Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Objective:To summarize the clinical and genetic characteristics of children with β-ketothiolase deficiency (BKTD).Methods:The clinical characteristics, biochemical, markers detected by tandem mass spectrometry (MS/MS) and gas chromatography-mass spectrometry (GC/MS), as well as the variants in ACAT1 gene among 5 children with BKTD in Children′s Hospital of Chongqing Medical University between October 2018 and December 2022 were retrospectively analyzed.Results:The onset age of the disease in 5 patients (4 males and 1 female) ranged from 9.7 to 28.0 months. During the acute phase, severe metabolic acidosis was observed with a pH of 6.9-7.1, as well as hypoglycaemia (2.3-3.4 mmol/L) and positive urinary ketone bodies (+-++++). Blood levels of methylcrotonyl carnitine, methylmalonyl carnitine and malonyl carnitine were 0.03-0.42, 0.34-1.43 and 0.83-3.53 μmol/L respectively and were significantly elevated. Urinary 2-methyl-3-hydroxybutyric acid was 22-202 and 3-hydroxybutyric acid was 4-6 066, both were higher than the normal levels. Methylcrotonylglycine was mild elevated (0-29). The metabolites detected by MS/MS and GC/MS were significantly reduced after treatment. Analysis of ACAT1 gene mutation was performed in 5 children. Most variants were missense (8/9). Four previously unreported variants were identified: c.678G>T (p.Trp226Cys), c.302A>G (p.Gln101Arg), c.627_629dupTGA (p.Asn209_Glu210insAsp) and c.316C>T (p.Gln106Ter), the first 2 variants were predicted to be damaging by SIFT, PolyPhen-2 and Mutation Taster software. c.316C>T (p.Gln106Ter) is a nonsense variant.Conclusions:β-ketothiolase deficiency is relatively rare, lacks specific clinical manifestations, however severe metabolic acidosis, hypoglycemia, and ketosis during the acute onset were consistent findings. Missense mutations in the ACAT1 gene are common genetic causes of β-ketothiolase deficiency.

Objective:To identify a strain isolated from the cerebrospinal fluid of a patient and to investigate its biological characteristics.Methods:The strain was analyzed by several methods including Gram staining, biochemical identification, 16S rRNA and recN gene sequencing, average nucleotide identity (ANI), antibiotic susceptibility testing and detection of drug resistance and virulence genes. Results:The strain was Gram-positive cocci and formed α-hemolytic colonies on the blood plate. It was identified as Streptococcus parasuis by 16S rRNA, recN gene and whole-genome sequencing. It was sensitive to multiple antibiotics and carried the genes encoding a variety of virulence factors such as adhesion. Conclusions:Streptococcus parasuis could cause human infection and be identified by whole-genome sequencing.

Objective:To explore whether deep learning could apply to recognize the recurrent laryngeal nerve in the video of unilateral axillary approach endoscopic thyroidectomy.Methods:Videos of endoscopic thyroidectomy via unilateral axillary approach in Peking Union Medical College Hospital from Jul. 1st, 2020 to May. 1st, 2021 were collected. Videos containing the recurrent laryngeal nerve were selected, and the outline of recurrent laryngeal nerve were marked by two senior thyroid surgeons and staffs. Data were divided into training set and test set in a ratio of 5:1, and classified into high, medium and low recognition group according to difficulty of recognizing the outline of the nerve. The neuron network was based on PSPNet combined with Resnet50. All data were analyzed by R (ver. 4.0.2) .Results:A total of 38 videos including 35,501 frames of pictures were included in this study. 29, 704 frames of 32 videos were in our training set and 5797 frames of 6 videos were in the test set. When the intersection over union (IOU) threshold is 0.1, the sensitivity and precision is 100.0%/92.1%, 95.8%/80.2% and 81.0%/80.6% in high, medium and low recognition group respectively. When the IOU threshold is 0.5, the sensitivity and precision is 92.6%/85.3%, 71.7%/60.5% and 38.1%/37.9% in high, medium and low recognition group respectively, indicating that neuron network could located the outline of recurrent laryngeal nerve in high and medium recognition group. False negatives were often due to small targets and unclear boundaries.Conclusion:Recurrent laryngeal nerve recognition based on deep learning is feasible and has potential application value in endoscopic thyroidectomy, which may help surgeons reduce the risk of accidental injury of recurrent laryngeal nerve and improve the safety of thyroidectomy.

Objective:To explore whether deep learning could apply to recognize the recurrent laryngeal nerve (RLN) in videos of endoscopic thyroidectomy (ETE) via breast approach.Methods:Videos of ETE via breast approach in Peking Union Medical College Hospital from Feb. 2020 to Aug. 2021 were collected. Videos containing RLN were selected, and the outline of RLN was marked by two thyroid surgeons. Then data were divided into a training set and a test set in a ratio of 5:1 and classified into the high and low difficulty group according to a senior thyroid surgeon’s opinion. Those pictures were input to D-LinkNet model. Precision, sensitivity and mean dice index was calculated.Results:A total of 46 videos including 153, 520 frames of pictures were included in this study. 131,039 frames of 39 videos were in the training set and 22,481 frames of 7 videos were in the test set. When the intersection over union threshold was 0.1, the sensitivity and precision was 92.9%/72.8% and 47.6%/54.9% in high and low recognition group, respectively. When the intersection over union threshold was 0.5, the sensitivity and precision turned to 85.8%/67.2% and 37.6%/43.5% in high and low difficulty group, respectively. Mean Dice index was 0.781 and 0.663 in high and low difficulty group, respectively.Conclusions:RLN recognition based on deep learning is feasible and has potential application value in ETE, which may help surgeons reduce the risk of accidental injury of RLN and improve the safety of thyroidectomy.

Objective:To summarize the experience and the clinical data of patients with primary hyperparathyroidism undergoing endoscopic parathyroidectomy.Methods:A total of 24 patients who underwent endoscopic parathyroidectomy for primary hyperparathyroidism in Peking Union Medical College Hospital during Feb. 2021 to May. 2022 were concluded in this study (20 cases of parathyroidectomy via axillary approach and 4 cases of parathyroidectomy via thoracic and breast approach) . The operation time, postoperative drainage, length of stay, level of parathyroid hormone and serum calcium of those patients were collected. Postoperative complications and recurrence of hyperparathyroidism were also observed.Results:The postoperative levels of serum parathyroid hormone and serum calcium were significantly reduced (over 50%) compared with preoperative level ( P<0.05) . The average operation time was (96±22) min (64-157 min) . The mean postoperative drainage volume was (47±16) ml on day 1, (46±11) ml on day 2, and (30±9) ml on day 3, respectively. The average length of postoperative hospital stay was (2.8±1.1) days (2-6 days) . In one case of parathyroidectomy via axillary approach, the operation was converted to open surgery because of the low position of lesion. Other cases completed endoscopic surgery and obtained satisfactory cosmetic results. There were no postoperative complications such as bleeding, permanent hoarseness, coughing while drinking water, or surgical site infection. The mean follow-up time was (7.4±4.2) months (1-16 months) . There was no obvious discomfort and no recurrence during follow-up. Conclusion:Endoscopic parathyroidectomy is safe and effective in the treatment of primary hyperparathyroidism, which can be used as a surgical option for patients with cosmetic requirements.

Objective:To investigate the application value of machine learning algorithms for gauze detection in laparoscopic pancreatic surgery.Methods:The retrospective and descriptive study was conducted. The 80 intact laparoscopic pancreatic surgery videos from Peking Union Medical College Hospital of Chinese Academy of Medical Sciences with timing of July 2017 to July 2020 were collected. The training set was used to train the neural network, and the test set was used to test the ability of neural network for gauze detection under different difficulties. Under the supervision of two superior doctors, videos that containing gauze were selected and classified according to recognition difficulty into three difficulty level including easy, normal and hard difficulty, and further divided based on random number method into training set with 61 videos and test set with 19 videos in a ratio of 3:1 roughly. The minimum enclosing rectangle of the gauze were marked frame by frame. All images were input to the neural network model for training after normalization and preprocessing. For every image, the output of neural network is the predicted minimum enclosing rectangle of gauze. The intersection over union >0.5 was identified as positive result. Observation indicators: (1) video annotation and classification; (2) test outcomes of neural network for test set.Count data were represented as absolute numbers or percentages.Results:(1) Video annotation and classification: a total of 26 893 frames of images form 80 videos were annotated, with 61 videos including 22 564 frames of images as the training set and 19 videos including 4 329 frames of images as the test set. Of the training set, 19 videos including 5 791 frames of images were classifed as easy difficulty, 38 videos including 15 771 frames of images were classifed as normal difficulty, 4 videos including 1 002 frames of images were classifed as hard difficulty, respectively. Of the test set, 4 videos including 1 684 frames of images were classifed as easy difficulty, 6 videos including 1 016 frames of images were classifed as normal difficulty, 9 videos including 1 629 frames of images were classifed as hard difficulty, respectively. (2) Test outcomes of neural network for test set: the overall sensitivity and accuracy of gauze detection by neural network in the test set were 78.471%(3 397/4 329) and 69.811%(3 397/4 866), respectively. The sensitivity and accuracy of gauze detection by neural network were 94.478%(1 591/1 684) and 83.168%(1 591/1 913) in easy difficulty test set. The sensitivity and accuracy of gauze detection by neural network were 80.413%(817/1 016) and 70.859%(817/1 153) in normal difficulty test set, 60.712%(989/1 629) and 54.944%(989/1 800)in hard difficulty test set. The frame rate reached more than or equally to 15 fps. The overall false negative rate and false positive rate of gauze detection by neural network in the test set were 21.529%(932/4 329) and 30.189%(1 469/4 866), respectively. The false negative was mainly due to the existence of blurred images, too small gauze exposure or blood immersion of gauze. The false positive was caused by the reflection of connective tissue or body fluids.Conclusion:The machine learning algorithms for gauze detection in laparoscopic pancreatic surgery is feasible, which could help medical staff identify gauze.

Objective To investigate the effect of nursing intervention on blood glucose control of ICU patients with stress hyperglycemia.Methods A total of 120 ICU stress hyperglycemia patients were randomly divided into observation group and control group,with 60 cases per group.The control group received routine nursing and the observation group was given intensive glucose control nursing,and blood glucose control,insulin dosage,recovery and prognosis of the two groups were compared.Results The insulin dosage in the observation group was less than that in the control group,the blood glucose after intervention was lower,the blood glucose was less,the time of blood glucose reaching standard was shorter than the control group,and the hypoglycemia was less than that in the control group,the differences were statistically significant (P ＜0.05).The mechanical ventilation time and ICU time in the observation group were shorter,the incidence rate of MODS was lower than that in the control group,and the differences were statistically significant (P ＜ 0.05).Conclusion Intensive hypoglycemic nursing can improve the level of blood glucose control,reduce the dosage of insulin,and improve the prognosis of patients with ICU stress hyperglycemia.

Objective To explore the risk factors of stress hyperglycemia in patients with ICU,and to put forward the countermeasures of nursing intervention.Methods The clinical data of 530 ICU patients in our hospital were retrospectively analyzed,factors that may lead to stress-induced hyperglycemia were given Logistic single factor and multi factor regression analysis.Results There were significant differences in age,etiology,BMI,APACHE Ⅱ score (P ＜ 0.05).Multivariate Logistic regression analysis showed that age ≥60 years old,cerebral infarction,severe acute pancreatitis and traumatic brain injury,APACHE score ＞ 15 points,BMI ≥24 kg/m2 were independent risk factors for the occurrence of stress hyperglycemia.Conclusion Mortality of ICU stress hyperglycemia patients is high,so the monitoring and targeted nursing measures should be strengthened.

Objective To investigate the effect of nursing intervention on blood glucose control of ICU patients with stress hyperglycemia.Methods A total of 120 ICU stress hyperglycemia patients were randomly divided into observation group and control group,with 60 cases per group.The control group received routine nursing and the observation group was given intensive glucose control nursing,and blood glucose control,insulin dosage,recovery and prognosis of the two groups were compared.Results The insulin dosage in the observation group was less than that in the control group,the blood glucose after intervention was lower,the blood glucose was less,the time of blood glucose reaching standard was shorter than the control group,and the hypoglycemia was less than that in the control group,the differences were statistically significant (P ＜0.05).The mechanical ventilation time and ICU time in the observation group were shorter,the incidence rate of MODS was lower than that in the control group,and the differences were statistically significant (P ＜ 0.05).Conclusion Intensive hypoglycemic nursing can improve the level of blood glucose control,reduce the dosage of insulin,and improve the prognosis of patients with ICU stress hyperglycemia.