Objective To explore the related risk factors of osteosarcopenia (OS) in elderly patients with type 2 diabetes mellitus (T2DM) and to evaluate their predictive value. Methods We selected 409 elderly patients with T2DM from our hospital between June 2021 and December 2024 as the study subjects, and divided them into an OS occurrence group and a non-occurrence group based on whether they were diagnosed with OS. Results Among the 409 elderly patients with T2DM included, 93 were diagnosed with OS, yielding a prevalence rate of 22.73%. Spearman correlation analysis revealed a significant association between lumbar spine BMD and T-scores with age, history of previous fractures, fasting plasma glucose (FPG), procollagen type I N-terminal propeptide (PINP), osteocalcin (OC), and 25-hydroxyvitamin D (25(OH)D). Gender (OR=0.193), Body Mass Index (BMI) (OR=0.254), history of previous fractures (OR=8.883), FPG (OR=0.543), Total Cholesterol (TC) (OR=3.684), High-Density Lipoprotein Cholesterol (HDL-C) (OR=86.024), PINP (OR=0.818), and OC (OR=0.526) are identified as influential factors for the occurrence of OS in elderly patients with T2DM. The combined prediction of these variables yields a sensitivity of 96.5%, a specificity of 97.8%, and an area under the curve (AUC) of 0.992 for the occurrence of OS in elderly patients with T2DM, indicating an excellent predictive performance. Conclusion The following factors—gender, BMI, history of previous fractures, FPG, TC, HDL-C, PINP, and OC—are influential in the occurrence of OS among elderly patients with T2DM. Formulating intervention measures based on these influencing factors can provide assistance in preventing and treating the occurrence of OS.

Methods: A total of 45 patients diagnosed with de novo thoracic or lumbar SEA who underwent posterior-only surgical decompression between 2005 and 2014, with a minimum postoperative follow-up of 2 years, were included. Patients were stratified based on the presence or absence of postoperative residual ND, and neurological function was assessed immediately after surgery and at the final followup using the Frankel grading system. SITE scores, along with clinical and radiological data associated with residual ND, were collected. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to identify significant predictors. Results: Patients with residual ND had significantly lower SITE scores than those without residual ND (4.3±1.3 vs. 7±1.8, p<0.0001). Multivariate analysis identified the SITE score as an independent predictor (odds ratio, 2.70; p=0.012). ROC analysis showed that a SITE score ≤6 predicted residual ND with 73.3% sensitivity and 100% specificity, with an area under the curve of 0.877 (p<0.001). Other significant predictors included cauda equina syndrome and a shorter symptom-to-surgery interval, both of which were associated with a higher risk of residual ND. Conclusions The SITE score is a reliable and independent predictor of residual ND after surgery for SEA. SITE scores <6 indicate a significantly higher risk of postoperative ND.

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.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Objective To examine the potential causal associations between different dietary taste preferences and the risk of prostatitis, so as to provide reference for the primary prevention of this disease. Methods Summary-level genome-wide association study(GWAS)data on five taste preferences-sweet, sour, salty, bitter and spicy—were obtained from the GWAS, and GWAS data for prostatitis were sourced from the FinnGen database. The potential causal associations between the dietary taste preferences and risk of prostatitis were analyzed with MR. The inverse variance weighted(IVW)method served as the primary analytical approach. Heterogeneity was assessed using Cochran's Q test. Horizontal pleiotropy was evaluated via the MR-Egger intercept and Mendelian randomization pleiotropy residual sum and outlier(MR-PRESSO)tests. Leave-one-out analysis was performed to evaluate the influence of individual single nucleotide polymorphisms(SNPs)on the causal estimates. Results Preferences for sweet(OR=1.338, 95%CI:1.024-1.748, P=0.033)and salty(OR=1.356, 95%CI:1.092-1.684, P=0.006)tastes showed positive causal associations with the risk of prostatitis, whereas a preference for bitter taste was negatively associated(OR=0.638, 95%CI:0.416-0.978, P=0.039). No significant causal links were observed for sour or spicy taste preferences(P＞0.05). Sensitivity analyses and reverse MR analyses further supported the robustness of the findings.Conclusion Sweet and salty taste preferences may be potential dietary risk factors for prostatitis, while bitter taste preference could play a protective role. Further research is needed to elucidate these causal relationships, which may contribute to developing tailored dietary intervention strategies for prostatitis prevention.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

OBJECTIVE: To evaluate the safety and effectiveness of robot-assisted percutaneous coronary intervention (R-PCI) compared to traditional manual percutaneous coronary intervention (M-PCI). METHODS: This prospective, multicenter, randomized controlled, non-inferior clinical trial enrolled patients with coronary heart disease who met the inclusion criteria and had indications for elective percutaneous coronary intervention. Participants were randomly assigned to either the R-PCI group or the M-PCI group. Primary endpoints were clinical and technical success rates. Clinical success was defined as visually estimated residual post-percutaneous coronary intervention stenosis < 30% with no 30-day major adverse cardiac events. Technical success in the R-PCI group was defined as successful completion of percutaneous coronary intervention using the ETcath200 robot-assisted system, without conversion to M-PCI in the event of a guidewire or balloon/stent catheter that was unable to cross the vessel or was poorly supported by the catheter. Secondary endpoints included total procedure time, percutaneous coronary intervention procedure time, fluoroscopy time, contrast volume, operator radiation exposure, air kerma, and dose-area product. RESULTS: The trial enrolled 152 patients (R-PCI: 73 patients, M-PCI: 79 patients). Lesions were predominantly B2/C type (73.6%). Both groups achieved 100% clinical success rate. No major adverse cardiac events occurred during the 30-day follow-up. The R-PCI group had a technical success rate of 100%. The R-PCI group had longer total procedure and fluoroscopy times, but lower operator radiation exposure. The percutaneous coronary intervention procedure time, contrast volume, air kerma, and dose-area product were similar between the two groups. CONCLUSIONS For certain complex lesions, performing percutaneous coronary intervention using the ETcath200 robot-assisted system is safe and effective and does not result in conversion to M-PCI.

OBJECTIVE: To examine the effectiveness of Chinese medicine (CM) Lianhua Qingwen Granule (LHQW) and Jingyin Gubiao Prescription (JYGB) in asymptomatic or mild patients with Omicron infection in the shelter hospital. METHODS: This single-center retrospective cohort study was conducted in the largest shelter hospital in Shanghai, China, from April 10, 2022 to May 30, 2022. A total of 56,244 asymptomatic and mild Omicron cases were included and divided into 4 groups, i.e., non-administration group (23,702 cases), LHQW group (11,576 cases), JYGB group (12,112 cases), and dual combination of LHQW and JYGB group (8,854 cases). The length of stay (LOS) in the hospital was used to assess the effectiveness of LHQW and JYGB treatment on Omicron infection. RESULTS: Patients aged 41-60 years, with nadir threshold cycle (C_T) value of N gene <25, or those fully vaccinated preferred to receive CM therapy. Before or after propensity score matching (PSM), the multiple linear regression showed that LHQW and JYGB treatment were independent influence factors of LOS (both P<0.001). After PSM, there were significant differences in LOS between the LHQW/JYGB combination and the other groups (P<0.01). The results of factorial design ANOVA proved that the LHQW/JYGB combination therapy synergistically shortened LOS (P=0.032). CONCLUSIONS Patients with a nadir C_T value <25 were more likely to accept CM. The LHQW/JYGB combination therapy could shorten the LOS of Omicron-infected individuals in an isolated environment.
Humans ; Drugs, Chinese Herbal/therapeutic use* ; Male ; Female ; Middle Aged ; Adult ; China/epidemiology* ; Hospitalization ; COVID-19 Drug Treatment ; COVID-19/epidemiology* ; SARS-CoV-2 ; Retrospective Studies ; Treatment Outcome ; Length of Stay ; Young Adult ; Aged