Different clinical characteristics and outcomes of adult hospitalized SARS-CoV-2 pneumonia patients complicated by cardiovascular events during the first, delta and omicron waves of COVID-19.

Background: The effects of SARS-CoV-2 have varied between significant waves of hospitalization. Research question: Are cardiovascular complications different among the first, delta and omicron waves of hospitalized COVID-19 pneumonia patients? Study design and methods: This was a multi-centre retrospective study of patients hospitalized with SARS-CoV-2 pneumonia: 632 were hospitalized during the first wave (March-July 2020), 1013 during the delta wave (September 2020-March 2021), and 323 during the omicron wave (January 2022-July 2022). Patients were stratified by wave and occurrence of cardiovascular events. Results: Among all hospitalized patients with cardiovascular events, patients in the omicron wave were younger (62.4 ± 14 years) than patients in the first wave (67.4 ± 7.8 years) and the delta wave (66.9 ± 12.6 years) and had a higher proportion of non-Hispanic White people than in the first wave (78.6% vs. 61.7%). For COVID-19 patients who suffered from cardiovascular events, the omicron wave patients had significantly higher neutrophil/lymphocyte ratio, white blood cell and platelet counts when compared to the first wave. Omicron wave patients had significantly lower albumin and B-type natriuretic peptide levels (only 5.8% of the first wave and 14.6% of the delta wave) when compared to either the first wave or delta wave patients. In COVID-19 patients who suffered cardiovascular events during hospitalization, mortality rate in the omicron wave (26.8%) was significantly lower than the first wave (48.3%), time to mortality for non-survivors of COVID-19 patients who suffered cardiovascular events was significantly longer in the omicron wave (median 16 days) than in the first wave (median 10 days). Conclusions: Younger and white patients were affected with cardiovascular complications more often by the omicron variant. Despite higher neutrophil/lymphocyte ratio and WBC counts, the omicron patients with cardiovascular events showed lower heart injuries, lower mortality and longer time to mortality for non-survivors when compared to the first and delta waves.

Anti-angiogenic therapy or immunotherapy? A real-world study of patients with advanced non-small cell lung cancer with EGFR/HER2 exon 20 insertion mutations.

Background: For patients with EGFR/HER2 exon20 insertions, platinum-containing double-drug chemotherapy is still the standard treatment method. First-generation TKIs have almost no therapeutic activity against EGFR exon 20 insertions. The efficacy of second-and third-generation TKIs is still controversial. Immunotherapy research is scarce, and there is an urgent need for more evidence and new treatment options for this group of patients. Methods: We reviewed patients with advanced NSCLC with EGFR/HER2 exon 20 insertion mutations treated in Shanghai Chest Hospital and Shanghai Pulmonary Hospital from 2015 to 2022 and assessed the efficacy of receiving chemotherapy, anti-angiogenic therapy and immunotherapy, including objective response rate (ORR) and disease control rate (DCR), and compared progression-free survival (PFS) and overall survival (OS). Results: Of the 126 patients included in the study, 51 patients had EGFR20ins mutations and 7 5 patients had HER2-20ins mutations. In the first-line treatment, bevacizumab + chemotherapy (Beva+Chemo), ICI+chemotherapy (ICI+Chemo), compared with chemotherapy alone (Chemo), ORR: 40% vs 33.3% vs 15% (p=0.0168); DCR: 84% vs 80.9% vs 67.5% (p=0.1817); median PFS: 8.3 vs 7.0 vs 4.6 months (p=0.0032), ICI+Chemo has a trend of benefiting on OS. Stratified analysis showed that compared with chemotherapy, ICI+Chemo was more effective for EGFR20ins mutation with median PFS: 10.3 vs. 6.3m (P=0.013); Beva+Chemo was more effective for HER2-20ins mutation, with a median PFS: 6.6 vs. 4.3m (p=0.030). In the second-line treatment of EGFR20ins mutation, bevacizumab + chemotherapy has a significant advantage in PFS compared with targeted therapy, median PFS:10.8 vs 4.0 months (P=0.016). Conclusion: For patients with EGFR20ins mutation, compared to chemotherapy, ICI+Chemo prolongs PFS, and after chemotherapy progression, bevacizumab combined with chemotherapy seems better than Furmonertinib-based targeted therapy on PFS. For HER2-20ins mutation, Beva+Chemo may be a better choice.

Proteomic characterization of esophageal squamous cell carcinoma response to immunotherapy reveals potential therapeutic strategy and predictive biomarkers.

Immunotherapy is the first-line therapy for esophageal squamous cell carcinoma (ESCC), yet many patients do not respond due to drug resistance and the lack of reliable predictive markers. We collected 73 ESCC patients (including discovery cohort and validation cohort) without immune thrombocytopenia and undergoing anti-PD1 immunotherapy. Proteomic and phosphoproteomic analysis of 73 ESCC treatment-naive samples by mass spectrometry-based label-free quantification were applied to explore the potential resistant and sensitive mechanisms, and identify predictive markers of ESCC immunotherapy. Comparative analysis found the pathways related to immune and mitochondrial functions were associated with ESCC immunotherapy sensitivity; while platelet activation bioprocess showed negative correlation with CD8+ T cells and related to ESCC immunotherapy non-sensitivity. Finally, we identified 10 ESCC immunotherapy predictive biomarkers with high accuracy (≥ 0.90) to predict the immunotherapeutic response, which was validated in the independent cohort.

Development of a double antibody sandwich ELISA method for the quantitative detection of serum C-reactive protein based on nanobody.

In this study, we successfully developed a nanobody-based double antibody sandwich ELISA kit for the detection of clinical serum C-reactive protein (CRP) by using two novel CRP specific nanobodies. The developed method exhibited a linear detection range of approximately 6-200 ng/mL, with a detection limit of 1 ng/mL. Furthermore, the method demonstrated excellent specificity, as there was no cross-reactivity with interfering substances such as total bilirubin and hemoglobin and so on. To assess reproducibility, independent measurements of the samples were conducted under experimental conditions, resulting in intra- and inter-batch coefficients of variation below 10% and a recovery rate of 93%-102%. These results indicate robust reproducibility of the method. To evaluate the performance of the developed kit, we collected 90 clinical samples for correlation analysis with commercial kits. The results showed a high correlation coefficient value (R2) of 0.98, indicating accurate concordance between the developed and commercial kits. In conclusion, our study successfully developed a nanobody-based double antibody sandwich ELISA kit to detect clinical serum CRP. The utilization of nanobodies represents a significant advancement in the field of CRP immunoassay development. The developed kit demonstrates excellent performance characteristics and holds promise for clinical applications.

Peroxygenase-Enabled Reductive Kinetic Resolution for the Enantioenrichment of Organoperoxides.

Enantiomerically pure organoperoxides serve as valuable precursors in organic transformations. Herein, we present the first examples of unspecific peroxygenase catalyzed kinetic resolution of racemic organoperoxides through asymmetric reduction. Through meticulous investigation of the reaction conditions, it is shown that the unspecific peroxygenase from Agrocybe aegerita (AaeUPO) exhibits robust catalytic activity in the kinetic resolution reactions of the model substrate with turnover numbers up to 60000 and turnover frequency of 5.6 s-1. Various aralkyl organoperoxides were successfully resolved by AaeUPO, achieving excellent enantioselectivities (e.g., up to 99 % ee for the (S)-organoperoxide products). Additionally, we screened commercial peroxygenase variants to obtain the organoperoxides with complementary chirality, with one mutant yielding the (R)-products. While unspecific peroxygenases have been extensively demonstrated as a powerful oxidative catalysts, this study highlights their usefulness in catalyzing the reduction of organoperoxides and providing versatile chiral synthons.

Melt-quenched glass formation of a family of metal-carboxylate frameworks.

Metal-organic framework (MOF) glasses are an emerging class of glasses which complement traditional inorganic, organic and metallic counterparts due to their hybrid nature. Although a few zeolitic imidazolate frameworks have been made into glasses, how to melt and quench the largest subclass of MOFs, metal carboxylate frameworks, into glasses remains challenging. Here, we develop a strategy by grafting the zwitterions on the carboxylate ligands and incorporating organic acids in the framework channels to enable the glass formation. The charge delocalization of zwitterion-acid subsystem and the densely filled channels facilitate the coordination bonding mismatch and thus reduce the melting temperature. Following melt-quenching realizes the glass formation of a family of carboxylate MOFs (UiO-67, UiO-68 and DUT-5), which are usually believed to be un-meltable. Our work opens up an avenue for melt-quenching porous molecular solids into glasses.

Unraveling the Significance of MET Focal Amplification in Lung Cancer: Integrative NGS, FISH, and IHC Investigation.

MET amplification (METamp) represents a promising therapeutic target in non-small cell lung cancer, but no consensus has been established to identify METamp-dependent tumors that could potentially benefit from MET inhibitors. In this study, an analysis of MET amplification/overexpression status was performed in a retrospectively recruited cohort comprising 231 patients with non-small cell lung cancer from Shanghai Chest Hospital (SCH cohort) using 3 methods: fluorescence in situ hybridization (FISH), hybrid capture-based next-generation sequencing, and immunohistochemistry for c-MET and phospho-MET. The SCH cohort included 130 cases known to be METamp positive by FISH and 101 negative controls. The clinical relevance of these approaches in predicting the efficacy of MET inhibitors was evaluated. Additionally, next-generation sequencing data from another 2 cohorts including 22,010 lung cancer cases were utilized to examine the biological characteristics of different METamp subtypes. Of the 231 cases, 145 showed MET amplification/overexpression using at least 1 method, whereas only half of them could be identified by all 3 methods. METamp can occur as focal amplification or polysomy. Our study revealed that the inconsistency between next-generation sequencing and FISH primarily occurred in the polysomy subtype. Further investigations indicated that compared with polysomy, focal amplification correlated with fewer co-occurring driver mutations, higher protein expressions of c-MET and phospho-MET, and higher incidence in acquired resistance than in de novo setting. Moreover, patients with focal amplification presented a more robust response to MET inhibitors compared with those with polysomy. Notably, a strong correlation was observed between focal amplification and programmed cell death ligand-1 expression, indicating potential therapeutic implications with combined MET inhibitor and immunotherapy for patients with both alterations. Our findings provide insights into the molecular complexity and clinical relevance of METamp in lung cancer, highlighting the role of MET focal amplification as an oncogenic driver and its feasibility as a primary biomarker to further investigate the clinical activity of MET inhibitors in future studies.

Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning.

OBJECTIVE: Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two. METHODS: Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI. RESULTS: Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%). DISCUSSION: Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered. PUBLIC SIGNIFICANCE: There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection.

BACKGROUND: Despite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy. METHODS: We retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3. FINDINGS: A total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001). INTERPRETATION: IASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

EGFR and ERBB2 Exon 20 Insertion Mutations in Chinese Non-small Cell Lung Cancer Patients: Pathological and Molecular Characterization, and First-Line Systemic Treatment Evaluation.

BACKGROUND: Data from studies looking at both EGFR and ERBB2 exon 20 insertion mutations (-20ins) in the same cohort of patients with non-small cell lung cancer (NSCLC) are limited. OBJECTIVE: The purpose of this study was to analyze EGFR/ERBB2-20ins in all-stage NSCLC patients to reveal their histological and molecular features, and to retrospectively evaluate the results of first-line real-world systemic treatments in patients with advanced-stage disease. PATIENTS AND METHODS: We collected 13,920 formalin-fixed paraffin-embedded NSCLC specimens. Clinicopathological features were recorded and DNA-based next-generation sequencing was performed. First-line systemic treatment data were obtained via chart review. RESULTS: In total, 414 (2.97%) EGFR-20ins cases and 666 (4.78%) ERBB2-20ins cases were identified. Both were more common in women, non-smokers, and patients with adenocarcinoma. The incidence of EGFR/ERBB2-20ins in adenocarcinoma is inversely proportional to the degree of invasion; 77 and 26 variants were detected in EGFR-20ins and ERBB2-20ins cases, respectively. The most common concurrently mutated genes were TP53 and RB1. In invasive adenocarcinoma, lepidic components were more common in EGFR/ERBB2-20ins-alone cases than in those with other concurrent mutated genes. In EGFR-/ERBB2-20ins patients, there was no significant difference in progression-free survival (PFS) or treatment response to first-line systemic treatments in this study. There was no significant difference in PFS or treatment response among patients with different EGFR/ERBB2-20ins variants and those with or without concurrent mutated genes. CONCLUSIONS: EGFR/ERBB2-20ins is more common in early lung adenocarcinoma. EGFR-20ins had more variants. In both cohorts, the results for first-line systemic treatments showed no significant difference.

Integrated transcriptomic analysis systematically reveals the heterogeneity and molecular characterization of cancer-associated fibroblasts in osteosarcoma.

BACKGROUND: Osteosarcoma (OS), with a peak incidence during the adolescent growth spurt, is correlated with poor prognosis for its high malignancy. The tumor microenvironment (TME) is highly complicated, with frequent interactions between tumor and stromal cells. The cancer-associated fibroblasts (CAFs) in the TME have been considered to actively involve in the progression, metastasis, and drug resistance of OS. This study aimed to characterize cellular heterogeneity and molecular characterization in CAFs subtypes and explore the potential targeting therapeutic strategies to improve the prognosis of OS patients. METHODS: The single-cell atlas of human OS tumor lesions were constructed from the GEO database. Then significant marker genes and potential biological functions for each CAFs subtype were identified and explored using the Seurat R package. Next, by performing the survival analyses and constructing the risk scores for CAFs subtypes, we aimed to identify and characterize the prognostic values of specific marker genes and different CAFs subtypes. Furthermore, we explored the therapeutic targets and innovative drugs targeting different CAFs subtypes based on the GDSC database. Finally, prognoses related CAFs subtypes were further validated through immunohistochemistry (IHC) on clinical OS specimens. RESULTS: Overall, nine main cell clusters and five subtypes of CAFs were identified. The differentially expressed marker genes for each CAFs clusters were then identified. Moreover, through Gene Ontology (GO) enrichment analysis, we defined the CAFs_2 (upregulated CXCL14 and C3), which was closely related to leukocyte migration and chemotaxis, as inflammatory CAFs (iCAFs). Likewise, we defined the CAFs_4 (upregulated CD74, HLA-DRA and HLA-DRB1), which was closely related to antigen process and presentation, as antigen-presenting CAFs (apCAFs). Furthermore, Kaplan-Meier analyses showed that CAFs_2 and CAFs_4 were correlated with poor clinical prognosis of OS patients. Meanwhile, therapeutic drugs targeting CAFs_2 and CAFs_4, such as 17-AAG/Docetaxel/Bleomycin and PHA-793887/NG-25/KIN001-102, were also explored, respectively. Finally, IHC assay confirmed the abundant CAFs_2 and CAFs_4 subtypes infiltration in the OS microenvironment compared with adjacent tissues. CONCLUSION: Our study revealed the diversity, complexity, and heterogeneity of CAFs in OS, and complemented the single-cell atlas in OS TME.

Comparisons between atypical anorexia nervosa and anorexia nervosa: Psychological and comorbidity patterns.

OBJECTIVE: Literature comparing "atypical" anorexia nervosa (atypical AN) and anorexia nervosa (AN) suggests these diagnoses share significant similarities in eating disorder (ED) pathology and psychiatric comorbidities. This study evaluated potential differences in ED pathology, psychiatric comorbidity, associated mechanisms (i.e., ED fears and perfectionism), and demographic factors (i.e., ethnicity and age) between individuals with atypical AN and AN. METHOD: Data from seven protocols were combined for a total 464 individuals diagnosed with atypical AN (n = 215) or AN (n = 249). Between-group differences in ED severity and behaviors, psychiatric comorbidities, ED fears, perfectionism, and demographic factors were assessed using t-tests, Wilcoxon rank-sum tests, and Fisher's exact test. RESULTS: Participants with atypical AN reported higher levels of overvaluation of weight and shape than those with AN. Participants with AN scored higher on food-related fears (anxiety about eating, food avoidance behaviors, and feared concerns) and fears of social eating, as well as obsessive-compulsive symptoms. Participants with AN were more likely to identify as Asian or Pacific Islander. No other statistically significant differences were found between groups for overall ED severity, ED behaviors, psychiatric comorbidities, general ED fears, perfectionism, or demographic factors. DISCUSSION: Overall, results support previous literature indicating limited differences between individuals with atypical AN and AN, though individuals with atypical AN reported more overvaluation of weight and shape and those with AN reported higher food and social eating fears and obsessive-compulsive symptoms. Relatively few overall differences between atypical AN and AN highlight the importance of exploring dimensional conceptualizations of AN as an alternative to the current categorical conceptualization. PUBLIC SIGNIFICANCE: This study assessed differences among individuals with atypical anorexia nervosa and anorexia nervosa in eating disorder severity and behaviors, comorbid psychiatric diagnoses, associated mechanisms, and demographic factors. Few differences emerged, though participants with atypical anorexia nervosa reported more overvaluation of weight and shape, while those with anorexia nervosa reported more food and social eating fears and higher obsessive-compulsive symptoms. Results support exploration of these diagnoses as a spectrum disorder.

New dienelactone hydrolase from microalgae bacterial community-Antibiofilm activity against fish pathogens and potential applications for aquaculture.

Biofilms are resistant to many traditional antibiotics, which has led to search for new antimicrobials from different and unique sources. To harness the potential of aquatic microbial resources, we analyzed the meta-omics datasets of microalgae-bacteria communities and mined them for potential antimicrobial and quorum quenching enzymes. One of the most interesting candidates (Dlh3), a dienelactone hydrolase, is a α/ß-protein with predicted eight α-helices and eight ß-sheets. When it was applied to one of the major fish pathogens, Edwardsiella anguillarum, the biofilm development was reproducibly inhibited by up to 54.5%. The transcriptome dataset in presence of Dlh3 showed an upregulation in functions related to self-defense like active genes for export mechanisms and transport systems. The most interesting point regarding the biotechnological potential for aquaculture applications of Dlh3 are clear evidence of biofilm inhibition and that health and division of a relevant fish cell model (CHSE-214) was not impaired by the enzyme.

Genomic Staging of Multifocal Lung Squamous Cell Carcinomas Is Independent of the Comprehensive Morphologic Assessment.

INTRODUCTION: Morphologic and molecular data for staging of multifocal lung squamous cell carcinomas (LSCCs) are limited. In this study, whole exome sequencing (WES) was used as the gold standard to determine whether multifocal LSCC represented separate primary lung cancers (SPLCs) or intrapulmonary metastases (IPMs). Genomic profiles were compared with the comprehensive morphologic assessment. METHODS: WES was performed on 20 tumor pairs of multifocal LSCC and matched normal lymph nodes using the Illumina NovaSeq6000 S4-Xp (Illumina, San Diego, CA). WES clonal and subclonal analysis data were compared with histologic assessment by 16 thoracic pathologists. In addition, the immune gene profiling of the study cases was characterized by the HTG EdgeSeq Precision Immuno-Oncology Panel. RESULTS: By WES data, 11 cases were classified as SPLC and seven cases as IPM. Two cases were technically suboptimal. Analysis revealed marked genomic and immunogenic heterogeneity, but immune gene expression profiles highly correlated with mutation profiles. Tumors classified as IPM have a large number of shared mutations (ranging from 33.5% to 80.7%). The agreement between individual morphologic assessments for each case and WES was 58.3%. One case was unanimously interpreted morphologically as IPM and was in agreement with WES. In a further 17 cases, the number of pathologists whose morphologic interpretation was in agreement with WES ranged from two (one case) to 15 pathologists (one case) per case. Pathologists showed a fair interobserver agreement in the morphologic staging of multiple LSCCs, with an overall kappa of 0.232. CONCLUSIONS: Staging of multifocal LSCC based on morphologic assessment is unreliable. Comprehensive genomic analyses should be adopted for the staging of multifocal LSCC.

Panoramic three-dimensional optical digitization system assisted by a bi-mirror.

The digitization of objects' full surfaces finds widespread applications in fields such as virtual reality, art and design, and medical and biological sciences. For the realization of three-dimensional full-surface digitization of objects within complex sceneries, we propose a straightforward, efficient, and robust panoramic three-dimensional optical digitization system. This system contains a laser-based optical three-dimensional measurement system and a bi-mirror. By integrating mirrors into the system, we enable the illumination of the object from all angles using the projected laser beam in a single scanning process. Moreover, the main camera employed in the system can acquire three-dimensional information of the object from several different viewpoints. The rotational scanning method enhances the efficiency and applicability of the three-dimensional scanning process, enabling the acquisition of surface information of large-scale objects. After obtaining the three-dimensional data of the sample from different viewpoints using laser triangulation, mirror reflection transformation was employed to obtain the full-surface three-dimensional data of the object in the global coordinate system. The proposed method has been subjected to precision and validity experiments using samples with different surface characteristics and sizes, resulting in the demonstration of its capability for achieving correct three-dimensional digitization of the entire surface in diverse complex sceneries.

Hsa-LINC02418/mmu-4930573I07Rik regulated by METTL3 dictates anti-PD-L1 immunotherapeutic efficacy via enhancement of Trim21-mediated PD-L1 ubiquitination.

BACKGROUND: Limited response to programmed death ligand-1 (PD-L1)/programmed death 1 (PD-1) immunotherapy is a major hindrance of checkpoint immunotherapy in non-small cell lung cancer (NSCLC). The abundance of PD-L1 on the tumor cell surface is crucial for the responsiveness of PD-1/PD-L1 immunotherapy. However, the negative control of PD-L1 expression and the physiological significance of the PD-L1 inhibition in NSCLC immunotherapy remain obscure. METHODS: Bioinformatics analysis was performed to profile and investigate the long non-coding RNAs that negatively correlated with PD-L1 expression and positively correlated with CD8+T cell infiltration in NSCLC. Immunofluorescence, in vitro PD-1 binding assay, T cell-induced apoptosis assays and in vivo syngeneic mouse models were used to investigate the functional roles of LINC02418 and mmu-4930573I07Rik in regulating anti-PD-L1 therapeutic efficacy in NSCLC. The molecular mechanism of LINC02418-enhanced PD-L1 downregulation was explored by immunoprecipitation, RNA immunoprecipitation (RIP), and ubiquitination assays. RIP, luciferase reporter, and messenger RNA degradation assays were used to investigate the m6A modification of LINC02418 or mmu-4930573I07Rik expression. Bioinformatics analysis and immunohistochemistry (IHC) verification were performed to determine the significance of LINC02418, PD-L1 expression and CD8+T cell infiltration. RESULTS: LINC02418 is a negative regulator of PD-L1 expression that positively correlated with CD8+T cell infiltration, predicting favorable clinical outcomes for patients with NSCLC. LINC02418 downregulates PD-L1 expression by enhancing PD-L1 ubiquitination mediated by E3 ligase Trim21. Both hsa-LINC02418 and mmu-4930573I07Rik (its homologous RNA in mice) regulate PD-L1 therapeutic efficacy in NSCLC via Trim21, inducing T cell-induced apoptosis in vitro and in vivo. Furthermore, METTL3 inhibition via N6-methyladenosine (m6A) modification mediated by YTHDF2 reader upregulates hsa-LINC02418 and mmu-4930573I07Rik. In patients with NSCLC, LINC02418 expression is inversely correlated with PD-L1 expression and positively correlated with CD8+T infiltration. CONCLUSION: LINC02418 functions as a negative regulator of PD-L1 expression in NSCLC cells by promoting the degradation of PD-L1 through the ubiquitin-proteasome pathway. The expression of LINC02418 is regulated by METTL3/YTHDF2-mediated m6A modification. This study illuminates the underlying mechanisms of PD-L1 negative regulation and presents a promising target for improving the effectiveness of anti-PD-L1 therapy in NSCLC.

Improving Indoor Pedestrian Dead Reckoning for Smartphones under Magnetic Interference Using Deep Learning.

As micro-electro-mechanical systems (MEMS) technology continues its rapid ascent, a growing array of smart devices are integrating lightweight, compact, and cost-efficient magnetometers and inertial sensors, paving the way for advanced human motion analysis. However, sensors housed within smartphones frequently grapple with the detrimental effects of magnetic interference on heading estimation, resulting in diminished accuracy. To counteract this challenge, this study introduces a method that synergistically employs convolutional neural networks (CNNs) and support vector machines (SVMs) for adept interference detection. Utilizing a CNN, we automatically extract profound features from single-step pedestrian motion data that are then channeled into an SVM for interference detection. Based on these insights, we formulate heading estimation strategies aptly suited for scenarios both devoid of and subjected to magnetic interference. Empirical assessments underscore our method's prowess, boasting an impressive interference detection accuracy of 99.38%. In indoor environments influenced by such magnetic disturbances, evaluations conducted along square and equilateral triangle trajectories revealed single-step heading absolute error averages of 2.1891° and 1.5805°, with positioning errors averaging 0.7565 m and 0.3856 m, respectively. These results lucidly attest to the robustness of our proposed approach in enhancing indoor pedestrian positioning accuracy in the face of magnetic interferences.

Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study.

Background: Predicting disability risk in healthy older adults in China is essential for timely preventive interventions, improving their quality of life, and providing scientific evidence for disability prevention. Therefore, developing a machine learning model capable of evaluating disability risk based on longitudinal research data is crucial. Methods: We conducted a prospective cohort study of 2,175 older adults enrolled in the China Health and Retirement Longitudinal Study (CHARLS) between 2015 and 2018 to develop and validate this prediction model. Several machine learning algorithms (logistic regression, k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, and XGBoost) were used to assess the 3-year risk of developing disability. The optimal cutoff points and adjustment parameters are explored in the training set, the prediction accuracy of the models is compared in the testing set, and the best-performing models are further interpreted. Results: During a 3-year follow-up period, a total of 505 (23.22%) healthy older adult individuals developed disabilities. Among the 43 features examined, the LASSO regression identified 11 features as significant for model establishment. When comparing six different machine learning models on the testing set, the XGBoost model demonstrated the best performance across various evaluation metrics, including the highest area under the ROC curve (0.803), accuracy (0.757), sensitivity (0.790), and F1 score (0.789), while its specificity was 0.712. The decision curve analysis (DCA) indicated showed that XGBoost had the highest net benefit in most of the threshold ranges. Based on the importance of features determined by SHAP (model interpretation method), the top five important features were identified as right-hand grip strength, depressive symptoms, marital status, respiratory function, and age. Moreover, the SHAP summary plot was used to illustrate the positive or negative effects attributed to the features influenced by XGBoost. The SHAP dependence plot explained how individual features affected the output of the predictive model. Conclusion: Machine learning-based prediction models can accurately evaluate the likelihood of disability in healthy older adults over a period of 3 years. A combination of XGBoost and SHAP can provide clear explanations for personalized risk prediction and offer a more intuitive understanding of the effect of key features in the model.