Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Objective To investigate the expression and prognostic significance of serum protease C1 inhib-itor(SERPING1)and plasminogen activator inhibitor type 1(SERPINE1)in patients with sepsis.Methods A total of 132 patients with sepsis treated in the hospital from March 2018 to March 2020 were se-lected as the sepsis group.According to whether they died within 28 days of admission,they were divided into a death group(n=34)and a survival group(n=98).Enzyme linked immunosorbent assay was used to detect the expression of serum SERPING1 and SERPINE1.Multivariate Logistic regression model and receiver oper-ating characteristic curve were used to study the value of serum SERPING1 and serpine1 in evaluating the prognosis of patients'death.Results[Compared with the control group,serum SERPING1(331.12±51.80 ng/L vs.639.04±91.12 ng/L)was lower and serum serpine1(412.67±64.84 ng/L vs.42.33±10.32 ng/L)was higher in the sepsis group,and the differences were statistically significant(P＜0.05).[Compared to the survival group,the levels of serum SERPINE1,procalcitonin,C-reactive protein,Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ)score and Sequential Organ Failure Assessment(SOFA)score in the death group were higher,while serum SERPING1 was lower,and the differences were statistically significant(all P＜0.05).Serum SERPING1 showed negative correlation with APACHE Ⅱ and SOFA scores(r=-0.779,-0.653,P＜0.05),while serum SERPINE1 showed positive correlation with APACHE Ⅱ and SO-FA scores(r=0.740,0.685,P＜0.05).APACHE Ⅱ score,SOFA score,and serum SERPINE1 were risk fac-tors affecting the prognosis of sepsis patients,while serum SERPING1 was a protective factor.The area under the curve of serum SERPING1 and SERPINE1 combined for the evaluation of the death in sepsis patients was 0.938(95％CI:0.893-0.968),which was significantly higher than 0.860(95％CI:0.812-0.899)and 0.838(95％CI:0.781-0.868)of the single detection,and the differences were statistically significant(Z=3.861,4.015,P＜0.001).Conclusion The elevated levels of serum SERPING1 and SERPINE1 in patients with sepsis are related to the severity of the patient's condition.The combination of the two has high prognos-tic value for sepsis patients.

Dental age estimation is a crucial aspect and one of the ways to accomplish forensic age estimation,and imaging technology is an important technique for dental age estimation.In recent years,some studies have preliminarily confirmed the feasibility of magnetic resonance imaging(MRI)in evaluating dental development,providing a new perspective and possibility for the evaluation of den-tal development,suggesting that MRI is expected to be a safer and more accurate tool for dental age estimation.However,further research is essential to verify its accuracy and feasibility.This article re-views the current state,challenges and limitations of MRI in dental development and age estimation,offering reference for the research of dental age assessment based on MRI technology.

Objective:We aimed to develop a novel visualized model based on nomogram to predict postoperative overall survival.Methods:This was a multicenter, retrospective, observational cohort study, including participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery from 11 medical centers in China from August 1, 2015 to June 30, 2018. Baseline characteristics, histopathological data and nutritional status, as assessed using Nutrition Risk Screening 2002 (NRS 2002) score and the scored Patient-Generated Subjective Global Assessment, were collected. The least absolute shrinkage and selection operator regression and Cox regression were used to identify variables to be included in the predictive model. Internal and external validations were performed.Results:There were 681 and 127 patients in the training and validation cohorts, respectively. A total of 188 deaths were observed over a median follow-up period of 59 (range: 58 to 60) months. Two independent predictors of NRS 2002 and Tumor-Node-Metastasis (TNM) stage were identified and incorporated into the prediction nomogram model together with the factor of age. The model's concordance index for 1-, 3- and 5-year overall survival was 0.696, 0.724, and 0.738 in the training cohort and 0.801, 0.812, and 0.793 in the validation cohort, respectively.Conclusions:In this study, a new nomogram prediction model based on NRS 2002 score was developed and validated for predicting the overall postoperative survival of patients with gastric colorectal cancer. This model has good differentiation, calibration and clinical practicability in predicting the long-term survival rate of patients with gastrointestinal cancer after radical surgery.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Objective To prepare and optimize the formulation of Albumin nanoparticles loading PROTAC molecule and observe the inhibition effect of nanoparticles on the proliferation and NAD⁺ metabolism of glioma cells. Methods Albumin nanoparticles loading NPT-B2 were prepared and characterized with a thermal driving method, and the prescription was optimized. An HPLC method was established to determine the content of NPT-B2. The proliferation inhibition of NPT-B2 and B2-BSA-NPs on U251 cells were investigated by the CCK8 method, and the degradation effects of NPT-B2 and B2-BSA-NPs on NAMPT in glioma cells were investigated by western blotting. Results The HPLC method was stable, with good linearity, precision, and recovery rate. The nanoparticles had a particle size of about 55.48 nm, a potential of about −12.9 mV, an encapsulation rate of about 94.74%, and a drug loading amount of about 8.61%. The IC₅₀ of NPT-B2 on glioma cells was 61.16 nmol/L, which had a degradation effect on NAMPT. The IC₅₀ of B2-BSA-NPs on glioma was 41.21 nmol/L, which had a very significant degradation effect on NAMPT. Conclusion Albumin nanoparticles loading PROTAC molecules were constructed. The prescription was optimized to improve the drug encapsulation rate, and the low water solubility of PROTAC molecule was improved, which had a significant inhibitory effect on the proliferation and NAD⁺ energy metabolism of glioma cells.

Endocrine-resistance remains a major challenge in estrogen receptor α positive (ERα⁺) breast cancer (BC) treatment and constitutively active somatic mutations in ERα are a common mechanism. There is an urgent need to develop novel drugs with new mode of mechanism to fight endocrine-resistance. Given aberrant ERα activity, we herein report the identification of novel covalent selective estrogen receptor degraders (cSERDs) possessing the advantages of both covalent and degradation strategies. A highly potent cSERD 29c was identified with superior anti-proliferative activity than fulvestrant against a panel of ERα⁺ breast cancer cell lines including mutant ERα. Crystal structure of ERα‒ 29c complex alongside intact mass spectrometry revealed that 29c disrupted ERα protein homeostasis through covalent targeting C530 and strong hydrophobic interaction collied on H11, thus enforcing a unique antagonist conformation and driving the ERα degradation. These significant effects of the cSERD on ERα homeostasis, unlike typical ERα degraders that occur directly via long side chains perturbing the morphology of H12, demonstrating a distinct mechanism of action (MoA). In vivo, 29c showed potent antitumor activity in MCF-7 tumor xenograft models and low toxicity. This proof-of-principle study verifies that novel cSERDs offering new opportunities for the development of innovative therapies for endocrine-resistant BC.

Humans ; Lacrimal Apparatus ; Endoscopy ; Carcinoma ; Eyelids

Objective To explore the epidemiological characteristics and trend of lung cancer mortality in Suzhou, to predict the future lung cancer mortality by ARIMA model, and to provide a scientific basis for the research of lung cancer prevention and control strategy. Methods The annual change percentage (APC) was used to analyze the annual change trend of lung cancer mortality from 2001 to 2020, and the ARIMA optimal model was employed to predict the lung cancer mortality from 2021 to 2025. Results The average annual crude mortality of lung cancer in Suzhou from 2001 to 2020 was 46.45/100 000, while the standardized mortality was 23.51/100 000. In recent 20 years, the crude mortality showed an upward trend and the standardized mortality showed a downward trend (APC crude rate = 2.51%, APC standardized rate = -0.78% , P < 0.001). The standardized mortality of lung cancer in men was 3.22 times that in women. The mortality of lung cancer in people over 45 years old increased with the increase of age, but the mortality in the 30-59 years old group showed a downward trend year by year. ARIMA model predicted that the annual trend of lung cancer crude mortality will tend to be flat in the next five years. Conclusion The crude mortality rate of lung cancer in Suzhou shows an upward trend, while the standardized mortality rate decreases year by year, suggesting that we should pay attention to the prevention and control of lung cancer in the elderly, accurately identify high-risk population of lung cancer, promote health publicity and education, carry out lifestyle intervention, and popularize the early screening of lung cancer.

Exosomes are nanoscale extracellular vesicles that are secreted by a variety of cells in the body. They carry particular miRNA, protein molecules, transcription factors, and other information molecules, and they play a role in the pathophysiological regulation of a number of diseases in the body. Exosomes can persist steadily in biological tissues and bodily fluids. Exosomes have quickly advanced in ophthalmology in recent years due to the extensive studies of exosomes in a variety of fields, such as diabetic retinopathy, age-related macular degeneration, autoimmune uveitis, corneal disease, glaucoma, and other diseases. The number of people who are blind caused by diabetic retinopathy is rising as living standards rise. However, it is still unclear how diabetic retinopathy works. In recent years, many studies have found that exosomes play an important role in diabetic retinopathy. In this paper, the most recent developments in exosome studies as they relate to the pathogenesis and progression of diabetic retinopathy are reviewed.