BACKGROUND: Oral squamous cell carcinoma (OSCC) is a prevalent type of cancer with a high mortality rate in its late stages. One of the major challenges in OSCC treatment is the resistance to epidermal growth factor receptor (EGFR) inhibitors. Therefore, it is imperative to elucidate the mechanism underlying drug resistance and develop appropriate precision therapy strategies to enhance clinical efficacy. METHODS: To evaluate the efficacy of the combination of the Ca 2+ /calmodulin-dependent protein kinase II (CAMK2) inhibitor KN93 and EGFR inhibitors, we performed in vitro and in vivo experiments using two FAT atypical cadherin 1 ( FAT1 )-deficient (SCC9 and SCC25) and two FAT1 wild-type (SCC47 and HN12) OSCC cell lines. We assessed the effects of EGFR inhibitors (afatinib or cetuximab), KN93, or their combination on the malignant phenotype of OSCC in vivo and in vitro . The alterations in protein expression levels of members of the EGFR signaling pathway and SRY-box transcription factor 2 (SOX2) were analyzed. Changes in the yes-associated protein 1 (YAP1) protein were characterized. Moreover, we analyzed mitochondrial dysfunction. Besides, the effects of combination therapy on mitochondrial dynamics were also evaluated. RESULTS: OSCC with FAT1 mutations exhibited resistance to EGFR inhibitors treatment. The combination of KN93 and EGFR inhibitors significantly inhibited the proliferation, survival, and migration of FAT1 -mutated OSCC cells and suppressed tumor growth in vivo . Mechanistically, combination therapy enhanced the therapeutic sensitivity of FAT1 -mutated OSCC cells to EGFR inhibitors by modulating the EGFR pathway and downregulated tumor stemness-related proteins. Furthermore, combination therapy induced reactive oxygen species (ROS)-mediated mitochondrial dysfunction and disrupted mitochondrial dynamics, ultimately resulting in tumor suppression. CONCLUSION Combination therapy with EGFR inhibitors and KN93 could be a novel precision therapeutic strategy and a potential clinical solution for EGFR-resistant OSCC patients with FAT1 mutations.
Humans ; ErbB Receptors/metabolism* ; Mouth Neoplasms/metabolism* ; Cell Line, Tumor ; Animals ; Drug Resistance, Neoplasm/genetics* ; Cadherins/metabolism* ; Carcinoma, Squamous Cell/metabolism* ; Mice ; Mutation/genetics* ; Mice, Nude ; Protein Kinase Inhibitors/therapeutic use* ; Cetuximab/pharmacology* ; Afatinib/therapeutic use* ; Cell Proliferation/drug effects* ; Signal Transduction/drug effects*

BACKGROUND: Preclinical studies have indicated that Angong Niuhuang Pills (ANP) reduce cerebral infarct and edema volumes. This study aimed to investigate whether ANP safely reduces cerebral infarct and edema volumes in patients with moderate to severe acute ischemic stroke. METHODS: This randomized, double-blind, placebo-controlled pilot trial included patients with acute ischemic stroke with National Institutes of Health Stroke Scale (NIHSS) scores ranging from 10 to 20 in 17 centers in China between April 2021 and July 2022. Patients were allocated within 36 h after onset via block randomization to receive ANP or placebo (3 g/day for 5 days). The primary outcomes were changes in cerebral infarct and edema volumes after 14 days of treatment. The primary safety outcome was severe adverse events (SAEs) for 90 days. RESULTS: There were 57 and 60 patients finally included in the ANP and placebo groups, respectively for modified intention-to-treat analysis. The median age was 66.0 years, and the median NIHSS score at baseline was 12.0. The changes in cerebral infarct volume at day 14 were 0.3 mL and 0.4 mL in the ANP and placebo groups, respectively (median difference: -7.1 mL; interquartile range [IQR]: -18.3 to 2.3 mL, P = 0.30). The changes in cerebral edema volume of the ANP and placebo groups on day 14 were 11.4 mL and 4.0 mL, respectively ( median difference: 3.0 mL, IQR: -1.3 to 9.9 mL, P = 0.15). The rates of SAE within 90 days were similar in the ANP (3/57, 5%) and placebo (7/60, 12%) groups ( P = 0.36). Changes in serum mercury and arsenic concentrations were comparable. In patients with large artery atherosclerosis, ANP reduced the cerebral infarct volume at 14 days (median difference: -12.3 mL; IQR: -27.7 to -0.3 mL, P = 0.03). CONCLUSIONS: ANP showed a similar safety profile to placebo and non-significant tendency to reduce cerebral infarct volume in patients with moderate-to-severe stroke. Further studies are warranted to assess the efficacy of ANP in reducing cerebral infarcts and improving clinical prognosis. TRAIL REGISTRATION Clinicaltrials.gov , No. NCT04475328.
Aged ; Female ; Humans ; Male ; Middle Aged ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Ischemic Stroke/drug therapy* ; Pilot Projects ; Stroke/drug therapy* ; Treatment Outcome

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

ObjectiveTo construct and validate a clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure （CHF），aiming to assist clinical diagnosis and provide tools and methods for individualized treatment of CHF. MethodsThe clinical data of patients with chronic heart failure treated at Dongzhimen Hospital of Beijing University of Chinese Medicine from January 2022 to January 2024 were retrospectively collected. The patients were randomly divided into a training group and a validation group with a ratio of 7∶3. First， the least absolute shrinkage and selection operator （LASSO） regression analysis was used to preliminarily screen the predictive factors affecting the diagnosis of Qi deficiency and blood stasis syndrome in CHF. Subsequently， the Logistic regression method was applied to conduct a more in-depth and detailed analysis of these factors. Variables with P<0.05 in the results of the multi-factor Logistic regression were carefully selected and included. Based on the regression coefficients obtained from this analysis， a model was constructed， and a nomogram was accurately drawn. Using R software，the receiver operating characteristic （ROC） curve，calibration curve，and decision curve analysis （DCA） were precisely drawn. These analyses were used to comprehensively evaluate the model from three crucial aspects： discrimination，calibration，and clinical applicability. Additionally， the accuracy，specificity，sensitivity，positive predictive value，and negative predictive value of the model were meticulously calculated to conduct a more all-round and comprehensive assessment. ResultsIn total， 168 cases were successfully obtained in the training group， and 71 cases were included in the validation group. After a thorough comparison， it was found that there were no statistically significant differences in the baseline data between the two groups. After being rigorously screened by the LASSO-multivariate logistic regression method， dark red tongue，smoking history，cardiac troponin I，and N-terminal pro-B-type natriuretic peptide （NT-ProBNP） were identified as the influencing factors for diagnosing patients with the Qi deficiency and blood stasis syndrome in CHF. The constructed model demonstrated an area under the curve （AUC） of 0.812 in the training group and 0.719 in the validation group. The calibration curve showed that the predicted curve of the model was close to the actual observed curve. DCA indicated that the model could provide substantial clinical benefits for patients at the decision thresholds ranging from 0.2 to 0.9. ConclusionThe clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure constructed in this study shows good performance. It has certain application value in clinical practice， which may contribute to the improvement of the diagnosis and treatment of CHF patients with this syndrome.

[Objective] To use Platelet Additive Solution Ⅲ M to suspend concentrated platelets and evaluate their quality at different storage periods, in order to investigate the optimal ratio of Ⅲ M to plasma in the medium for storing concentrated platelets. [Methods] Disposable plastic blood bags with platelet storage bags were used to collect whole blood from healthy voluntary blood donors, and concentrated platelets were collected by plasma-rich method, with a volume of about 50 mL and ≥4.0×10¹⁰ platelets contained in each bag. According to the Platelet Addictive Solution ⅢM/plasma volume ratio in the medium of suspended platelets, the platelets were divided into 3 groups: control group (plasma only), experimental group 1(Platelet Addictive Solution ⅢM/plasma volume ratio of 6.5∶3.5) and experimental group 2 (low plasma group, Platelet Addictive Solution ⅢM/plasma volume ratio of 9∶1), each group of 50 samples. Three groups of platelets were stored at (22±2) ℃ at a flat-bed shaker, and 5 mL were sampled by sterile connection at day 1, 3, 5 and 7 respectively to detect platelet count, pH value, lactate dehydrogenase, CD62P positive rate and Annexin V positive rate. All the data were analyzed with SPSS24.0 software. One-way ANOVA was employed to compare the differences among three groups. In order to pairwise comparisons between means of multiple samples, Bonferroni method was applied. [Results] With the extension of storage time, the platelet count decreased and the Annexin V positive rate increased in the 3 groups, and the difference of the 3 groups was not statistically significant (P>0.05). The pH value decreased in the 3 groups, with values at day 1, 3, 5 and 7 of 7.44±0.13 vs 7.44±0.14 vs 7.41±0.11, 7.31±0.68 vs 7.43±0.23 vs 7.22±0.12, 7.30±0.15 vs 7.42±0.14 vs 7.17±0.12, 7.29±0.33 vs 7.26±0.18 vs 7.04 ± 0.12, respectively. The pH decline in the control group and experiment group 1 was minor, with no statistically significant difference, but experiment group 2 showed relatively larger decreases in day 5 and day 7, with statistically significant difference (P<0.05). LDH concentrate was elevated in 3 groups (mmol/L), with values at day 1,3,5 and 7 of 169.62±99.33 vs 105.80±150.71 vs 77.14±105.38, 225.10±112.86 vs 116.00±72.77 vs 94.42±88.74, 249.42±79.55 vs 119.00±53.51 vs 118.35±80.39, 253.34±86.95 vs 147.71±90.71 vs 124.68±128.68 respectively. Compared with the control group, the difference was statistically significant (P<0.05). Experimental group1 had the smallest increase; CD62P positive rate increased in 3 groups (%), with values at day 1, 3, 5 and 7 of 26.22±11.74 vs 23.48±12.48 vs 40.49±11.86, 41.29±8.36 vs 33.53±25.64 vs 50.42±22.36, 59.59±10.13 vs 36.39±23.10 vs 50.94±20.50, 72.92±15.44 vs 55.54±23.65 vs 61.89±18.82 respectively. Compared with the control group, the increase in experiment group1 was smaller, and the difference was statistically significant (P<0.05). [Conclusion] Platelet Addictive Solution ⅢM/plasma volume ratio of 6.5∶3.5 is superior to traditional plasma in maintaining platelet quality during the in vitro preservation period of platelets.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Background: and Purpose Ruptured intracranial aneurysms (RIA) are associated with a mortality rate of up to 40% in the Chinese population, highlighting the critical need for targeted treatment interventions for at-risk individuals. Although the impact of aldehyde dehydrogenase 2 (ALDH2) gene mutations on susceptibility to intracranial aneurysms (IA) is well documented, the potential connection between ALDH2 rs671 single-nucleotide polymorphism (SNP) and RIA remains unexplored. Given the increased prevalence of ALDH2 gene mutations among Chinese Han individuals, it is clinically relevant to investigate the link between ALDH2 rs671 SNP and IA rupture. Methods: A prospective study was conducted on 546 patients diagnosed with IA to investigate the association between ALDH2 rs671 SNP and the risk of IA rupture. Results: The ALDH2 rs671 SNP (ALDH2*2) was significantly more prevalent in patients with unruptured IA (UIA) than in those with RIA (32.56% vs. 18.58%, P=0.004). Multivariate logistic regression analysis revealed that people with the ALDH2 mutation (ALDH2*1/*2 and ALDH2*2/*2 gene type) had a significantly reduced odds ratio (OR=0.49; 95% confidence level [CI] 0.27–0.88; P=0.018) for RIAs. Age-specific subgroup analysis indicated that the ALDH2 mutation provided a stronger protective effect in individuals aged 60 years and above with IA compared to those under 60 years old (OR=0.38 vs. OR=0.52, both P<0.05). Conclusion The incidence of RIA was significantly higher in individuals with a normal ALDH2 gene (ALDH2*1/*1) than in those with an ALDH2 rs671 SNP (ALDH2*1/*2 or ALDH2*2/*2). ALDH2 rs671 SNP may serve as a protective factor against RIA in the Chinese Han population.

Objective:To investigate the risk factors associated with post-prematurity respiratory disease (PPRD) in very preterm infants.Methods:A prospective cohort study was conducted, enrolling 369 very preterm infants who were admitted to the neonatal intensive care unit of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, within one week of birth from January 2019 to June 2023. Data on maternal and infant clinical characteristics, neonatal morbidities, and treatments during hospitalization were collected. The very preterm infants were divided into 2 groups based on whether they developed PPRD. Continuous variables were compared using Mann-Whitney U test, while categorical variables were compared using χ2 tests or continuity correction χ2 test. Multivariate Logistic regression analysis was used to identify the independent risk factors for PPRD in very preterm infants. Results:Among the 369 very preterm infants, 217 cases(58.8%) were male, with a gestational age of 30 (28, 31) weeks at birth and a birth weight of 1 320 (1 085, 1 590) g. Of these, 116 cases (31.4%) developed PPRD, while 253 cases (68.6%) did not. The very preterm infants in the PPRD group had a lower gestational age and lower birth weight (both, P<0.001). The PPRD group also had a higher proportion of males, lower Apgar scores at the 1 th minute after birth and the 5 th minutes after birth, a higher rate of born via cesarean delivery, and a higher incidence of bronchopulmonary dysplasia, more pulmonary surfactant treatment, longer durations of mechanical ventilation, longer total oxygen therapy, and lower Z-score for weight at discharge (all P<0.05). Multivariate Logistic regression analysis showed that gestational age ( OR=0.85, 95% CI 0.73-0.99, P=0.037), born via cesarean delivery ( OR=2.23, 95% CI 1.21-4.10, P=0.010), a duration of mechanical ventilation ≥7 days ( OR=2.51, 95% CI 1.43-4.39, P=0.001), and a Z-score for weight at discharge ( OR=0.82, 95% CI 0.67-0.99, P=0.040) were all independent risk factors for PPRD in very preterm infants. Conclusion:Very preterm infants with a small gestational age, born via cesarean section, mechanical ventilation ≥7 days, and a low Z-score for weight at discharge should be closely monitored for PPRD, and provided with standardized respiratory management after discharge.