Additional file 1 of Circular RNA circ_0008365 regulates SOX9 by targeting miR-338-3p to inhibit IL-1β-induced chondrocyte apoptosis and extracellular matrix degradation

Additional file 1: Fig. S1. The circular structure of circ_0008365 was analyzed with divergent primers and convergent primers

Spatial-Potential-Color-Resolved Bipolar Electrode Electrochemiluminescence Biosensor Using a CuMoOx Electrocatalyst for the Simultaneous Detection and Imaging of Tetracycline and Lincomycin

A spatial-potential-color-resolved bipolar electrode electrochemiluminescence biosensor (BPE-ECL) using a CuMoOx electrocatalyst was constructed for the simultaneous detection and imaging of tetracycline (TET) and lincomycin (LIN). HOF-101 emitted peacock blue light under positive potential scanning, and CdSe quantum dots (QDs) emitted green light under negative potential scanning. CuMoOx could catalyze the electrochemical reduction of H2O2 to greatly increase the Faradic current of BPE and realize the ECL signal amplification. In channel 1, CuMoOx-Aptamer II (TET) probes were introduced into the BPE hole (left groove A) by the dual aptamer sandwich method of TET. During positive potential scanning, the polarity of BPE (left groove A) was negative, resulting in the electrochemical reduction of H2O2 catalyzed by CuMoOx, and the ECL signal of HOF-101 was enhanced for detecting TET. In channel 2, CuMoOx-Aptamer (LIN) probes were adsorbed on the MXene of the driving electrode (DVE) hole (left groove B) by hydrogen-bonding and metal-chelating interactions. LIN bound with its aptamers, causing CuMoOx to fall off. During negative potential scanning, the polarity of DVE (left groove B) was negative and the Faradic current decreased. The ECL signal of CdSe QDs was reduced for detecting LIN. Furthermore, a portable mobile phone imaging platform was built for the colorimetric (CL) detection of TET and LIN. Thus, the multiple mode-resolved detection of TET and LIN could be realized simultaneously with only one potential scan, which greatly improved detection accuracy and efficiency. This study opened a new technology of BPE-ECL sensor application and is expected to shine in microchips and point-of-care testing (POCT)

Versatile Photoelectrochemical Biosensing for Hg²⁺ and Aflatoxin B1 Based on Enhanced Photocurrent of AgInS₂ Quantum Dot–DNA Nanowires Sensitizing NPC–ZnO Nanopolyhedra

Eliminating false positives or negatives in analysis has been a challenge. Herein, a phenomenon of polarity-switching photocurrent of AgInS2 quantum dot (QD)–DNA nanowires reversing nitrogen-doped porous carbon–ZnO (NPC–ZnO) nanopolyhedra was found for the first time, and a versatile photoelectrochemical (PEC) biosensor with a reversed signal was innovatively proposed for dual-target detection. NPC–ZnO is a photoactive material with excellent PEC properties, while AgInS2 QDs as a photosensitive material match NPC–ZnO in the energy level, which not only promotes the transfer of photogenerated carriers but also switches the direction of PEC current. Furthermore, in order to prevent spontaneous agglomeration of AgInS2 (AIS) QDs and improve its utilization rate, a new multiple-branched DNA nanowire was specially designed to assemble AgInS2 QDs for constructing amplified signal probes, which not only greatly increased the load of AgInS2 QDs but also further enhanced the photoelectric signal. When the target Hg2+-induced cyclic amplification process generated abundant RDNA, the DNA nanowire signal probe with plenty of QDs was linked to the NPC–ZnO/electrode by RDNA, generating greatly amplified polarity-reversed photocurrent for signal “ON” detection of Hg2+. After specific binding of the target (aflatoxin B1, AFB1) to its aptamer, the signal probes of AIS QD-DNA nanowires were released, realizing signal “OFF” assay of AFB1. Thus, the proposed new PEC biosensor provides a versatile method for detection of dual targets and also effectively avoids both false positive and negative phenomena in the assay process, which has great practical application potential in both environmental and food analysis

DataSheet_1_Comprehensive analysis of mitophagy-related genes in NSCLC diagnosis and immune scenery: based on bulk and single-cell RNA sequencing data.pdf

Lung cancer is the main cause of cancer-related deaths, and non-small cell lung cancer (NSCLC) is the most common type. Understanding the potential mechanisms, prognosis, and treatment aspects of NSCLC is essential. This study systematically analyzed the correlation between mitophagy and NSCLC. Six mitophagy-related feature genes (SRC, UBB, PINK1, FUNDC1, MAP1LC3B, and CSNK2A1) were selected through machine learning and used to construct a diagnostic model for NSCLC. These feature genes are closely associated with the occurrence and development of NSCLC. Additionally, NSCLC was divided into two subtypes using unsupervised consensus clustering, and their differences in clinical characteristics, immune infiltration, and immunotherapy were systematically analyzed. Furthermore, the interaction between mitophagy-related genes (MRGs) and immune cells was analyzed using single-cell sequencing data. The findings of this study will contribute to the development of potential diagnostic biomarkers for NSCLC and the advancement of personalized treatment strategies.</p

Image_3_Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data.tif

BackgroundArtificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.MethodsBetween 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.FindingsA total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.ConclusionThe nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.</p

Image_1_Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data.tif

BackgroundArtificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.MethodsBetween 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.FindingsA total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.ConclusionThe nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.</p

Image_2_Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data.tif

BackgroundArtificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.MethodsBetween 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.FindingsA total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.ConclusionThe nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.</p