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 investigate the expression of LncRNA RMST in gastric cancer and its value in diagnosis and prognosis.Methods Tumor and adjacent normal tissues were collected and analyzed from 92 gastric cancer patients.The expression of LncRNA RMST in these tissues was measured using RT-qPCR.ROC curve analysis was performed to evaluate the diagnostic value of LncRNA RMST for gastric cancer.Survival curves were plotted to assess the prognostic significance of LncRNA RMST in gastric cancer.Results LncRNA RMST expression was significantly lower in gastric cancer tissues than in normal tissues.Further analysis revealed that LncRNA RMST expression levels decreased progressively with advancing cancer stages and were significantly correlated with tumor size,TNM staging,and lymph node metastasis(P<0.05),with no significant correlation with patient age or gender.Additionally,ROC curve analysis indicated that LncRNA RMST has substantial diagnostic value in gastric cancer,with AUC of 0.76(95%CI:0.69～0.83,P<0.01),sensitivity of 70.31%,and specificity of 71.56%.Survival analysis showed that patients with high LncRNA RMST expression had significantly higher overall survival rates than those with low expression.Conclusion LncRNA RMST plays a critical role in the occurrence,progres-sion,and prognosis of gastric cancer.It may serve as a potential biomarker for the diagnosis and prognosis of gastric cancer,offering new insights for clinical screening and therapeutic strategies.

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.

Fifty whitespotted bamboo sharks (Chiloscyllium playgiosum) of both sexes were used to establish a large capacity variable domain of the new antigen receptor (VNAR) library with a total capacity of over 10⁹ colony-forming units (CFU). It was applied to screen VNARs against human serum albumin (HSA) and human transcription factor EB (TFEB), respectively. Meanwhile, VNAR libraries specific to HSA and TFEB with capacities above 10⁸ CFU were obtained following conventional immunization. These two approaches were systematically studied in terms of VNAR yield and composition. By comparing the VNAR sequences obtained from naïve and antigen-immunized libraries, we found that the complementary-determining region 3 (CDR3) of the former differs in composition from that of the latter. It shares a higher degree of homology with the naïve library. Meanwhile, the binding efficiency assessed by ELISA is also different between the naïve and antigen-immunized libraries. The binding of VNARs from the TFEB-immunized library appeared to surpass that observed with the naïve libraries, whereas the performance of VNARs from the HSA-immunized library indicated that both the immunized and naïve libraries for HSA had positive binding responses in polyclonal and monoclonal ELISA. The results are useful to develop novel diagnostic and therapeutic products based on shark VNARs.

Filamenting temperature-sensitive mutant Z (FtsZ), a protein essential for bacterial cell division, is highly conserved across bacterial species but absent in humans, positioning it as a strategic target for the development of antibiotics. Significant efforts to identify FtsZ inhibitors-via biochemical assays (e.g., GTPase activity) and cellular approaches (e.g., immunofluorescence)-have yielded over 100 natural products and synthetic compounds, whose cheminformatics clustering underscores a limited chemical diversity among the current scaffolds. Structural studies, including X-ray crystallography and cryo-electron microscopy, have resolved 97 FtsZ structures revealing conserved polymerization mechanisms and conformational plasticity, as exemplified by extremophile adaptations (e.g., Shewanella benthica from the high-pressure environment of the Mariana Trench's Challenger Deep). However, clinical translation is hindered by weak binding affinities, inhibitory inefficacy, dynamic conformational flexibility, and evolving drug resistance linked to FtsZ's functional plasticity. To address these challenges, future efforts should be directed to resolve transient assembly intermediates, leveraging machine learning with high-throughput screening, and integrating structural biology with pharmacokinetic optimization. Multidisciplinary strategies combining these approaches hold promise for translating FtsZ-focused research into clinically viable therapies, addressing the critical unmet need posed by antibiotics resistance.

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

OBJECTIVE To comparatively analyze tumor staging versus clinical staging in reimbursement scope restrictions under medical insurance for antineoplastic agents in order to better implement the medicare drug payment policy. METHODS Antineoplastic agents included in the National Basic Medical Insurance， Workers’ Compensation Insurance and Maternity Insurance Drug Catalogue （2024） （hereinafter referred to as the “Medical Insurance Catalog”） were used as research subject to compile and analyze reimbursement scope restrictions regarding tumor staging. By consulting clinical diagnosis and treatment guidelines and relevant literature， the tumor staging in reimbursement scope restrictions of the Medical Insurance Catalog was mapped and compared with clinical staging. RESULTS & CONCLUSIONS A total of 89 antineoplastic agents’ medical insurance payments had tumor staging. Among these， there were 86 western drugs （including 17 ordinary western drugs， 68 negotiated drugs， and 1 competitive drug） and 3 Chinese patent medicines （including 1 ordinary Chinese patent medicine and 2 negotiated drugs）. Non-small cell lung cancer involved the most restricted payment drugs， with 36 drugs. The tumor staging in reimbursement scope restrictions was mostly “metastatic” and “locally advanced”， involving 67 and 48 drugs respectively. Tumor staging in most reimbursement scope restrictions could correspond to the clinical staging of the tumor. However， mid-advanced esophageal cancer， unresectable gastrointestinal stromal tumors， unresectable locally advanced neuroendocrine tumors， locally advanced basal cell carcinoma， and unresectable neurofibromatosis type Ⅰ did not have a corresponding clinical staging mentioned in authoritative guidelines or high-quality clinical studies and need to be determined by the clinic according to the actual situation of the patient. Therefore， it is recommended that the interpretation of tumor staging in reimbursement scope restrictions should be accurately defined and standardized， so as to improve the accuracy of the drug payment policy in the actual implementation process.

Objective To develop a neural network-based deep learning model for predicting postoperative recurrence in thyroid tumor patients and validate the model with external datasets for providing clinicians with a reliable decision support tool.Methods An artificial neural network structure was adopted in the study,with thyroid tumor data from the SEER database serving as the training set.External validation was conducted with open-source data from the University of California,Irvine(UCIrvine),and the data from 100 patients at a general tertiary hospital in Hunan province.The model's accuracy and reliability in predicting recurrence were evaluated through multiple performance metrics.Results Experimental results showed that the model outperformed Logistic model in recurrence prediction,with accuracy,recall rate,precision and F1 score reaching 0.915 3,0.981 8,0.921 1 and 0.947 4 in internal validation.Moreover,the model achieved accuracies,recall rates,precisions,F1 scores and ROC_AUC values of 0.832 9,0.945 5,0.841 4,0.890 4 and 0.78 on the UCIrvine validation set,while 0.870 0,0.880 0,0.862 7,0.871 3 and 0.80 on the local validation set.Conclusion This neural network-based predictive model exhibits excellent performance in thyroid tumor recurrence prediction,providing clinicians with a valuable decision support tool that can help optimize postoperative treatment plans and improve patient prognosis management.

Objective:To translate the Birth Satisfaction Scale-Revised (BSS-R) into Chinese and assesses the psychometric properties of this tool.Methods:Following the Beaton cross-cultural adaptation guidelines, the Chinese version of BSS-R was formed through forward translation, back translation, expert consultation, and pre-testing. The present study was conducted in a sample of 347 women took part between 6-8 weeks postpartum, from the postpartum clinics of two tertiary hospitals in Sichuan province.Results:Totally 340 questionnaires were effectively collected with a majority of respondents aged 30-39 accounting for 64.7% of the total. The Chinese version of BSS-R comprises 10 items, which were classified into three domains as stress experienced during labour, women′s personal attributes, and quality of care provision. The Cronbach α coefficient was 0.745 for the total scale and ranged from 0.523 to 0.738 for sub-scales. The split-half reliability was 0.746 for the total scale, and ranged from 0.523 to 0.733 for sub-scales. Exploratory factor analysis revealed the presence of three components with eigen values greater than 1, explaining a total variance of 58.868%. The fit indices showed that the original correlated three factor model of Chinese version of BSS-R was adequate.Conclusions:The Chinese version of BSS-R can be considered a clear, easily understandable and convenient tool for assessing the women′s birth satisfaction in Chinese cultural context.

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.