Objective To explore the impact of the TINCR-MAF:MAFB transcription factor network on the expression of proliferation and differentiation-related genes in keratinocytes,to verify the role of this network in the occurrence and development of psoriasis and its potential mechanisms.Methods Employed RNA interference technology to knock down TINCR gene expression,and the proliferation ability of keratinocytes was assessed using the CCK-8 method.Additionally,qRT-PCR and Western blot analyses were conducted to evaluate the RNA and protein expression levels of TINCR,MAFB,and KLF4 genes.Immunohistochemical methods were used to detect the expression of KLF4 protein in psoriasis tissues.Results After TINCR gene siRNA interference,the proliferation ability of keratinocytes significantly decreased at 24,48,and 72 hours(P<0.001),indicating that the TINCR gene plays a critical role in cell proliferation.The results of qRT-PCR and Western blot analyses showed that the RNA and protein expression levels of TINCR,MAFB,and KLF4 genes were significantly reduced(P<0.001),suggesting that TINCR may influence the differentiation of keratinocytes by regulating the expression of MAFB transcription factor and KLF4 differentiation-related genes.Furthermore,immunohistochemical results indicated that the expression of KLF4 protein was significantly elevated in psoriasis tissues compared to normal skin tissues,suggesting that KLF4 plays an important role in the pathogenesis of psoriasis.Conclusions The TINCR-MAF:MAFB transcription factor network may participate in the occurrence and development of psoriasis by affecting the proliferation and differentiation of keratinocytes.This finding provides a new perspective on the pathogenesis of psoriasis and potential targets for future therapeutic strategies.

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 impact of the TINCR-MAF:MAFB transcription factor network on the expression of proliferation and differentiation-related genes in keratinocytes,to verify the role of this network in the occurrence and development of psoriasis and its potential mechanisms.Methods Employed RNA interference technology to knock down TINCR gene expression,and the proliferation ability of keratinocytes was assessed using the CCK-8 method.Additionally,qRT-PCR and Western blot analyses were conducted to evaluate the RNA and protein expression levels of TINCR,MAFB,and KLF4 genes.Immunohistochemical methods were used to detect the expression of KLF4 protein in psoriasis tissues.Results After TINCR gene siRNA interference,the proliferation ability of keratinocytes significantly decreased at 24,48,and 72 hours(P<0.001),indicating that the TINCR gene plays a critical role in cell proliferation.The results of qRT-PCR and Western blot analyses showed that the RNA and protein expression levels of TINCR,MAFB,and KLF4 genes were significantly reduced(P<0.001),suggesting that TINCR may influence the differentiation of keratinocytes by regulating the expression of MAFB transcription factor and KLF4 differentiation-related genes.Furthermore,immunohistochemical results indicated that the expression of KLF4 protein was significantly elevated in psoriasis tissues compared to normal skin tissues,suggesting that KLF4 plays an important role in the pathogenesis of psoriasis.Conclusions The TINCR-MAF:MAFB transcription factor network may participate in the occurrence and development of psoriasis by affecting the proliferation and differentiation of keratinocytes.This finding provides a new perspective on the pathogenesis of psoriasis and potential targets for future therapeutic strategies.

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 potential predictors of refractory Mycoplasma pneumoniae pneumonia (RMPP) in early stage. Methods:The prospective multicenter study was conducted in Zhejiang, China from May 1 st, 2019 to January 31 st, 2020. A total of 1 428 patients with fever >48 hours to <120 hours were studied. Their clinical data and oral pharyngeal swab samples were collected; Mycoplasma pneumoniae DNA in pharyngeal swab specimens was detected. Patients with positive Mycoplasma pneumoniae DNA results underwent a series of tests, including chest X-ray, complete blood count, C-reactive protein, lactate dehydrogenase (LDH), and procalcitonin. According to the occurrence of RMPP, the patients were divided into two groups, RMPP group and general Mycoplasma pneumoniae pneumonia (GMPP) group. Measurement data between the 2 groups were compared using Mann-Whitney U test. Logistic regression analyses were used to examine the associations between clinical data and RMPP. Receiver operating characteristic (ROC) curves were used to analyse the power of the markers for predicting RMPP. Results:A total of 1 428 patients finished the study, with 801 boys and 627 girls, aged 4.3 (2.7, 6.3) years. Mycoplasma pneumoniae DNA was positive in 534 cases (37.4%), of whom 446 cases (83.5%) were diagnosed with Mycoplasma pneumoniae pneumonia, including 251 boys and 195 girls, aged 5.2 (3.3, 6.9) years. Macrolides-resistant variation was positive in 410 cases (91.9%). Fifty-five cases were with RMPP, 391 cases with GMPP. The peak body temperature before the first visit and LDH levels in RMPP patients were higher than that in GMPP patients (39.6 (39.1, 40.0) vs. 39.2 (38.9, 39.7) ℃, 333 (279, 392) vs. 311 (259, 359) U/L, both P<0.05). Logistic regression showed the prediction probability π=exp (-29.7+0.667×Peak body temperature (℃)+0.004×LDH (U/L))/(1+exp (-29.7+0.667×Peak body temperature (℃)+0.004 × LDH (U/L))), the cut-off value to predict RMPP was 0.12, with a consensus of probability forecast of 0.89, sensitivity of 0.89, and specificity of 0.67; and the area under ROC curve was 0.682 (95% CI 0.593-0.771, P<0.01). Conclusion:In MPP patients with fever over 48 to <120 hours, a prediction probability π of RMPP can be calculated based on the peak body temperature and LDH level before the first visit, which can facilitate early identification of RMPP.

Purpose/Significance To explore the research status,research hotspots and frontiers in the field of clinical decision sup-port based on electronic medical records(EMR).Method/Process The bibliometric method and CiteSpace 6.2.R2 software are used to draw scientific knowledge graph of country/region distribution,author cooperation,institutional cooperation,keyword co-occurrence and clustering for visualized comparative analysis.Python is used for clustering hotspot mining and analysis.Result/Conclusion The field of clinical decision support based on EMR data shows a rapid development trend,with the United States and China as the main research countries and strong cooperation between domestic and foreign institutions.The keywords mainly involve EMR,artificial intelligence(AI),etc.

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.

The article analyzes chief physician SUN Wuquan's empirical characteristics in treating neck-type cervical spondylosis:disease differentiation combined with pattern differentiation,emphasizing the assessment of tendons and bones,with DING's Tuina(Chinese therapeutic massage)manipulations and static Gongfa(Qigong exercise)as the predominant treatment,inherits the academic features of DING's Tuina school,"paying equal attention to tendons and bones,putting function first";thus provides a reference for treating neck-type cervical spondylosis with Tuina therapy.

Objective:To survey the training requirements for doctoral candidates in general practice.Methods:A questionnaire survey was conducted to investigate the requirements for general practice doctorate training; the items included the training target, training objectives and training contents. A total of 130 stakeholders of general practice participated in the survey, including administrators in health committees, general practice educators of medical schools, general practitioners in hospital and community health service centers and students in general medicine. The participants were divided into training unit group ( n=42), employer group ( n=42) and doctor and student group ( n=46). Results:Among all training objectives, the training "general medicine scientific research ability" ranked the first (121, 93.1%); while 114 (87.7%) respondents believed that the training target should be the leading talents in general medicine. Regarding the content of clinical competence training, 86 (66.1%) respondents believed that there was a lack of medical care training for whole life-cycle and whole population currently. The training requirement for acute disease management and referral competence in employer group was significantly higher than the training unit group (χ2=25.73, P<0.01) and the doctor and student group (χ2=24.37, P<0.01). Most survey respondents believed that the doctorate candidates in general medicine should focus on scientific research training in community clinical medicine ( n=110, 84.6%), community preventive medicine and epidemiology ( n=114, 87.7%); 117 (90.0%) respondents believed that clinical research design and methodology training should be strengthened, and 125 (96.2%) respondents believed that clinical teaching thinking training should be strengthened; 108 (83.1%) survey respondents believed that it is necessary for their units to recruit personnel with doctoral degree in general medicine. Conclusion:The training of doctorate candidates in general medicine should aim at cultivating leading talents in general medicine, strengthen the training of clinical scientific research capabilities, and focus on cultivating their comprehensive abilities of "being able to research, be good at teaching, and understand management", so that they can truly become the leaders in this discipline.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.