Background: The World Health Organization published the 2019 Integrated Care for Older People (ICOPE) framework to guide, assess, and promote the intrinsic capacity (IC) of older adults, referring to their physical and mental health. This study aims to investigate the relationship between IC and frailty among older adults. Methods: This cross-sectional study was conducted in a medical center in Taiwan in 2021. Two hundred ten patients over 65 admitted to the geriatric ward were invited to participate. The questionnaire included an IC measure, Fried Frailty Scale, and demographic items. The IC measure was ascertained using the six domains of ICOPE (cognition, mobility, nutrition, visual, hearing, and depressive symptoms). The Fried Frailty Scale was used to categorize participants as robust (Fried Frailty Scale=0), prefrail (Fried Frailty Scale=1-2), or frail (Fried Frailty Scale ≥3). Multinomial logistic regression was used to analyze the association between individual ICOPE domains and frailty stages, while adjusting for confounders. Results: Among the participants, 39.0% were prefrail, and 28.6% were frail. Limited mobility and depressive symptoms were significantly associated with prefrail (adjusted odds ratio [aOR]=4.44, 95% confidence interval [CI] 1.82–10.82; aOR=8.41, 95% CI 1.75–40.37) and frail (aOR=11.57, 95% CI 3.63–36.93; aOR=13.77, 95% CI 2.62–72.49) individuals, respectively. Malnutrition (aOR=4.01, 95% CI 1.18–13.62) and hearing loss (aOR=4.37, 95% CI 1.09–19.66) were significantly associated with frail older adults. Conclusion Limited mobility and depressive symptoms occurring at the prefrail stage could be used as assessment items for early detection of prefrail.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Alzheimer’s disease (AD) is a prevalent neurodegenerative condition characterized by progressive cognitive decline and memory loss. As the incidence of AD continues to rise annually, researchers have shown keen interest in the active components found in natural plants and their neuroprotective effects against AD. Quercetin, a flavonol widely present in fruits and vegetables, has multiple biological effects including anticancer, anti-inflammatory, and antioxidant. Oxidative stress plays a central role in the pathogenesis of AD, and the antioxidant properties of quercetin are essential for its neuroprotective function. Quercetin can modulate multiple signaling pathways related to AD, such as Nrf2-ARE, JNK, p38 MAPK, PON2, PI3K/Akt, and PKC, all of which are closely related to oxidative stress. Furthermore, quercetin is capable of inhibiting the aggregation of β‑amyloid protein (Aβ) and the phosphorylation of tau protein, as well as the activity of β‑secretase 1 and acetylcholinesterase, thus slowing down the progression of the disease.The review also provides insights into the pharmacokinetic properties of quercetin, including its absorption, metabolism, and excretion, as well as its bioavailability challenges and clinical applications. To improve the bioavailability and enhance the targeting of quercetin, the potential of quercetin nanomedicine delivery systems in the treatment of AD is also discussed. In summary, the multifaceted mechanisms of quercetin against AD provide a new perspective for drug development. However, translating these findings into clinical practice requires overcoming current limitations and ongoing research. In this way, its therapeutic potential in the treatment of AD can be fully utilized.

Objective To analyze the PLCβ4 gene mRNA expression and its clinical significance in hepatocellular carcinoma (HCC) based on TCGA database. Methods Based on the data on 424 clinical samples (including 374 cases of HCC tissues and 50 cases of nontumor liver tissues) in the TCGA database, Kaplan–Meier method, Cox regression analysis, and immune infiltration analysis were performed to evaluate the relationship between PLCβ4 gene and the clinical characteristics and survival prognosis of HCC patients. Correlation analysis between PLCβ4 gene and 24 types of immune cells was applied to investigate the relationship between PLCβ4 gene and immune cell infiltration and mRNA expression level of TP53 gene, a high-frequency mutation gene in HCC. In addition, paraffin sections of highly, moderately, and poorly differentiated tumor tissues and normal liver tissues from HCC patients were collected. The histopathological observation was carried out via HE staining method, and the expression levels of PLCβ4 and Ki-67 proteins in each clinical sample were verified through the immunohistochemical method. Results The expression level of PLCβ4 gene in HCC was significantly higher than that in normal tissues (P<0.01), and all patients in the PLCβ4 high-expression group had a significantly longer overall survival than those in the low-expression group (P<0.05), which suggested that PLCβ4 substantially affected the prognosis of HCC patients. Correlation analysis showed that the expression level of PLCβ4 gene was highly correlated with immune cell infiltration and the expression level of TP53 gene. As verified by clinical sample experiments, HE staining experiments and immunohistochemical results revealed that PLCβ4 gene expression in HCC tissue samples was significantly higher than that in normal tissues (P<0.001), and it was negatively correlated with the degree of differentiation. Conclusion PLCβ4 may serve as an independent prognostic factor in HCC and is expected to be a novel molecular target for HCC treatment.

Objective To observe the morphology of the superficial,middle,and deep layers of the masseter muscle and related bony structures in the lateral facial region of adults through gross anatomy,and to probe into the effects of these muscle layers on the bony structures of the lateral facial region.Methods The bilateral masseter muscles of 12 adult cadavers were exposed,and the superficial,middle,and deep layers were separated and measured for muscle length,tendon length,and muscle belly length.After the masseter muscles were stripped,the total thickness was measured,and the mandible and zygomatic arch were exposed to measure the angle of the mandibular angle,thickness of the zygomatic arch,and width of the zygomatic arch.Observations were made of the masseter tuberosities,and statistical analysis was conducted on their interrelations.Results The zygomatic arch thickness was positively correlated with the length of superficial,middle and deep masseter muscles and the length of superficial and middle masseter belly(r superficial masseter length=0.624,r middle masseter length=0.787,r deep masseter length=0.423,r superficial masseter belly length=0.493,r middle masseter belly length=0.548).The width of the zygomatic arch was positively correlated with the lengths of the superficial and middle muscle layers and the middle muscle belly length(r superficial masseter length=0.527,r middle masseter length=0.521,r middle masseterbelly length=0.437).The angle of the mandibular angle was only negatively correlated with the middle muscle belly length(r=-0.422).The tuberosities of the superficial and middle masseter muscles were not affected by the corresponding muscle layers;However,the tuberosity of the deep masseter was negatively correlated with the length of the deep muscle and the length of the deep tendon(r deep masseter length=-0.543,r deep masseter tendon length=-0.443).Conclusion In the masseter muscle layers of Chinese individuals,the superficial and middle layers have the most significant impact on the bony structures structures of the lateral facial region.These findings are of guiding significance for the remodeling of structures in the lateral facial region.

Objective To investigate the expressions of annexin A2 and glycogen synthesis kinase-3β(GSK-3β)in cutaneous squamous cell carcinoma(CSCC)tissues,and to analyze their correlation with CSCC as well as their clinical pathological diagnostic value.Methods The pathological tissues of 68 patients with CSCC and 40 patients with keratoacanthoma(KA)who underwent surgical treatment in the Department of Dermatology of the Second Hospital of Nanning from October 2020 to May 2024 were collected,and the surrounding normal skin tissues of 32 patients with benign skin diseases were used as controls.The expressions of annexin A2,GSK-3β and β-catenin were detected by immunohistochemistry and Western blotting.Spearman was used to evaluate the correlation between the expressions of annexin A2 and GSK-3β and the pathological characteristics in CSCC.The receiver operating characteristic(ROC)curve was drawn to analyze the clinical diagnostic value of annexin A2 and GSK-3β in CSCC.Results Compared with the normal skin tissues,the expressions of annexin A2 and β-catenin in CSCC increased,and GSK-3β decreased(P＜0.05);Compared with the KA tissues,the expression of annexin A2 in CSCC tissues increased(P＜0.05).The expression of annexin A2 was negatively correlated with that of GSK-3β in CSCC(r=-0.3901,P＜0.01).GSK-3β expression was related to tissue differentiation,with lower expression in poorly differentiated patients'cancer tissues(P＜0.05).The sensitivity of annexin A2 and GSK-3β for diagnosis of CSCC was 85.3%and 41.2%,respectively,with specificities of 46.9%and 84.4%respectively.The sensitivity of annexin A2 for distinguishing between CSCC and KA was 85.3%,with a specificity of 40.0%.Conclusion Annexin A2 and GSK-3β may be used as potential biomarkers for the early diagnosis or differential diagnosis of CSCC,and play important roles in the development of CSCC.Their mechanism may be related to the activation of Wnt/β-catenin signaling pathway.

Objective To investigate the value of intraoperative ultrasound radiomics for predicting isocitrate dehydrogenase 1(IDH1)mutation of high-grade glioma.Methods Ninety-five patients with high-grade glioma(WHO grade Ⅲ and Ⅳ)who underwent craniotomy glioma resection and ultrasound assisted tumor localization during operation and then confirmed by pathology were retrospectively enrolled.The patients were divided into training set(n=66,including 24 IDH1 mutation type and 42 IDH1 wild type)and validation set(n=29,including 11 IDH1 mutation type and 18 IDH1 wild type)at the ratio of 7∶3.Based on intraoperative ultrasound,radiomics features were extracted,the best ones were screened,and a radiomics model was established for predicting IDH1 mutation of high-grade glioma using random forest algorithm.Receiver operating characteristic(ROC)curve was plotted,the area under the curve(AUC)was calculated to evaluate the predictive efficacy of the model,and decision curve analysis(DCA)was used to evaluate the clinical value of the model.Results A total of 851 radiomics features were extracted based on intraoperative ultrasound,and finally 5 best ones were screened out to construct a radiomics model.The AUC of the radiomics model for predicting IDH1 mutation of high-grade glioma in training and validation sets was 0.902 and 0.707,respectively,with no significant difference(P=0.097).DCA maps showed that the clinical net benefit of the radiomics model was high.Conclusion Intraoperative ultrasound radiomics could effectively predict IDH1 mutation of high-grade glioma.

Objective:To investigate the prevalence and influencing factors of frailty among older adults diagnosed with non-ST-segment elevation acute coronary syndrome(NSTE-ACS).Methods:We conducted a cross-sectional study involving patients aged 65 years and older with NSTE-ACS, who were admitted to the Cardiology Center and the Department of Geriatrics at Beijing Friendship Hospital, Capital Medical University, between January 2020 and November 2021.Patients were categorized into non-frail, pre-frail, and frail groups based on the FRAIL scale.We collected clinical data, including general health conditions, comorbidities, laboratory results, treatments, and comprehensive geriatric assessments.Logistic regression analysis was employed to identify the influencing factors associated with frailty and pre-frailty in older adults with NSTE-ACS.Results:A total of 528 patients with NSTE-ACS were included in the study, comprising 308 males(58.3%)and 220 females(41.7%). The age range of participants was from 65 to 90 years, with a median age of 72(68, 76)years.The prevalence of frailty among older adults with NSTE-ACS was 11.4%(60/528), while pre-frailty was observed in 51.9%(274/528), and non-frailty in 36.7%(194/528). Compared to the non-frail and pre-frail groups, patients in the frail group were older, had a higher proportion of females, exhibited a greater prevalence of chronic diseases, and presented with elevated inflammatory markers.Additionally, frail patients demonstrated poorer nutritional status and reduced functional ability(all P<0.005). Risk factors for frailty in older adults with NSTE-ACS included older age( OR=1.110, 95% CI: 1.032-1.194, P=0.005), diabetes( OR=2.489, 95% CI: 1.091-5.679, P=0.030), cerebrovascular disease ( OR=4.151, 95% CI: 1.660-10.384, P=0.002), chronic kidney disease ( OR=42.874, 95% CI: 3.957-464.513, P=0.002), and elevated white blood cell levels( OR=1.424, 95% CI: 1.125-1.802, P=0.003). Conversely, being male( OR=0.252, 95% CI: 0.105-0.604, P=0.002)was identified as a protective factor against frailty in this patient population.For pre-frail older adults with NSTE-ACS, identified risk factors included diabetes( OR=1.882, 95% CI: 1.199-2.955, P=0.006), cerebrovascular disease( OR=1.938, 95% CI: 1.176-3.195, P=0.009), and chronic kidney disease ( OR=12.137, 95% CI: 1.536-95.934, P=0.018). Similarly, being male( OR=0.601, 95% CI: 0.376-0.961, P=0.033)was also a protective factor for pre-frailty in older adults with NSTE-ACS. Conclusions:The prevalence of frailty and pre-frailty among older adults with NSTE-ACS is notably high.Common risk factors for frailty and pre-frailty in this population include female gender, diabetes, cerebrovascular disease, and chronic kidney disease.

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