,and management training sub-scales had only 1 dimension,with a variance contribution rate of 77.12％,76.33％,and 85.75％,respectively.For the mission subscale,2 male factors were extracted,with a cumulative variance contribution rate of 91.34％.The factor loadings were all greater than 0.4.The validation factor analysis results shows that the model fit is good.The AVE of each subscale and dimension is greater than 0.5,and the CR is greater than 0.7,which indicates that the convergent validity is good.Conclusion The scale has good reliability and validity,and can be used to quantitatively as-sess the medical staff's perception and sense of experience of hospital culture.