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 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 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 carry out cytogenetic and molecular genetic analysis for two infertile patients carrying rare small supernumerary marker chromosomes (sSMC).Methods:Two infertile patients who received reproductive and genetic counseling at CITIC Xiangya Reproductive and Genetic Hospital on October 31, 2018 and May 10, 2021, respectively were selected as the study subjects. The origin of sSMCs was determined by conventional G banding, fluorescence in situ hybridization (FISH) and copy number variation sequencing (CNV-seq). Microdissection combined with high-throughput whole genome sequencing (MicroSeq) was carried out to determine the fragment size and genomic information of their sSMCs. Results:For patient 1, G-banded karyotyping and FISH revealed that he has a karyotype of mos47, XY, del(16)(p10p12), + mar[65]/46, XY, del(16)(p10p12)[6]/48, XY, del(16)(p10p12), + 2mar[3].ish mar(Tel 16p-, Tel 16q-, CEP 16-, WCP 16+ ). CNV analysis has yielded a result of arr[GRCh37]16p12.1p11.2(24999364_33597595)×1[0.25]. MicroSeq revealed that his sSMC has contained the region of chromosome 16 between 24979733 and 34023115 (GRCh37). For patient 2, karyotyping and reverse FISH revealed that she has a karyotype of mos 47, XX, + mar[37]/46, XX[23].rev ish CEN5, and CNV analysis has yielded a result of seq[GRCh37]dup(5)(p12q11.2)chr5: g(45120001_56000000)dup[0.8]. MicroSeq results revealed that her sSMC has contained the region of chromosome 5 between 45132364 and 55967870(GRCh37). After genetic counseling, both couples had opted in vitro fertilization (IVF) treatment and preimplantation genetic testing (PGT). Conclusion:For individuals harboring sSMCs, it is vital to delineate the origin and structural characteristics of the sSMCs for their genetic counseling and reproductive guidance. Preimplantation genetic testing after microdissection combined with high-throughput whole genome sequencing (MicroSeq-PGT) can provide an alternative treatment for carrier couples with a high genetic risk.

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.

Objective To observe the value of habitat model based on lung CT for predicting brain metastasis(BM)of lung adenocarcinoma with epidermal growth factor receptor(EGFR)mutation.Methods Data of plain lung CT of 198 lung adenocarcinoma patients with EGFR-mutant were retrospectively analyzed.The patients were divided into training set(n=138)and test set(n=60)at the ratio of 7∶3,and further divided into BM subgroup and non-BM subgroup in each set.Then a logistic regression(LR)clinical model was constructed using variables being statistically different between subgroups in training set.For features extracted from tumor and subregion of tumor,radiomics models and habitat models were constructed based on random forest,Gaussian process(GP)and support vector machine(SVM)algorithms,and the best radiomics and habitat models with generalization ability were screened.LR combined model was constructed based on the predicted values of the best radiomics and habitat models with generalization ability,as well as the clinical model.Then receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the efficacy of each model for predicting BM of lung adenocarcinoma with EGFR-mutant.Spearman correlation analysis was performed to observe the correlations between Ki-67 and habitat features of lung adenocarcinoma with EGFR-mutant.Results AUC of LR clinical model,GP radiomics model,SVM habitat model and LR combined model for predicting BM of lung adenocarcinoma with EGFR-mutant was 0.700,0.726,0.801 and 0.834 in training set,0.754,0.600,0.715 and 0.848 in test set,respectively.AUC of LR combined model was higher than that of LR clinical model in training set(P＜0.001),also higher than that of GP radiomics model in test set(P=0.010).Compared with GP radiomics model and SVM habitat model,the performance of LR combined model was significantly and positively improved in training set(integrated discrimination improvement index[IDI]=8.60%,8.55%,both P＜0.001).Ki-67 level of EGFR-mutant lung adenocarcinoma was lowly and positively correlated with habitatmap_original_glszm_lalgle extracted from habitat map(│rs│=0.201,P=0.004).Conclusion The habitat model based on lung CT could be used to predict BM of lung adenocarcinoma with EGFR-mutant effectively.

OBJECTIVE: To investigate the prevalence, risk factors, duration and outcome of delirium in intensive care unit (ICU) patients. METHODS: A prospective observational study was conducted for critically ill patients admitted to the department of critical care medicine, the Affiliated Hospital of Guizhou Medical University from September to November 2021. Delirium assessments were performed twice daily using the Richmond agitation-sedation scale (RASS) and confusion assessment method of ICU (CAM-ICU) for patients who met the inclusions and exclusion criteria. Patient's age, gender, body mass index (BMI), underlying disease, acute physiologic assessment and chronic health evaluation (APACHE) at ICU admission, sequential organ failure assessment (SOFA) at ICU admission, oxygenation index (PaO₂/FiO₂), diagnosis, type of delirium, duration of delirium, outcome, etc. were recorded. Patients were divided into delirium and non-delirium groups according to whether delirium occurred during the study period. The clinical characteristics of the patients in the two groups were compared, and risk factors for the development of delirium were screened using univariate analysis and multivariate Logistic regression analysis. RESULTS: A total of 347 ICU patients were included, and delirium occurred in 57.6% (200/347) patients. The most common type was hypoactive delirium (73.0% of the total). Univariate analysis showed statistically significant differences in age, APACHE score and SOFA score at ICU admission, history of smoking, hypertension, history of cerebral infarction, immunosuppression, neurological disease, sepsis, shock, glucose (Glu), PaO₂/FiO₂ at ICU admission, length of ICU stay, and duration of mechanical ventilation between the two groups. Multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.045, 95% confidence interval (95%CI) was 1.027-1.063, P < 0.001], APACHE score at ICU admission (OR = 1.049, 95%CI was 1.008-1.091, P = 0.018), neurological disease (OR = 5.275, 95%CI was 1.825-15.248, P = 0.002), sepsis (OR = 1.941, 95%CI was 1.117-3.374, P = 0.019), and duration of mechanical ventilation (OR = 1.005, 95%CI was 1.001-1.009, P = 0.012) were all independent risk factors for the development of delirium in ICU patients. The median duration of delirium in ICU patients was 2 (1, 3) days. Delirium was still present in 52% patients when they discharged from the ICU. CONCLUSIONS The prevalence of delirium in ICU patients is over 50%, with hypoactive delirium being the most common. Age, APACHE score at ICU admission, neurological disease, sepsis and duration of mechanical ventilation were all independent risk factors for the development of delirium in ICU patients. More than half of patients with delirium were still delirious when they discharged from the ICU.
Humans ; Prevalence ; Critical Care ; Risk Factors ; Sepsis ; Intensive Care Units

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 preliminarily observe the feasibility of different immobilization techniques for total skin irradiation (TSI) using helical tomotherapy.Methods:Three eczema scrophuloderma patients treated with TSI in Sun Yat-sen University Cancer Center were immobilized with low-temperature thermoplastic in a prone position, diving suit combined with negative pressure vacuum bag in a supine position, low-temperature thermoplastic combined with vacuum bag in a supine position, respectively. Different immobilization effects were observed. The conformity index (CI) of the target area, heterogeneity index (HI) of the target area, and the mean dose (D mean) of the target area were calculated. Results:Three immobilization methods could achieve satisfactory immobilization effects, and all the dosimetric parameters of radiation treatment plans met the clinical requirements. The average set-up errors in the left and right, head and foot, and abdomen and back directions of three patients were (0.26±3.40) mm, (-2.63±4.63) mm and (6.13±4.86) mm, respectively. The CI, HI andD mean were0.56±0.09, 1.186±0.059 and (2586.56±63.28) cGy. Conclusions:Low-temperature thermoplastic or diving suits can be combined with vacuum bags for immobilization in TSI. The epidermal dose can be increased with bolus through the dose-building effect, which can provide a safe and reliable method for TSI in helical tomotherapy.

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.