Differential reinforcement of cGAS-STING pathway-involved immunotherapy by biomineralized bacterial outer membrane-sensitized EBRT and RNT.

Radiotherapy (RT), including external beam radiation therapy (EBRT) and radionuclide therapy (RNT), realizes physical killing of local tumors and activates systemic anti-tumor immunity. However, these effects need to be further strengthened and the difference between EBRT and RNT should be discovered. Herein, bacterial outer membrane (OM) was biomineralized with manganese oxide (MnO2) to obtain OM@MnO2-PEG nanoparticles for enhanced radio-immunotherapy via amplifying EBRT/RNT-induced immunogenic cell death (ICD) and cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) activation. OM@MnO2-PEG can react with H2O2 and then gradually produce O2, Mn2+ and OM fragments in the tumor microenvironment. The relieved tumor hypoxia improves the radio-sensitivity of tumor cells, resulting in enhanced ICD and DNA damage. Mn2+ together with the DNA fragments in the cytoplasm activate the cGAS-STING pathway, further exhibiting a positive role in various aspects of innate immunity and adaptive immunity. Besides, OM fragments promote tumor antigen presentation and anti-tumor macrophages polarization. More importantly, our study reveals that OM@MnO2-PEG-mediated RNT triggers much stronger cGAS-STING pathway-involved immunotherapy than that of EBRT, owing to the duration difference of RT. Therefore, this study develops a powerful sensitizer of radio-immunotherapy and uncovers some differences between EBRT and RNT in the activation of cGAS-STING pathway-related anti-tumor immunity.

Gossypol acetic acid regulates leukemia stem cells by degrading LRPPRC via inhibiting IL-6/JAK1/STAT3 signaling or resulting mitochondrial dysfunction.

BACKGROUND: Leukemia stem cells (LSCs) are found to be one of the main factors contributing to poor therapeutic effects in acute myeloid leukemia (AML), as they are protected by the bone marrow microenvironment (BMM) against conventional therapies. Gossypol acetic acid (GAA), which is extracted from the seeds of cotton plants, exerts anti-tumor roles in several types of cancer and has been reported to induce apoptosis of LSCs by inhibiting Bcl2. AIM: To investigate the exact roles of GAA in regulating LSCs under different microenvironments and the exact mechanism. METHODS: In this study, LSCs were magnetically sorted from AML cell lines and the CD34+CD38- population was obtained. The expression of leucine-rich pentatricopeptide repeat-containing protein (LRPPRC) and forkhead box M1 (FOXM1) was evaluated in LSCs, and the effects of GAA on malignancies and mitochondrial function were measured. RESULTS: LRPPRC was found to be upregulated, and GAA inhibited cell proliferation by degrading LRPPRC. GAA induced LRPPRC degradation and inhibited the activation of interleukin 6 (IL-6)/janus kinase (JAK) 1/signal transducer and activator of transcription (STAT) 3 signaling, enhancing chemosensitivity in LSCs against conventional chemotherapies, including L-Asparaginase, Dexamethasone, and cytarabine. GAA was also found to downregulate FOXM1 indirectly by regulating LRPPRC. Furthermore, GAA induced reactive oxygen species accumulation, disturbed mitochondrial homeostasis, and caused mitochondrial dysfunction. By inhibiting IL-6/JAK1/STAT3 signaling via degrading LRPPRC, GAA resulted in the elimination of LSCs. Meanwhile, GAA induced oxidative stress and subsequent cell damage by causing mitochondrial damage. CONCLUSION: Taken together, the results indicate that GAA might overcome the BMM protective effect and be considered as a novel and effective combination therapy for AML.

Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study.

OBJECTIVE: To develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL). METHODS: The AI system was developed, trained and validated on 5362 images of 551 patients from three hospitals. Automated regions of interest (ROI) segmentation algorithm was utilized to construct image-level features. MIL was used to fusion image level results to patient level features, then the extracted features were modeled by seven machine learning algorithms. Finally, we evaluated the image level and patient level results. Additionally, 50 videos of VFL were prospectively gathered to assess the system's real-time diagnostic capabilities. A human-machine comparison database was also constructed to compare the diagnostic performance of otolaryngologists with and without AI assistance. RESULTS: In internal and external validation sets, the maximum area under the curve (AUC) for image level segmentation models was 0.775 (95 % CI 0.740-0.811) and 0.720 (95 % CI 0.684-0.756), respectively. Utilizing a MIL-based fusion strategy, the AUC at the patient level increased to 0.869 (95 % CI 0.798-0.940) and 0.851 (95 % CI 0.756-0.945). For real-time video diagnosis, the maximum AUC at the patient level reached 0.850 (95 % CI, 0.743-0.957). With AI assistance, the AUC improved from 0.720 (95 % CI 0.682-0.755) to 0.808 (95 % CI 0.775-0.839) for senior otolaryngologists and from 0.647 (95 % CI 0.608-0.686) to 0.807 (95 % CI 0.773-0.837) for junior otolaryngologists. CONCLUSIONS: The MIL based AI-assisted diagnosis system can significantly improve the diagnostic performance of otolaryngologists for VFL and help to make proper clinical decisions.

Cell-cycle related centromere protein F deficiency suppresses ovarian cancer cells growth by inducing ferroptosis.

OBJECTIVES: This study aimed to investigate the involvement of the cell cycle-related protein centriole protein F (CENPF) in the development of ovarian cancer (OC) and explored its relationship with ferroptosis. DESIGN: The databases were analyzed to identify differential expression of cell cycle-related proteins between individuals with ovarian cancer and normal individuals. Immunohistochemistry and statistical analysis were conducted on ovarian tissues obtained from 40 patients with epithelial ovarian cancer and 20 normal individuals. In vitro experiments were performed using SKOV3 and HEY epithelial ovarian cancer cell lines. PARTICIPANTS/MATERIALS, SETTING, METHODS: The mRNA microarray data set, consisting of GSE14001, GSE54388, GSE40595, and GSE14407, was downloaded from the Gene Expression Omnibus (GEO) database to investigate the genes associated with cell cycle regulation in ovarian cancer cells. CENPF was selected as the subject of study through differential analysis. Assessed the expression of CENPF in both ovarian cancer (OC) patients and normal ovarian tissues using immunohistochemistry. Lentivirus infection was employed to downregulate CENPF expression, and subsequent experiments including Cell Counting Kit-8 (CCK-8) assay, cell cycle analysis, transwell assay, and wound-healing assay were conducted to investigate the effects of CENPF on proliferation, invasion, migration, and cell cycle regulation in OC cells. The Reactive oxygen species (ROS) and the malondialdehyde (MDA) assays were performed to assess the involvement of CENPF in cellular redox reactions. Western blot analysis was conducted to examine the expression levels of ferroptosis-related proteins (GPX4, SLC7A11, DMT1, and P53). RESULTS: By querying and integrating cell cycle-related genes from the GEO database, in silico analyses using The Cancer Genome Atlas (TCGA) database combined with immunohistochemical studies, we discovered that CENPF is upregulated in ovarian cancer tissues and is related to survival. Downregulation of CENPF inhibited biological function of OC cells, increased intracellular ROS and MDA levels, and downregulated the GPX4 protein and the SLC7A11/xCT protein, but upregulated the DMT1 protein and the tumor protein 53(P53) protein expression to induce ferroptosis. LIMITATIONS: This study did not investigate ferroptosis-related studies following CENPF overexpression, and the findings have not been validated in animal studies. CONCLUSIONS: Our findings demonstrated that the deficiency of CENPF played a crucial anti-oncogenic role in the progression of OC through the mechanism of ferroptosis.

Advances in therapeutic cancer vaccines: Harnessing immune adjuvants for enhanced efficacy and future perspectives.

Preventive cancer vaccines are highly effective in preventing viral infection-induced cancer, but advances in therapeutic cancer vaccines with a focus on eliminating cancer cells through immunotherapy are limited. To develop therapeutic cancer vaccines, the integration of optimal adjuvants is a potential strategy to enhance or complement existing therapeutic approaches. However, conventional adjuvants do not satisfy the criteria of clinical trials for therapeutic cancer vaccines. To improve the effects of adjuvants in therapeutic cancer vaccines, effective vaccination strategies must be formulated and novel adjuvants must be identified. This review offers an overview of the current advancements in therapeutic cancer vaccines and highlights in situ vaccination approaches that can be synergistically combined with other immunotherapies by harnessing the adjuvant effects. Additionally, the refinement of adjuvant systems using cutting-edge technologies and the elucidation of molecular mechanisms underlying immunogenic cell death to facilitate the development of innovative adjuvants have been discussed.

Molecular Prevalence, Genetic Diversity, and Tissue Tropism of Bartonella Species in Small Mammals from Yunnan Province, China.

Bartonella is an intracellular parasitic zoonotic pathogen that can infect animals and cause a variety of human diseases. This study investigates Bartonella prevalence in small mammals in Yunnan Province, China, focusing on tissue tropism. A total of 333 small mammals were sampled from thirteen species, three orders, four families, and four genera in Heqing and Gongshan Counties. Conventional PCR and real-time quantitative PCR (qPCR) were utilized for detection and quantification, followed by bioinformatic analysis of obtained DNA sequences. Results show a 31.5% detection rate, varying across species. Notably, Apodemus chevrieri, Eothenomys eleusis, Niviventer fulvescens, Rattus tanezumi, Episoriculus leucops, Anourosorex squamipes, and Ochotona Thibetana exhibited infection rates of 44.4%, 27.7%, 100.0%, 6.3%, 60.0%, 23.5%, and 22.2%, respectively. Genetic analysis identified thirty, ten, and five strains based on ssrA, rpoB, and gltA genes, with nucleotide identities ranging from 92.1% to 100.0%. Bartonella strains were assigned to B. grahamii, B. rochalimae, B. sendai, B. koshimizu, B. phoceensis, B. taylorii, and a new species identified in Episoriculus leucops (GS136). Analysis of the different tissues naturally infected by Bartonella species revealed varied copy numbers across different tissues, with the highest load in spleen tissue. These findings underscore Bartonella's diverse species and host range in Yunnan Province, highlighting the presence of extensive tissue tropism in Bartonella species naturally infecting small mammalian tissues.

Butane Tetracarboxylic Acid Grafted on Polymeric Nanofibrous Aerogels for Highly Efficient Protein Absorption and Separation.

Developing high-performance and low-cost protein purification materials is of great importance to meet the demands for highly purified proteins in biotechnological industries. Herein, a facile strategy was developed to design and construct high-efficiency protein absorption and separation media by combining aerogels' molding techniques and impregnation processes. Poly (ethylene-co-vinyl alcohol) (EVOH) nanofibrous aerogels (NFAs) were modified by grafting butane tetracarboxylic acid (BTCA) over them in situ. This modification was carried out using polyphosphoric acid as a catalyst. The resulting EVOH/BTCA NFAs exhibited favorable comprehensive properties. Benefiting from the highly interconnected porous structure, good underwater compressive properties, and abundant absorption ligands, the obtained EVOH/BTCA NFAs possessed a high static absorption capacity of 1082.13 mg/g to lysozyme and a short absorption equilibrium time of about 6 h. A high saturated dynamic absorption capacity for lysozyme (716.85 mg/g) was also realized solely by gravity. Furthermore, EVOH/BTCA NFAs displayed excellent reusability, good acid and alkaline resistance, and unique absorption selectivity performance. The successful synthesis of such aerogels can provide a potential candidate for next-generation protein absorbents for bio-separation and purification engineering.

Association of healthy lifestyle with self-reported sleep disordered breathing: A cross-sectional study.

Some healthy lifestyle components have been linked with sleep disordered breathing (SDB), yet little is known about the relationship between comprehensive lifestyle factors and SDB. This study aimed to examine the healthy lifestyle with SDB in community-dwelling adults. We conducted a cross-sectional analysis of the Suzhou Food Consumption and Health Survey in China between 2018 and 2020. The healthy lifestyle index (HLI) was created by combining smoking, alcohol drinking, diet, physical activity, and body mass index (BMI). Its association with SDB was assessed by multiple logistic regression analysis. Subgroup analysis and sensitivity analysis were conducted to assess the robustness of our results. The final analysis included 3788 participants (2859 without SDB and 929 with SDB). In multivariable-adjusted analyses, non-smoking (OR: 0.58, 95 % CI: 0.47-0.71), non-drinking (OR: 0.55, 95 % CI: 0.45-0.68), healthy diet (OR: 0.79, 95 % CI: 0.65-0.95), and healthy BMI (OR: 0.72, 95 % CI: 0.6-0.86) were associated with SDB. Compared with participants with HLI score of 0-1, participants with HLI score of 2, 3, 4, and 5 had OR of 0.68 (95 % CI: 0.51-0.91), 0.49 (95 % CI: 0.37-0.64), 0.29 (95 % CI: 0.21-0.38), and 0.22 (95 % CI: 0.15-0.33), respectively, after adjustment for confounding factors (P-trend<0.001). An inverse dose-response relationship between HLI and SDB was also observed. The association was similar in subgroups stratified by sex, marital status, diabetes and dyslipidemia. A higher score of HLI was associated with reduced odds of SDB in Chinese adults. Our findings suggest the potential of addressing five modifiable lifestyle factors for the prevention of SDB.

Screening for ferroptosis genes related to endometrial carcinoma and predicting of targeted drugs based on bioinformatics.

Endometrial carcinoma is one of most common malignant tumors in women, and ferroptosis is closely related to the development and treatment of endometrial carcinoma. The aim of this study was to screen ferroptosis-related genes associated with endometrial carcinoma and predict targeted drugs through bioinformatics. 761 differentially expressed genes were obtained by the dataset GSE63678 from the GEO database, and most of the genes were enriched in the KEGG_CELL_CYCLE and KEGG_OOCYTE_MEIOSIS signaling pathways. 22 ferroptosis-differentially expressed genes were obtained by intersection with the FerrDb database. These genes were involved in biological processes including macromolecular complex assembly and others, and involved in signal pathways including glutathione metabolism, p53 signaling pathway and others. CDKN2A, IDH1, NRAS, TFRC and GOT1 were obtained as hub genes by PPI network analysis. GEPIA showed that CDKN2A, IDH1, NRAS and TFRC were significantly expressed in endometrial carcinoma. Immunohistochemical results showed that CDKN2A, NRAS and TFRC were significantly expressed in endometrial carcinoma clinical tissue samples. The ROC constructed by TCGA database showed that CDKN2A, NRAS and TFRC had significant value in the diagnosis of endometrial carcinoma, and all had prognostic efficacy. 136,572-09-3 BOSS and others were identified as potential targeted drugs for endometrial carcinoma targeting ferroptosis. Our study has shown that ferroptosis-related genes CDKN2A, NRAS and TFRC are diagnostic markers of endometrial carcinoma, and 136,572-09-3 BOSS, methyprylon BOSS, daunorubicin CTD 00005752, nitroglycerin BOSS and dUTP BOSS, IRON BOSS, Imatinib mesylate BOSS, 2-Butanone BOSS, water BOSS, and L-thyroxine BOSS may be potential therapeutic drugs.

Correlation of Blood and Intestinal Mucosa miR-34a Expressions with Disease Severity in Ulcerative Colitis Patients.

BACKGROUND: miR-34a has been implicated in many autoimmune diseases and gastrointestinal diseases. However, the expression of miR-34 in ulcerative colitis (UC) patients were not fully studied. This study was performed to in-vestigate the association of blood and intestinal tissue miR-34a expression of patients with disease severity in UC patients. METHODS: Our study enrolled 82 patients with UC and 80 age- and gender- matched healthy individuals. Blood miR-34a expressions were detected using reverse transcription-polymerase chain reaction (RT-PCR). Local intestinal miR-34a, STAT3 mRNA and IL-23 mRNA expressions were also detected in the lesioned area and adjacent non-affected intestinal tissue in patients. Disease severity of UC was assessed by Mayo score. The diagnostic value of both blood and local miR-34a expression for UC patients was assessed by receiver operating characteristic (ROC) curve. RESULTS: Blood miR-34a was increased in UC patients in contrast with healthy individuals with statistical significance. In UC patients, local intestinal miR-34a expressions were markedly upregulated compared to adjacent non-affected intestinal tissue. Local intestinal miR-34a expressions were positively correlated with STAT3 mRNA and IL-23 mNRA. Both blood and local miR-34a expressions were significantly and positively related to Mayo scores. ROC curve analysis indicated that both blood and local miR-34a expressions may act as decent marker for Mayo grade. CONCLUSIONS: Blood and intestinal tissue miR-34a expressions are correlated with disease severity in UC patients. Both blood and intestinal tissue miR-34a expressions may serve as potential diagnostic and prognostic makers for UC. Therapeutic methods targeting miR-34a may act as potential ways for UC treatment.

Placental inflammatory injury induced by chlorinated polyfluorinated ether sulfonate (F-53B) through NLRP3 inflammasome activation.

Chlorinated polyfluorinated ether sulfonate, commercially known as F-53B, has been associated with adverse birth outcomes. However, the reproductive toxicology of F-53B on the placenta remains poorly understood. To address this gap, we examined the impact of F-53B on placental injury and its underlying molecular mechanisms in vivo. Pregnant C57BL/6 J female mice were randomly allocated to three groups: the control group, F-53B 0.8 µg/kg/day group, and F-53B 8 µg/kg/day group. After F-53B exposure through free drinking water from gestational day (GD) 0.5-14.5, the F-53B 8 µg/kg/day group exhibited significant increases in placental weights and distinctive histopathological alterations, including inflammatory cell infiltration, heightened syncytiotrophoblast knots, and a loosened trophoblastic basement membrane. Within the F-53B 8 µg/kg/day group, placental tissue exhibited increased apoptosis, as indicated by increased caspase3 activation. Furthermore, F-53B potentially induced the NF-κB signaling pathway activation through IκB-α phosphorylation. Subsequently, this activation upregulated the expression of inflammatory cytokines and components of the NLRP3 inflammasome, including activated caspase1, IL-1ß, IL-18, and cleaved gasdermin D (GSDMD), ultimately leading to pyroptosis in the mouse placenta. Our findings reveal a pronounced inflammatory injury in the placenta due to F-53B exposure, suggesting potential reproductive toxicity at concentrations relevant to the human population. Further toxicological and epidemiological investigations are warranted to conclusively assess the reproductive health risks posed by F-53B.

Gut Microbiota Improves Prognostic Prediction in Critically Ill COVID-19 Patients Alongside Immunological and Hematological Indicators.

The gut microbiota undergoes substantial changes in COVID-19 patients; yet, the utility of these alterations as prognostic biomarkers at the time of hospital admission, and its correlation with immunological and hematological parameters, remains unclear. The objective of this study is to investigate the gut microbiota's dynamic change in critically ill patients with COVID-19 and evaluate its predictive capability for clinical outcomes alongside immunological and hematological parameters. In this study, anal swabs were consecutively collected from 192 COVID-19 patients (583 samples) upon hospital admission for metagenome sequencing. Simultaneously, blood samples were obtained to measure the concentrations of 27 cytokines and chemokines, along with hematological and biochemical indicators. Our findings indicate a significant correlation between the composition and dynamics of gut microbiota with disease severity and mortality in COVID-19 patients. Recovered patients exhibited a higher abundance of Veillonella and denser interactions among gut commensal bacteria compared to deceased patients. Furthermore, the abundance of gut commensal bacteria exhibited a negative correlation with the concentration of proinflammatory cytokines and organ damage markers. The gut microbiota upon admission showed moderate prognostic prediction ability with an AUC of 0.78, which was less effective compared to predictions based on immunological and hematological parameters (AUC 0.80 and 0.88, respectively). Noteworthy, the integration of these three datasets yielded a higher predictive accuracy (AUC 0.93). Our findings suggest the gut microbiota as an informative biomarker for COVID-19 prognosis, augmenting existing immune and hematological indicators.

Magnetic resonance image reconstruction based on image decomposition constrained by total variation and tight frame.

OBJECTIVES: Magnetic resonance imaging (MRI) is a commonly used tool in clinical medicine, but it suffers from the disadvantage of slow imaging speed. To address this, we propose a novel MRI reconstruction algorithm based on image decomposition to realize accurate image reconstruction with undersampled k-space data. METHODS: In our algorithm, the MR images to be recovered are split into cartoon and texture components utilizing image decomposition theory. Different sparse transform constraints are applied to each component based on their morphological structure characteristics. The total variation transform constraint is used for the smooth cartoon component, while the L0 norm constraint of tight frame redundant transform is used for the oscillatory texture component. Finally, an alternating iterative minimization approach is adopted to complete the reconstruction. RESULTS: Numerous numerical experiments are conducted on several MR images and the results consistently show that, compared with the existing classical compressed sensing algorithm, our algorithm significantly improves the peak signal-to-noise ratio of the reconstructed images and preserves more image details. CONCLUSIONS: Our algorithm harnesses the sparse characteristics of different image components to reconstruct MR images accurately with highly undersampled data. It can greatly accelerate MRI speed and be extended to other imaging reconstruction fields.

Application of convolutional neural network for differentiating ovarian thecoma-fibroma and solid ovarian cancer based on MRI.

BACKGROUND: Ovarian thecoma-fibroma and solid ovarian cancer have similar clinical and imaging features, and it is difficult for radiologists to differentiate them. Since the treatment and prognosis of them are different, accurate characterization is crucial. PURPOSE: To non-invasively differentiate ovarian thecoma-fibroma and solid ovarian cancer by convolutional neural network based on magnetic resonance imaging (MRI), and to provide the interpretability of the model. MATERIAL AND METHODS: A total of 156 tumors, including 86 ovarian thecoma-fibroma and 70 solid ovarian cancer, were split into the training set, the validation set, and the test set according to the ratio of 8:1:1 by stratified random sampling. In this study, we used four different networks, two different weight modes, two different optimizers, and four different sizes of regions of interest (ROI) to test the model performance. This process was repeated 10 times to calculate the average performance of the test set. The gradient weighted class activation mapping (Grad-CAM) was used to explain how the model makes classification decisions by visual location map. RESULTS: ResNet18, which had pre-trained weight, using Adam and one multiple ROI circumscribed rectangle, achieved best performance. The average accuracy, precision, recall, and AUC were 0.852, 0.828, 0.848, and 0.919 (P < 0.01), respectively. Grad-CAM showed areas associated with classification appeared on the edge or interior of ovarian thecoma-fibroma and the interior of solid ovarian cancer. CONCLUSION: This study shows that convolution neural network based on MRI can be helpful for radiologists in differentiating ovarian thecoma-fibroma and solid ovarian cancer.

Two telomere-to-telomere gapless genomes reveal insights into Capsicum evolution and capsaicinoid biosynthesis.

Chili pepper (Capsicum) is known for its unique fruit pungency due to the presence of capsaicinoids. The evolutionary history of capsaicinoid biosynthesis and the mechanism of their tissue specificity remain obscure due to the lack of high-quality Capsicum genomes. Here, we report two telomere-to-telomere (T2T) gap-free genomes of C. annuum and its wild nonpungent relative C. rhomboideum to investigate the evolution of fruit pungency in chili peppers. We precisely delineate Capsicum centromeres, which lack high-copy tandem repeats but are extensively invaded by CRM retrotransposons. Through phylogenomic analyses, we estimate the evolutionary timing of capsaicinoid biosynthesis. We reveal disrupted coding and regulatory regions of key biosynthesis genes in nonpungent species. We also find conserved placenta-specific accessible chromatin regions, which likely allow for tissue-specific biosynthetic gene coregulation and capsaicinoid accumulation. These T2T genomic resources will accelerate chili pepper genetic improvement and help to understand Capsicum genome evolution.

MOFs-Based Materials with Confined Space: Opportunities and Challenges for Energy and Catalytic Conversion.

Metal-Organic Frameworks (MOFs) are a very promising material in the fields of energy and catalysis due to their rich active sites, tunable pore size, structural adaptability, and high specific surface area. The concepts of "carbon peak" and "carbon neutrality" have opened up huge development opportunities in the fields of energy storage, energy conversion, and catalysis, and have made significant progress and breakthroughs. In recent years, people have shown great interest in the development of MOFs materials and their applications in the above research fields. This review introduces the design strategies and latest progress of MOFs are included based on their structures such as core-shell, yolk-shell, multi-shelled, sandwich structures, unique crystal surface exposures, and MOF-derived nanomaterials in detail. This work comprehensively and systematically reviews the applications of MOF-based materials in energy and catalysis and reviews the research progress of MOF materials for atmospheric water harvesting, seawater uranium extraction, and triboelectric nanogenerators. Finally, this review looks forward to the challenges and opportunities of controlling the synthesis of MOFs through low-cost, improved conductivity, high-temperature heat resistance, and integration with machine learning. This review provides useful references for promoting the application of MOFs-based materials in the aforementioned fields.

Multi-Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow-Band Imaging: A Multicenter Study.

OBJECTIVES: Vocal fold leukoplakia (VFL) is a precancerous lesion of laryngeal cancer, and its endoscopic diagnosis poses challenges. We aim to develop an artificial intelligence (AI) model using white light imaging (WLI) and narrow-band imaging (NBI) to distinguish benign from malignant VFL. METHODS: A total of 7057 images from 426 patients were used for model development and internal validation. Additionally, 1617 images from two other hospitals were used for model external validation. Modeling learning based on WLI and NBI modalities was conducted using deep learning combined with a multi-instance learning approach (MIL). Furthermore, 50 prospectively collected videos were used to evaluate real-time model performance. A human-machine comparison involving 100 patients and 12 laryngologists assessed the real-world effectiveness of the model. RESULTS: The model achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.868 and 0.884 in the internal and external validation sets, respectively. AUC in the video validation set was 0.825 (95% CI: 0.704-0.946). In the human-machine comparison, AI significantly improved AUC and accuracy for all laryngologists (p < 0.05). With the assistance of AI, the diagnostic abilities and consistency of all laryngologists improved. CONCLUSIONS: Our multicenter study developed an effective AI model using MIL and fusion of WLI and NBI images for VFL diagnosis, particularly aiding junior laryngologists. However, further optimization and validation are necessary to fully assess its potential impact in clinical settings. LEVEL OF EVIDENCE: 3 Laryngoscope, 2024.