With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

BACKGROUND:As a common disease in middle-aged and elderly,osteoarthritis is difficult to cure,and the pathogenesis is not clear.Long non-coding RNA participates in the pathogenesis of osteoarthritis through many ways,such as regulating translation,promoting or inhibiting mRNA,and adsorbing miRNAs. OBJECTIVE:To review the types of common long non-coding RNA in osteoarthritis,and the influence of multiple long non-coding RNAs on the pathological factors related to osteoarthritis,to analyze the future application of long non-coding RNAs in osteoarthritis. METHODS:Literature retrieval was conducted in CNKI,WanFang Data,VIP database,PubMed,Web of Science and Sciencedirect databases,using the search terms of"osteoarthritis,degenerative joint disease,degenerative arthritis,OA,LncRNA,long non-coding RNA,long noncoding RNA,long intergenic non-coding RNA"in Chinese and English.All relevant literature published from 1976 and May 2024 was retrieved.After literature screening,induction,analysis and summary,93 articles were finally included for review. RESULTS AND CONCLUSION:This review collected 25 long non-coding RNAs that are well studied with osteoarthritis.Long non-coding RNAs,as a molecular sponge for miRNA,are competing endogenous RNAs to competitively adsorb miRNAs and then affect downstream targets.Long non-coding RNAs can regulate physiopathological processes such as chondrocyte apoptosis and proliferation,cartilage extracellular matrix degradation,and inflammatory responses.Long non-coding RNAs are expected to become a biomarker and potential therapeutic target for the clinical diagnosis and therapeutic prognosis of osteoarthritis,and it may become a new strategy for the clinical treatment of osteoarthritis in the future.

Objective: To analyze the association between alcohol consumption and lumbar disc herniation (LDH), so as to provide a reference for the development of prevention and treatment strategies for LDH. Methods: From May to July 2022, permanent residents aged ≥18 years from eight counties (cities/districts) in Gansu Province were selected using a multistage stratified random sampling method. Data on basic characteristics, alcohol consumption in the past 30 days, hypertension, and diabetes mellitus were collected through questionnaire surveys. LDH was determined based on imaging findings, combined with disease history or clinical symptoms. Multivariable logistic regression model was used to analyze the association between alcohol consumption and LDH, with subgroup analyses conducted by gender, age, ethnicity, and altitude of residence. Propensity score matching (PSM) was utilized for sensitivity analysis. Results: A total of 4 545 individuals were surveyed. There were 2 026 (44.58%) males and 2 519 (55.42%) females. The mean age was (44.82±15.33) years. The study participants were predominantly of Han ethnicity, with 2 598 persons accounting for 57.17%. The altitude of residence was mainly above 3 500 m, with 1 941 persons accounting for 42.71%. There were 574 alcohol drinkers, accounting for 12.63%. LDH was detected in 1 035 cases, with a detection rate of 22.77%. Multivariable logistic regression analysis showed that after adjusting for gender, age, physical activity, and hypertension, compared to non-drinking residents, alcohol-consuming residents exhibited a 27.6% reduction in the risk of LDH (OR=0.724, 95%CI: 0.544-0.963). No significant interaction effects on LDH risk were observed between alcohol consumption and gender, age, ethnicity, or altitude of residence (all Pfor interaction >0.05). The results of the sensitivity analysis indicated that compared to non-drinking residents, alcohol-consuming residents exhibited a 38.8% reduction in the risk of LDH (OR=0.612, 95%CI: 0.382-0.976). Conclusion Alcohol consumption was statistically associated with a lower risk of LDH.

Objective To explore the effect of cognitive behavioral therapy(CBT)on improving anxiety,depression,sleep,and fatigue in patients with post-intensive care syndrome(PICS).Methods Sixty PICS patients transferred from the department of respiratory intensive care unit(RICU)of Puyang Oilfield General Hospital from September 2022 to February 2023 were selected as the study subjects.Patients were divided into a control group and an intervention group using a random number table method,with 30 cases in each group.The control group only received routine health education,while the intervention group received CBT on the basis of routine health education.After 3 months of intervention,the changes in hospital anxiety and depression scale(HADS),Pittsburgh sleep quality index(PSQI),and fatigue assessment scale(FAS)before and after intervention were observed in both groups of patients.Results There was no statistically significant difference in anxiety,depression,PSQI,and FAS scores between the two groups of patients before intervention.The scores gradually decreased over time and reached the lowest level at 3 months after intervention.After intervention,the above scores in the intervention group were significantly lower than those in the control group(anxiety score:5.06±1.76 vs.9.33±1.42,depression score:6.93±1.64 vs.7.63±1.61,PSQI score:5.27±1.37 vs.8.43±2.56,FAS score:5.67±0.47 vs.9.97±0.21,all P<0.05).Conclusion Implementing CBT for PICS patients can alleviate anxiety and depression,improve their sleep quality and fatigue symptoms,thereby enhancing their quality of life.

Objective To explore the effect of cognitive behavioral therapy(CBT)on improving anxiety,depression,sleep,and fatigue in patients with post-intensive care syndrome(PICS).Methods Sixty PICS patients transferred from the department of respiratory intensive care unit(RICU)of Puyang Oilfield General Hospital from September 2022 to February 2023 were selected as the study subjects.Patients were divided into a control group and an intervention group using a random number table method,with 30 cases in each group.The control group only received routine health education,while the intervention group received CBT on the basis of routine health education.After 3 months of intervention,the changes in hospital anxiety and depression scale(HADS),Pittsburgh sleep quality index(PSQI),and fatigue assessment scale(FAS)before and after intervention were observed in both groups of patients.Results There was no statistically significant difference in anxiety,depression,PSQI,and FAS scores between the two groups of patients before intervention.The scores gradually decreased over time and reached the lowest level at 3 months after intervention.After intervention,the above scores in the intervention group were significantly lower than those in the control group(anxiety score:5.06±1.76 vs.9.33±1.42,depression score:6.93±1.64 vs.7.63±1.61,PSQI score:5.27±1.37 vs.8.43±2.56,FAS score:5.67±0.47 vs.9.97±0.21,all P<0.05).Conclusion Implementing CBT for PICS patients can alleviate anxiety and depression,improve their sleep quality and fatigue symptoms,thereby enhancing their quality of life.

Objective To investigate the differences in blood pressure phenotypes,renal pathological changes,and related pathogenic pathways in genetically diverse hypertensive mice obtained from 13 strains.Methods The genotypes of Cckbr+/+,Cckbr+/-and Cckbr-/-were obtained by hybridization of 13 strains of genetically diverse mice with Cckbr-/-mice.Blood pressure was measured with a noninvasive blood pressure analysis system(BP-2000).The expression of CCKBR protein in mouse kidney tissue was detected by Western Blot,and the pathological changes in mouse kidney tissue were detected by hematoxylin-eosin(HE)staining and immunohistochemistry(IHC).The pathogenic pathways related to essential hypertension were screened by RNA sequencing.Results In three specific mouse strains(A/J,LOT,and FIM),the systolic blood pressure(SBP)was significantly different between the Cckbr-/-and Cckbr+/+groups.HE staining and IHC showed that hypertension caused a certain degree of renal injury in the mice.Gene Ontology(GO)and pathway enrichment analysis showed that differentially expressed genes were enriched in metabolic processes and circadian rhythm regulation.Conclusions Genetically diverse mice can effectively simulate the genetic background of the population and provide a new resource for studying the pathogenic genes related to essential hypertension.

Objective To investigate the radioactivity levels of gross α and gross β in foods around Zhangzhou nuclear power plant, China before operation. Methods Forty-nine samples from 33 kinds of foods in 5 categories of daily food around Zhangzhou nuclear power plant were collected, pretreated, dried, and ashed. The radioactivity levels of gross α and gross β were measured by the low-background α and β measuring instrument. The atomic absorption technique was employed to measure the level of potassium (K), and the radioactivity level of gross β (subtracting ⁴⁰K) was calculated with K concentrations in different foods consulted from the nutritional dietary system. Results The radioactivity levels of gross α in vegetables and fruits, grain, poultry and livestock, aquatic products, and tea around Zhangzhou nuclear power plant were < minimum detectable level (MDL)-7.97, < MDL-6.82, < MDL, < MDL-20.76, and 11.90-23.08 Bq/kg, respectively; the radioactivity levels of gross β were 34.56-122.81, 13.05-188.96, 56.00-108.34, 17.86-169.01, and 123.74-171.63 Bq/kg, respectively; the radioactivity levels of gross β (subtracting ⁴⁰K) were not detected (ND)-14.27, ND-27.86, ND-48.72, ND-45.85, and 6.69-13.79 Bq/kg, respectively. Conclusion The radioactivity of gross α and gross β in foods around Zhangzhou nuclear power plant before operation is basically at the same level as that in other areas of China.

BACKGROUND:With a gradually aging population,improving the ability to screen for the risk of death after arthroplasty and implementing timely personalized intervention programs for the increasing number of elderly patients with femoral neck fractures is key to improving the postoperative status of patients and prolonging survival expectations. OBJECTIVE:To investigate the risk factors for postoperative mortality in elderly patients with femoral neck fractures and to construct a nomogram predictive model to predict their mortality risk. METHODS:The study was conducted on 155 elderly patients(≥65 years old)who underwent arthroplasty for femoral neck fracture from January 2016 to January 2021,and 147 patients who met the inclusion criteria were analyzed to collect clinical data that may affect the patients'postoperative mortality.Single-factor and multi-factor Cox regression analyses were successively used to screen independent risk factors associated with postoperative mortality.The column line graph model was constructed and validated using Rstudio software. RESULTS AND CONCLUSION:(1)Age,frailty(age-adjusted Charlson comorbidities score),preoperative activity status,osteoporosis,and postoperative serum albumin level were five independent risk factors for postoperative mortality in elderly patients with femoral neck fractures(P<0.05).(2)The nomogram predictive model was constructed based on the results of multifactorial analysis,with a consistency index of 0.819(95%CI:0.771-0.868).Receiver operating characteristic curve analysis showed that the area under curve for 1-year and 3-year survival prediction was 0.8543 and 0.7263,respectively,indicating that the nomogram predictive model has good discriminatory and predictive power;calibration curve and decision curve analysis also showed good model discriminative power and clinical utility value.(3)The constructed nomogram predictive model has good diagnostic efficacy and accuracy,and can effectively assess the risk of postoperative death of patients.

OBJECTIVE: A prospective randomized controlled study was conducted to investigate the early postoperative analgesic effectiveness of using liposomal bupivacaine (LB) for local infiltration anesthesia (LIA) in unicompartmental knee arthroplasty (UKA). METHODS: Between January 2024 and July 2024, a total of 80 patients with knee osteoarthritis (KOA) who met the selection criteria were enrolled in the study. Patients were randomly assigned to either the LB group or the "cocktail" group in a 1∶1 ratio using a random number table, with 40 patients in each group. Baseline characteristics, including gender, age, body mass index, operated side, Kellgren-Lawrence grade, and preoperative American Society of Anesthesiologists (ASA) classification, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, and knee joint range of motion, showed no significant difference between the two groups ( P>0.05). Both groups received LIA and comprehensive pain management. The surgical duration, incision length, pain-related indicators [resting and activity visual analogue scale (VAS) scores, total dosage of oral morphine, WOMAC scores], knee joint range of motion, first ambulation time after operation, length of hospital stay, and postoperative adverse events. RESULTS: There was no significant difference between the two groups in surgical duration, incision length, first ambulation time after operation, length of hospital stay, total dosage of oral morphine, and pre-discharge satisfaction with surgery and WOMAC scores ( P>0.05). At 4, 12, and 24 hours after operation, the resting and activity VAS scores in the "cocktail" group were lower than those in the LB group; at 60 and 72 hours postoperatively, the resting VAS scores in the LB group were lower than those in the "cocktail" group, with the activity VAS scores also being lower at 60 hours; all showing significant differences ( P<0.05). There was no significant difference in the above indicators between the two groups at other time points ( P>0.05). On the second postoperative day, the sleep scores of the LB group were significantly higher than those of the "cocktail" group ( P<0.05), while there was no significant difference in sleep scores on the day of surgery and the first postoperative day ( P>0.05). Additionally, the incidence of complications showed no significant difference between the two groups ( P>0.05). CONCLUSION The use of LB for LIA in UKA can provide prolonged postoperative pain relief; however, it does not demonstrate a significant advantage over the "cocktail" method in terms of short-term analgesic effects or reducing opioid consumption and early functional recovery after UKA. Nevertheless, LB may help reduce postoperative sleep disturbances, making it a recommended option for UKA patients with cardiovascular diseases and insomnia or other mental health issues.
Aged ; Female ; Humans ; Male ; Middle Aged ; Anesthesia, Local/methods* ; Anesthetics, Local/administration & dosage* ; Arthroplasty, Replacement, Knee/methods* ; Bupivacaine/administration & dosage* ; Liposomes ; Osteoarthritis, Knee/surgery* ; Pain Measurement ; Pain, Postoperative/prevention & control* ; Prospective Studies ; Treatment Outcome

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers