In situ study on articular cartilage degeneration in simulated microgravity by HOF-ATR-FTIR spectroscopy.

Fourier transform infrared spectroscopy (FTIRS) can provide rich information on the composition and content of samples, enabling the detection of subtle changes in tissue composition and structure. This study represents the first application of FTIRS to investigate cartilage under microgravity. Simulated microgravity cartilage model was firstly established by tail-suspension (TS) for 7, 14 and 21 days, which would be compared to control samples. A self-developed hollow optical fiber attenuated total reflection (HOF-ATR) probe coupled with a FTIR spectrometer was used for the spectral acquisition of cartilage samples in situ, and one-way analysis of variance (ANOVA) was employed to analyze the changes in the contents of cartilage matrix at different stages. The results indicate that cartilage degenerates in microgravity, the collagen content gradually decreases with the TS time, and the structure of collagen fibers changes. The trends of proteoglycan content and collagen integrity show an initial decrease followed by an increase, ultimately significantly decreasing. The findings provide the basis for the cartilage degeneration in microgravity with TS time, which must be of real significance for space science and health detection.

Evaluation of the Bioavailability of Iodine and Arsenic in Raw and Cooked Saccharina japonica Based on Simulated Digestion/Caco-2 Cell Model.

Kelp is a traditional healthy food due to its high nutritional content; however, its relatively high contents of iodine and arsenic have raised concerns about its edible safety. This study explored the effects of different cooking treatments on the contents of iodine and arsenic in kelp, evaluated the bioaccessibility and bioavailability of iodine and arsenic in kelp using in vitro digestion, and compared the differences in the transport characteristics of iodine in kelp and KIO3 using a Caco-2 monolayer cell transport model. The results show that the content of target elements that reached systemic circulation could be reduced by cooking and gastrointestinal digestion. The highest reductions in iodine and arsenic were 94.4% and 74.7%, respectively, which were achieved by boiling for 10 min. The bioaccessibility and bioavailability of iodine and arsenic were significantly improved by a cooking treatment. However, the contents of iodine and arsenic decreased significantly, with the bioaccessibility of iodine reducing from 3188.2 µg/L to 317.0 µg/L and that of arsenic reducing from 32.5 µg/L to 18.1 µg/L in the gastric phase after boiling. The findings also show that the efficiency of iodine transport in kelp and KIO3 was positively correlated with the transport time and negatively correlated with the concentration of iodine. With the increase in the iodine concentration, the rate of iodine transport in kelp decreased from 63.93% to 3.14%, but that of KIO3 was stable at around 35%, which indicates that the absorption efficiency of iodine from kelp was limited, even when too much kelp was ingested. In conclusion, the edible safety of kelp is significantly improved after cooking. The risk of excessive iodine and arsenic intake caused by consuming kelp is extremely low, and as an effective iodine supplement source, kelp has higher edible safety compared with KIO3. This study clarifies the safety of algae based on iodine and arsenic contents and also provides a basis for the formulation of food safety standards.

Significant association between methyl mercury level and latent tuberculosis infection risk: a cross-sectional study.

OBJECTIVES: This cross-sectional study aimed to explore the association between methyl mercury (MeHg) level and latent tuberculosis infection (LTBI) risk based on the data from National Health and Nutrition Examination Survey (NHANES 2011-2012). METHODS: A total of 5243 participants with 20 variables were enrolled. The importance of these variables on TB infection was first ranked by XGBoost and Random Forest methods. Then the association between MeHg level and infection risk was evaluated by restricted cubic spline, threshold effect, and generalized linear regression analyses. We also explored the factors correlated with the difference in MeHg level and finally conducted a mediation analysis to assess the mediating effect of MeHg in LTBI. RESULTS: 521 participants were experiencing the LTBI, and 12 variables showed the differences between infection and non-infection groups (all P < 0.05). Of them, MeHg presented the highest importance on the LTBI. Restricted cubic spline (RCS) next revealed a significant non-linear correlation of MeHg with LTBI (all P < 0.05). Adjusted regression models further indicated their independent association (all P < 0.05), and infection risk increased with the increase of MeHg (P for trend < 0.05). We also found a significant turning point, and their association was significantly observed when MeHg > 5.75 µg/L (P < 0.05). In addition, asthma history was related to the difference in MeHg levels between LTBI and non-LTBI groups. Mediation analysis found that MeHg level partially mediated the association of asthma and LTBI risk (all P < 0.05). CONCLUSIONS: Our study identified MeHg as an independent risk factor for LTBI risk. Their causal relationship needs more investigation to verify.

Plasma extracellular vesicle long RNA profiling identifies a predictive signature for immunochemotherapy efficacy in lung squamous cell carcinoma.

Introduction: The introduction of Immune Checkpoint Inhibitors (ICIs) has marked a paradigm shift in treating Lung Squamous Cell Carcinoma (LUSC), emphasizing the urgent need for precise molecular biomarkers to reliably forecast therapeutic efficacy. This study aims to identify potential biomarkers for immunochemotherapy efficacy by focusing on plasma extracellular vesicle (EV)-derived long RNAs (exLRs). Methods: We enrolled 78 advanced LUSC patients undergoing first-line immunochemotherapy. Plasma samples were collected, and exLR sequencing was conducted to establish baseline profiles. A retrospective analysis was performed on 42 patients to identify differentially expressed exLRs. Further validation of the top differentially expressed exLRs was conducted using quantitative reverse transcription PCR (qRT-PCR). Univariate Cox analysis was applied to determine the prognostic significance of these exLRs. Based on these findings, we developed a predictive signature (p-Signature). Results: In the retrospective analysis of 42 patients, we identified 460 differentially expressed exLRs, with pathways related to leukocyte migration notably enriched among non-responders. Univariate Cox analysis revealed 45 exLRs with prognostic significance. The top 6 protein-coding exLRs were validated using qRT-PCR, identifying CXCL8, SSH3, and SDHAF1 as differentially expressed between responders and non-responders. The p-Signature, comprising these three exLRs, demonstrated high accuracy in distinguishing responders from non-responders, with an Area Under the Curve (AUC) of 0.904 in the retrospective cohort and 0.812 in the prospective cohort. Discussion: This study highlighted the potential of plasma exLR profiles in predicting LUSC treatment efficacy. Intriguingly, lower p-Signature scores were associated with increased abundance of activated CD4+ and CD8+ T cells, indicating a more robust immune environment. These findings suggest that the p-Signature could serve as a valuable tool in guiding personalized and effective therapeutic strategies for LUSC.

AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease.

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

Multidisciplinary team discussion based on etiological treatment improves refractory chronic cough outcomes.

BACKGROUND: Refractory chronic cough (RCC) causes significant impairments in the life quality of patients. Further research into the identification of etiologies and development of the treatment schedules for RCC is needed. PATIENTS AND METHODS: We established an multidisciplinary team (MDT) clinic, by integrating respiratory medicine, otorhinolaryngology, and gastroenterology departments, to investigate cough etiologies and the effectiveness of treatment. The therapeutic effect was assessed quantitatively using the Cough Visual Analog Scales (VAS), Leicester Cough Questionnaire (LCQ), and Reflux Symptoms Index (RSI) scores. RESULTS: In total, 213 patients attending the MDT outpatient clinic were examined, and 115 patients with RCC were included for analysis. The RCC diagnosis rate among the outpatient was 88.7%. Common causes of RCC included gastroesophageal reflux cough (63.5%), upper airway cough syndrome (UACS) (43.5%), and cough variant asthma (CVA) (14.8%). After an average treatment period of 2.17 ± 1.06 weeks (wk), 73.9% of the patients had partial cough remission, and 6.1% had complete cough remission. The cough VAS score before and after treatment was 6.11 ± 2.02 vs. 3.66 ± 2.22 (P < 0.05), respectively; LCQ total score before and after treatment was 10.24 ± 3.11 vs. 13.16 ± 3.59 (P < 0.05), respectively; and RSI score before and after treatment was 15.82 ± 7.01 vs. 10.71 ± 6.64 (P < 0.05), respectively. CONCLUSION: The etiologies of most patients with RCC could be identified in the MDT clinic, and the cough-related symptoms of a significant number of patients with RCC improved in a short period.

Enhanced activation of peroxymonosulfate by ZIF-67/g-C3N4 S-scheme photocatalyst under visible light assistance for degradation of polyethylene terephthalate.

Photocatalyst-activated peroxymonosulfate (PMS) degradation of pollutants is already widely used for wastewater treatment under visible light. Polyethylene terephthalate (PET) is widely used in daily life, but waste plastics have an irreversible negative impact on the environment. In this paper, the ZIF-67/g-C3N4 S-scheme heterojunction catalyst was synthesized as a photocatalyst to achieve a good effect on PET degradation in coordination with PMS. The results indicated that PET could be degraded up to 60.63 ± 2.12 % under the combined effect of catalyst, PMS, and light. In this experiment, the influence of catalyst-to-plastic ratio, PMS concentration, aqueous pH, and inorganic anions on plastic degradation by the photocatalytic synergistic PMS system was discussed, and the excellent performance of this system for degrading PET was highlighted through a comparative test. Electron spin resonance (ESR) and free radical quenching experiments demonstrated that SO4•- contributes the largest amount to the PET degradation performance. Furthermore, results from gas chromatography and liquid chromatography-mass spectrometry (LC-MS) indicated that the plastic degradation products include CO, CH4, and organic small-molecule liquid fuels. Finally, a possible mechanism for the light/PMS system to degrade PET in water was suggested. This paper provides a feasible solution to treat waste microplastics in water.

The BNB-GLID module regulates germline fate determination in Marchantia polymorpha.

Germline fate determination is a critical event in sexual reproduction. Unlike animals, plants specify the germline by reprogramming somatic cells at the late stages of their development. However, the genetic basis of germline fate determination and how it evolved during the land plant evolution are still poorly understood. Here, we report that the plant homeodomain finger protein GERMLINE IDENTITY DETERMINANT (GLID) is a key regulator of the germline specification in liverwort, Marchantia polymorpha. Loss of the MpGLID function causes failure of germline initiation, leading to the absence of sperm and egg cells. Remarkably, the overexpression of MpGLID in M. polymorpha induces the ectopic formation of cells with male germline cell features exclusively in male thalli. We further show that MpBONOBO (BNB), with an evolutionarily conserved function, can induce the formation of male germ cell-like cells through the activation of MpGLID by directly binding to its promoter. The Arabidopsis (Arabidopsis thaliana) MpGLID ortholog, MALE STERILITY1 (AtMS1), fails to replace the germline specification function of MpGLID in M. polymorpha, demonstrating that a derived function of MpGLID orthologs has been restricted to tapetum development in flowering plants. Collectively, our findings suggest the presence of the BNB-GLID module in complex ancestral land plants that has been retained in bryophytes, but rewired in flowering plants for male germline fate determination.

Spatial-resolved and self-calibrated 3D-printed photoelectrochemical biosensor engineered by multifunctional CeO2/CdS heterostructure for immunoassay.

A spatial-resolved and self-calibrated photoelectrochemical (PEC) biosensor has been fabricated by a multifunctional CeO2/CdS heterostructure, achieving portable and sensitive detection of carcinoembryonic antigen (CEA) using a homemade 3D printing device. The CeO2/CdS heterostructure with matched band structure is prepared to construct the dual-photoelectrodes to improve the PEC response of CeO2. In particular, as the photoactive nanomaterial, the CeO2 also plays the role of peroxidase mimetic nanozymes. Therefore, the catalytic performance of CeO2 with different morphologies (e.g., nano-cubes, nano-rods and nano-octahedra) have been studied, and CeO2 nano-cubes (c-CeO2) achieve the optimal catalytic activity. Upon introducing CEA, the sandwich-type immunocomplex is formed in the microplate using GOx-AuNPs-labeled second antibody as detection antibody. As a result, H2O2 can be produced from the catalytic oxidization of glucose substrate by GOx, which is further catalyzed by CeO2 to form •OH, thus in situ etching CdS and decreasing the photocurrents. The self-calibration is achieved by the dual-channel photoelectrodes on the homemade 3D printing device to obtain the photocurrents ratio, thus effectively normalizing the fluctuations of external factors to enhance the accuracy. This integrated biosensor with a detection limit as low as 0.057 ng mL-1 provides a promising way for ultrasensitive immunoassay in clinic application in complex environments.

Glyco-signatures in patients with advanced lung cancer during anti-PD-1/PD-L1 immunotherapy.

Immune checkpoint inhibitors (ICIs) targeting programmed cell death 1/programmed cell death ligand-1 (PD-1/PD-L1) have significantly prolonged the survival of advanced/metastatic patients with lung cancer. However, only a small proportion of patients can benefit from ICIs, and clinical management of the treatment process remains challenging. Glycosylation has added a new dimension to advance our understanding of tumor immunity and immunotherapy. To systematically characterize anti-PD-1/PD-L1 immunotherapy-related changes in serum glycoproteins, a series of serum samples from 12 patients with metastatic lung squamous cell carcinoma (SCC) and lung adenocarcinoma (ADC), collected before and during ICIs treatment, are firstly analyzed with mass-spectrometry-based label-free quantification method. Second, a stratification analysis is performed among anti-PD-1/PD-L1 responders and non-responders, with serum levels of glycopeptides correlated with treatment response. In addition, in an independent validation cohort, a large-scale site-specific profiling strategy based on chemical labeling is employed to confirm the unusual characteristics of IgG N-glycosylation associated with anti-PD-1/PD-L1 treatment. Unbiased label-free quantitative glycoproteomics reveals serum levels' alterations related to anti-PD-1/PD-L1 treatment in 27 out of 337 quantified glycopeptides. The intact glycopeptide EEQFN 177STYR (H3N4) corresponding to IgG4 is significantly increased during anti-PD-1/PD-L1 treatment (FC=2.65, P=0.0083) and has the highest increase in anti-PD-1/PD-L1 responders (FC=5.84, P=0.0190). Quantitative glycoproteomics based on protein purification and chemical labeling confirms this observation. Furthermore, obvious associations between the two intact glycopeptides (EEQFN 177STYR (H3N4) of IgG4, EEQYN 227STFR (H3N4F1) of IgG3) and response to treatment are observed, which may play a guiding role in cancer immunotherapy. Our findings could benefit future clinical disease management.

Study on breast cancerization and isolated diagnosis in situ by HOF-ATR-MIR spectroscopy with deep learning.

Mid-infrared (MIR) spectroscopy can characterize the content and structural changes of macromolecular components in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classification and recognition of different breast tissues. In parallel, the one-dimensional convolutional neural network (1D-CNN) stands out in the field of deep learning for its ability to efficiently process sequential data, such as spectroscopic signals. In this study, MIR spectra of breast tissue were collected in situ by coupling the self-developed MIR hollow optical fiber attenuated total reflection (HOF-ATR) probe with a Fourier transform infrared spectroscopy (FTIR) spectrometer. Staging analysis was conducted on the changes in macromolecular content and structure in breast cancer tissues. For the first time, a trinary classification model was established based on 1D-CNN for recognizing normal, paracancerous and cancerous tissues. The final predication results reveal that the 1D-CNN model based on baseline correction (BC) and data augmentation yields more precise classification results, with a total accuracy of 95.09%, exhibiting superior discrimination ability than machine learning models of SVM-DA (90.00%), SVR (88.89%), PCA-FDA (67.78%) and PCA-KNN (70.00%). The experimental results suggest that the application of 1D-CNN enables accurate classification and recognition of different breast tissues, which can be considered as a precise, efficient and intelligent novel method for breast cancer diagnosis.

Blood cadmium level as a risk factor for chronic pain: NHANES database 1999-2004.

Objective: The escalating prevalence of chronic pain poses a substantial socio-economic burden. Chronic pain primarily stems from musculoskeletal and nervous system impairments. Given cadmium's known toxicity to these systems, our study sought to investigate the correlation between blood cadmium levels and chronic pain. Methods: The cross-sectional study was conducted from the National Health and Nutrition Examination Survey (NHANES, 1999-2004), and comprised US adults who participated in a chronic pain interview. We employed logistic regression models and smooth curve fitting to elucidate the relationship between blood cadmium levels and chronic pain. Results: Our findings revealed a linear association between blood cadmium levels and chronic pain. Compared to the lower blood cadmium tertile 1 (<0.3 ug/dL), the adjusted odds ratios (ORs) for tertile 2 (0.3-0.4 ug/dL), and tertile 3 (≥0.5 ug/dL), were 1.11 (0.96-1.29) and 1.2 (1.03-1.39), respectively. Sensitivity analyses corroborated these results. Conclusion: Elevated levels of blood cadmium are associated with a heightened risk of chronic pain among adults in the United States. Mitigating cadmium exposure could potentially decrease the risk of chronic pain, thereby enhancing strategies for chronic pain prevention and management.

[Advances in the application of AlphaFold2: a protein structure prediction model].

Protein structure prediction is an important research field in life sciences and medicine, and it is also a key application scenario of artificial intelligence in scientific research. AlphaFold2 is a protein structure prediction system developed by DeepMind based on deep learning, capable of efficiently generating the atomic-scale spatial structure of a protein from the amino acid sequence. It has demonstrated superior performance in the prediction of protein structures since its inception, thus attracting much attention and research. This paper introduces the model architecture, highlights, limitations, and application progress of AlphaFold2. Furthermore, it briefs the capabilities, highlights, and limitations of several other types of protein structure prediction models and prospects the future development direction in this field.

A bimetallic peroxidase-mimicking nanozyme with antifouling property for construction of sensor array to identify phosphoproteins and diagnose cancers.

Protein phosphorylation is a significant post-translational modification that plays a decisive role in the occurrence and development of diseases. However, the rapid and accurate identification of phosphoproteins remains challenging. Herein, a high-throughput sensor array has been constructed based on a magnetic bimetallic nanozyme (Fe3O4@ZNP@UiO-66) for the identification and discrimination of phosphoproteins. Attributing to the formation of Fe-Zr bimetallic dual active centers, the as-prepared Fe3O4@ZNP@UiO-66 exhibits enhanced peroxidase-mimicking catalytic activity, which promotes the electron transfer from Zr center to Fe(II)/Fe(III). The catalytic activity of Fe3O4@ZNP@UiO-66 can be selectively inhibited by phosphoproteins due to the strong interaction between phosphate groups and Zr centers, as well as the ultra-robust antifouling capability of zwitterionic dopamine nanoparticle (ZNP). Considering the diverse binding affinities between various proteins with the nanozyme, the catalytic activity of Fe3O4@ZNP@UiO-66 can be changed to various degree, leading to the different absorption responses at 420 nm in the hydrogen peroxide (H2O2) - 2, 2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) system. By simply extracting different absorbance intensities at various time points, a sensor array based on reaction kinetics for the discrimination of phosphoproteins from other proteins is constructed through linear discriminant analysis (LDA). Besides, the quantitative determination of phosphoproteins and identification of protein mixtures have been realized. Further, based on the differential level of phosphoproteins in cells, the differentiation of cancer cells from normal cells can also be implemented by utilizing the proposed sensor array, showing great potential in disease diagnosis.

Dual function of activated PPARγ by ligands on tumor growth and immunotherapy.

As one of the peroxisome-proliferator-activated receptors (PPARs) members, PPARγ is a ligand binding and activated nuclear hormone receptor, which is an important regulator in metabolism, proliferation, tumor progression, and immune response. Increased evidence suggests that activation of PPARγ in response to ligands inhibits multiple types of cancer proliferation, metastasis, and tumor growth and induces cell apoptosis including breast cancer, colon cancer, lung cancer, and bladder cancer. Conversely, some reports suggest that activation of PPARγ is associated with tumor growth. In addition to regulating tumor progression, PPARγ could promote or inhibit tumor immunotherapy by affecting macrophage differentiation or T cell activity. These controversial findings may be derived from cancer cell types, conditions, and ligands, since some ligands are independent of PPARγ activity. Therefore, this review discussed the dual role of PPARγ on tumor progression and immunotherapy.

Synovial fluid research based on SERS and SERRS for enhanced detection of biomarkers in staged osteoarthritis.

Surface-enhanced (resonance) Raman scattering (SER(R)S) can extremely enhance Raman intensity of samples, which is helpful for detecting synovial fluid (SF) that does not show Raman activity under normal conditions. In this study, SER(R)S spectra of SF from three different osteoarthritis (OA) stages were collected and analyzed for OA progress, finding that the content of collagen increased throughout the disease, while non-collagen proteins and polysaccharides decreased sharply at advanced OA stage accompanied by the increase of phospholipid. The spectral features and differences were enhanced by salting-out and centrifugation. Much more information on biomolecules at different OA stages was disclosed by using SERRS for the first time, these main trace components (ß-carotene, collagen, hyaluronic acid, nucleotide, and phospholipid) can be used as potential biomarkers. It indicates that SERRS has a more comprehensive ability to assist SERS in seeking micro(trace) biomolecules as biomarkers and facilitating accurate and efficient diagnosis and mechanism research of OA.

GSK0660 enhances antitumor immunotherapy by reducing PD-L1 expression.

Blockade of PD-1/PD-L1 immune checkpoint is wildly used for multiple types of cancer treatment, while the low response rate for patients is still completely unknown. As nuclear hormone receptor, PPARδ (peroxisome-proliferator-activated receptor) regulates cell proliferation, inflammation, and tumor progression, while the effect of PPARδ on tumor immune escape is still unclear. Here we found that PPARδ antagonist GSK0660 significantly reduced colon cancer cell PD-L1 protein and gene expression. Luciferase analysis showed that GSK0660 decreased PD-L1 gene transcription activity. Moreover, reduced PD-L1 expression in colon cancer cells led to increased T cell activity. Further analysis showed that GSK0660 decreased PD-L1 expression in a PPARδ dependent manner. Implanted tumor model analysis showed that GSK0660 inhibited tumor immune escape and the combined PD-1 antibody with GSK0660 effectively enhanced colorectal cancer immunotherapy. These findings suggest that GSK0660 treatment could be an effective strategy for cancer immunotherapy.

PD-L1 mRNA derived from tumor-educated platelets as a potential immunotherapy biomarker in non-small cell lung cancer.

Background: To date, the role of programmed death ligand-1 (PD-L1) messenger RNA (mRNA) derived from tumor-educated platelets (TEPs) has not been well investigated in patients with advanced non-small cell lung cancer (NSCLC). A few reports have examined whether mRNA in TEPs can predict the clinical responses of patients with advanced NSCLC following immunotherapy. This study aimed to identify novel biomarkers to improve the clinical benefits and outcomes of NSCLC patients. Methods: Advanced NSCLC patients receiving a combination of immunotherapy and chemotherapy, or immunotherapy alone as a first- or second-line treatment at the Fudan University Shanghai Cancer Center were enrolled in this study. All the patients had wild-type epidermal growth factor receptor/anaplastic lymphoma kinase. The patients were enrolled in clinical trials for immune checkpoint inhibitors (ICIs), including nivolumab, pembrolizumab, atezolizumab, durvalumab, tremelimumab, and camrelizumab. Tumoral PD-L1 expression was tested by immunohistochemistry (PD-L1 22C3 pharmDx kit, Agilent, Santa Clara, CA, USA) in archived tissue samples, when available, to calculate the tumor proportion scores (TPSs). RNA and exosomal RNA of blood were isolated before immunotherapy using the Yunying RNA extraction kit (Yunying Medicine, Shanghai, China). The concentration and quality of the RNA was determined using a Qubit fluorometer (Life Technologies, Carlsbad, CA, USA). Finally, we analyzed the predictive value of TEP-derived PD-L1 mRNA expression and association with the level of the tumoral PD-L1 expression. Results: In total, 72 patients were enrolled in this study. Most of the patients were male (n=54, 75.0%), had adenocarcinoma (n=49, 68.1%). We found there was no significant correlation between the TEP-derived mRNA of PD-L1 and tumoral PD-L1 expression based on the results of the Pearson Correlation test (r=-0.19, P=0.233). Based on the median of PD-L1 mRNA, 72 patients were divided into a high PD-L1 group and a low PD-L1 group. We found that 19 patients (44.4%) responded to immunotherapy [partial response or progression-free survival (PFS) >6 months] in the high PD-L1 group, but only five patients (13.9%) responded to immunotherapy in the low PD-L1 group (P<0.01). The median PFS of the low PD-L1 group was lower than that of the high PD-L1 group (2.8 vs. 8.3 months, P<0.001). For the patients who were treated with immunotherapy alone (n=64), a similar PFS advantage was observed in the high PD-L1 group (2.8 vs. 8.0 months, P=0.002). Conclusions: This article presented the first data on TEP-derived PD-L1 mRNA in advanced NSCLC patients following immunotherapy and showed the potential advantage of using it as the surrogate biomarker for predicting the PFS and overall survival of patients following immunotherapy.

Association between HDL-C and chronic pain: data from the NHANES database 2003-2004.

Objective: High-density lipoprotein cholesterol (HDL-C) has been reported to be associated with pain symptoms of various diseases, and its anti-inflammatory and antioxidant mediation is related to the pathogenesis of chronic pain. This study aims to evaluate the relationship between HDL-C levels and chronic pain in American adults. Methods: This cross-sectional study used data from American adults aged 20 and above during the 2003-2004 National Health and Nutrition Examination Survey (NHANES) cycle. Participants were divided into 4 groups based on HDL-C quartiles. We used chi-square tests and Student's t-tests or Mann-Whitney U tests to analyze categorical variables and continuous variables to compare differences between groups. Multivariate logistic regression analysis was used to study the association between HDL-C levels and the risk of chronic pain. Likelihood ratio tests were used to assess interactions between subgroups, and sensitivity analyses were conducted. Results: Our final analysis included 4,688 participants, of which 733 (16.4%) had chronic pain. In the multivariate logistic regression model adjusted for covariates, there was a negative correlation between HDL-C levels and chronic pain. Specifically, for every 20 unit increase in HDL-C, the risk of chronic pain decreased by 26%. Compared with the lowest HDL-C quartile (< 43 mg/dL), the highest HDL-C quartile (≥ 64 mg/dL) was associated with a 24% reduction in the risk of chronic pain. No interaction factors affecting the relationship between HDL-C and chronic pain were found in the subgroup analysis. Conclusion: This study demonstrates a negative association between HDL-C levels and chronic pain in US adults, providing insights into the pathogenesis of chronic pain and potential improvements in chronic pain management strategies.

Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.

Objective: The recent World Endoscopy Organization (WEO) guidelines now recognize precursor lesions of colorectal cancer (CRC) as legitimate screening targets. However, an optimal screening method for detecting advanced adenoma (AA), a significant precursor lesion, remains elusive. Methods: We employed five machine learning methods, using clinical and laboratory data, to develop and validate a diagnostic model for identifying patients with AA (569 AAs vs. 3228 controls with normal colonoscopy). The best-performing model was selected based on sensitivity and specificity assessments. Its performance in recognizing adenoma-carcinoma sequence was evaluated in line with guidelines, and adjustable thresholds were established. For comparison, the Fecal Occult Blood Test (FOBT) was also selected. Results: The XGBoost model demonstrated superior performance in identifying AA, with a sensitivity of 70.8% and a specificity of 83.4%. It successfully detected 42.7% of non-advanced adenoma (NAA) and 80.1% of CRC. The model-transformed risk assessment scale provided diagnostic performance at different positivity thresholds. Compared to FOBT, the XGBoost model better identified AA and NAA, however, was less effective in CRC. Conclusion: The XGBoost model, compared to FOBT, offers improved accuracy in identifying AA patients. While it may not meet the recommendations of some organizations, it provides value for individuals who are unable to use FOBT for various reasons.