TMPRSS2 is a tumor suppressor and its downregulation promotes antitumor immunity and immunotherapy response in lung adenocarcinoma.

BACKGROUND: TMPRSS2, a key molecule for SARS-CoV-2 invading human host cells, has an association with cancer. However, its association with lung cancer remains insufficiently unexplored. METHODS: In five bulk transcriptomics datasets, one single-cell RNA sequencing (scRNA-seq) dataset and one proteomics dataset for lung adenocarcinoma (LUAD), we explored associations between TMPRSS2 expression and immune signatures, tumor progression phenotypes, genomic features, and clinical prognosis in LUAD by the bioinformatics approach. Furthermore, we performed experimental validation of the bioinformatics findings. RESULTS: TMPRSS2 expression levels correlated negatively with the enrichment levels of both immune-stimulatory and immune-inhibitory signatures, while they correlated positively with the ratios of immune-stimulatory/immune-inhibitory signatures. It indicated that TMPRSS2 levels had a stronger negative correlation with immune-inhibitory than with immune-stimulatory signatures. TMPRSS2 downregulation correlated with increased proliferation, stemness, genomic instability, tumor progression, and worse survival in LUAD. We further validated that TMPRSS2 was downregulated with tumor progression in the LUAD cohort we collected from Jiangsu Cancer Hospital, China. In vitro and in vivo experiments verified the association of TMPRSS2 deficiency with increased tumor cell proliferation and invasion and antitumor immunity in LUAD. Moreover, in vivo experiments demonstrated that TMPRSS2-knockdown tumors were more sensitive to BMS-1, an inhibitor of PD-1/PD-L1. CONCLUSIONS: TMPRSS2 is a tumor suppressor, while its downregulation is a positive biomarker of immunotherapy in LUAD. Our data provide a potential link between lung cancer and pneumonia caused by SARS-CoV-2 infection.

Efficacy of Repetitive Transcranial Magnetic Stimulation over the Supplementary Motor Area on Motor Function in Parkinson's Disease: A Meta-Analysis.

OBJECTIVES: The objective of this study was to assess the effect of repetitive transcranial magnetic stimulation (rTMS) on the supplementary motor area (SMA) in motor function in Parkinson's disease (PD) patients. METHOD: Databases searched included 5 databases from October 7,2022 to January 4, 2023. The Cochrane Bias Risk Assessment Tool was used for quality assessment. Standardized mean differences (SMDs) were calculated using a random-effects model. Outcome measure is the motor function examination of the motor part of Unified Parkinson's Disease Rating Scale (UPDRS-III). RESULTS: Seven studies totaling 374 patients were included. Meta-analysis showed that stimulation of SMA significantly improved motor function in PD patients compared with sham stimulation (SMD = -1.24; 95% CI, -2.24 to -0.24; P = 0.02; I 2 = 93%). Stimulation of the same target (SMA), subgroup analysis showed that high-frequency rTMS (HF-rTMS) is more effective than low-frequency rTMS (LF-rTMS) in improving motor function in PD (SMD = -1.39; 95% CI, -2.21 to -0.57; P = 0.04; I 2 = 77.2%). CONCLUSIONS: Overall, rTMS over SMA had a statistically significant improvement in motor function in PD patients, and HF-rTMS is statistically significantly more effective than LF-rTMS.

Can cerebellar theta-burst stimulation improve balance function and gait in stroke patients? A randomized controlled trial.

BACKGROUND: The cerebellum is a key structure involved in balance and motor control, and has become a new stimulation target in brain regulation technology. Interference theta-burst simulation (iTBS) is a novel simulation mode of repetitive transcranial magnetic simulation. However, the impact of cerebellar iTBS on balance function and gait in stroke patients is still unknown. AIM: The aim of this study was to determine whether cerebellar iTBS can improve function, particularly balance and gait, in patients with post-stroke hemiplegia. DESIGN: This study is a randomized, double-blind, sham controlled clinical trial. SETTING: The study was carried out at the Department of Rehabilitation Medicine in a general hospital. POPULATION: Patients with stroke with first unilateral lesions were enrolled in the study. METHODS: Thirty-six patients were randomly assigned to the cerebellar iTBS group or sham stimulation group. The cerebellar iTBS or pseudo stimulation site is the ipsilateral cerebellum on the paralyzed side, which is completed just before daily physical therapy. The study was conducted five times a week for two consecutive weeks. All patients were assessed before the intervention (T0) and at the end of 2 weeks of treatment (T1), respectively. The primary outcome was the Berg Balance Scale (BBS), while secondary outcome measures included the Fugl Meyer Lower Limb Assessment Scale (FMA-LE), timed up and go (TUG), Barthel Index (BI), and gait analysis. RESULTS: After 2 weeks of intervention, the BBS, FMA-LE, TUG, and BI score in both the iTBS group and the sham group were significantly improved compared to the baseline (all P<0.05). Also, there was a significant gait parameter improvement including the cadence, stride length, velocity, step length compared to the baseline (P<0.05) in the iTBS group, but only significant improvement in cadence was identified in the sham group (P<0.05). Intergroup comparison showed that the BBS (P<0.001), FMA-LE (P<0.001), and BI (P=0.002) in the iTBS group were significantly higher than those in the sham group, and the TUG in the iTBS was significantly lower than that in the sham group (P=0.002). In addition, there were significant differences in cadence (P=0.029), strip length (P=0.046), gain velocity (P=0.002), and step length of affected lower limb (P=0.024) between the iTBS group and the sham iTBS group. CONCLUSIONS: Physical therapy is able to improve the functional recovery in hemiplegic patients after stroke, but the cerebellar iTBS can facilitate and accelerate the recovery, particularly the balance function and gait. Cerebellar iTBS could be an efficient and facilitative treatment for patients with stroke. CLINICAL REHABILITATION IMPACT: Cerebellar iTBS provides a convenient and efficient treatment modality for functional recovery of patients with stroke, especially balance function and gait.

A model-based MR parameter mapping network robust to substantial variations in acquisition settings.

Deep learning methods show great potential for the efficient and precise estimation of quantitative parameter maps from multiple magnetic resonance (MR) images. Current deep learning-based MR parameter mapping (MPM) methods are mostly trained and tested using data with specific acquisition settings. However, scan protocols usually vary with centers, scanners, and studies in practice. Thus, deep learning methods applicable to MPM with varying acquisition settings are highly required but still rarely investigated. In this work, we develop a model-based deep network termed MMPM-Net for robust MPM with varying acquisition settings. A deep learning-based denoiser is introduced to construct the regularization term in the nonlinear inversion problem of MPM. The alternating direction method of multipliers is used to solve the optimization problem and then unrolled to construct MMPM-Net. The variation in acquisition parameters can be addressed by the data fidelity component in MMPM-Net. Extensive experiments are performed on R2 mapping and R1 mapping datasets with substantial variations in acquisition settings, and the results demonstrate that the proposed MMPM-Net method outperforms other state-of-the-art MR parameter mapping methods both qualitatively and quantitatively.

A fully automatic parenchyma extraction method for MRI T2* relaxometry of iron loaded liver in transfusion-dependent patients.

PURPOSE: To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver. METHODS: A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation. Axial liver slices from 129 patients at 1.5 T were allocated to training (85%) and internal test (15%) sets. Two external test sets separately included 1.5 T data from 20 patients and 3.0 T data from 28 patients. The final T2* measurement was obtained by fitting the average signal of the extracted liver parenchyma. The agreement between T2* measurements using fully and semiautomatic parenchyma extraction methods was assessed using coefficient of variation (CoV) and Bland-Altman plots. RESULTS: Dice of the deep network-based liver segmentation was 0.970 ± 0.019 on the internal dataset, 0.960 ± 0.035 on the external 1.5 T dataset, and 0.958 ± 0.014 on the external 3.0 T dataset. The mean difference bias between T2* measurements of the fully and semiautomatic methods were separately 0.12 (95% CI: -0.37, 0.61) ms, 0.04 (95% CI: -1.0, 1.1) ms, and 0.01 (95% CI: -0.25, 0.23) ms on the three test datasets. The CoVs between the two methods were 4.2%, 4.8% and 2.0% on the internal test set and two external test sets. CONCLUSIONS: The developed fully automatic parenchyma extraction approach provides an efficient and operator-independent T2* measurement for assessing hepatic iron content in clinical practice.

MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia.

BACKGROUND: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability. PURPOSE: To develop a deep learning-based method for automatic septum segmentation from black-blood MR images for the myocardial T2* measurement of thalassemia patients. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: One hundred forty-six transfusion-dependent thalassemia patients with cardiac MR examinations from two centers. Data from Center 1 (1.5 T) were assigned to the training (100 examinations) and internal testing (20 examinations) sets; data from Center 2 were assigned to the external testing set (26 examinations; 10 at 1.5 T and 16 at 3.0 T). FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T, multiecho gradient-echo sequence. ASSESSMENT: A modified attention U-Net for septum segmentation was constructed and trained, and its performance evaluated on unseen internal and external datasets. T2* was measured by fitting the average septum signal, separately segmented by automatic and manual methods. STATISTICAL TESTS: Agreement between manual and automatic septum segmentations was assessed with the Dice coefficient, and T2* agreement was assessed using the Bland-Altman plot and the coefficient of variation (CoV). RESULTS: The median Dice coefficient of deep network-based septum segmentation was 0.90 [0.05] on the internal dataset, 0.82 [0.10] on the external 1.5 T dataset, and 0.86 [0.14] on the external 3.0 T dataset. T2* measurements using automatic segmentation corresponded with those from manual segmentation, with a mean difference of 0.02 (95% LoA: -0.74 to 0.79) msec, 0.43 (95% LoA: -2.1 to 3.0) msec, and 0.36 (95% LoA: -0.72 to 1.4) msec on the three datasets. The CoVs between the two methods were 3.1%, 7.0%, and 6.1% on the internal and two external datasets, respectively. DATA CONCLUSIONS: The proposed septum segmentation yielded myocardial T2* measurements which were highly consistent with those obtained by manual segmentation. This automatic approach may facilitate data processing and avoid operator-dependent variability in practice. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

Gene set enrichment analysis identifies immune subtypes of kidney renal clear cell carcinoma with significantly different molecular and clinical properties.

Background: Kidney renal clear cell carcinoma (KIRC) is the most prevalent renal malignancy, marked by a high abundance of tumor-infiltrating lymphocytes (TILs) and an unfavorable prognosis upon metastasis. Numerous studies have demonstrated that KIRC possesses a tumor microenvironment that is highly heterogeneous, and this is associated with significant variations in the effectiveness of most first-line drugs administered to KIRC patients. Therefore, it is crucial to classify KIRC based on the tumor microenvironment, although these subtyping techniques are still inadequate. Methods: By applying gene set enrichment scores of 28 immune signatures, we conducted a hierarchical clustering of KIRC and determined its immune subtypes. In addition, we conducted a comprehensive exploration of the molecular and clinical features of these subtypes, including survival prognosis, proliferation, stemness, angiogenesis, tumor microenvironment, genome instability, intratumor heterogeneity, and pathway enrichment. Results: Through cluster analysis, two immune subtypes of KIRC were identified and termed Immunity-High (Immunity-H) and Immunity-Low (Immunity-L). This clustering outcome was consistent in four independent KIRC cohorts. The subtype Immunity-H exhibited elevated levels of TILs, tumor aneuploidy, homologous recombination deficiency, stemness, and proliferation potential, along with a poorer prognosis for survival. Despite this, the Immunity-L subtype demonstrated elevated intratumor heterogeneity and a stronger angiogenesis signature in contrast to Immunity-H. According to the results of pathway enrichment analysis, the Immunity-H subtype was found to be highly enriched in immunological, oncogenic, and metabolic pathways, whereas the Immunity-L subtype was highly enriched in angiogenic, neuroactive ligand-receptor interaction, and PPAR pathways. Conclusions: Based on the enrichment of immune signatures in the tumor microenvironment, KIRC can be categorized into two immune subtypes. The two subtypes demonstrate considerably distinct molecular and clinical features. In KIRC, an increase in immune infiltration is linked to a poor prognosis. Patients with Immunity-H KIRC may exhibit active responses to PPAR and immune checkpoint inhibitors, whereas patients with Immunity-L may manifest favorable responses to anti-angiogenic agents and immune checkpoint inhibitors. The immunological classification provides molecular insights into KIRC immunity, as well as clinical implications for the management of this disease.

Research status and prospects of acupuncture for autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder and has a predilection for children. Its symptoms, such as lifelong social communication deficits and repetitive sensory-motor behaviors, put a huge burden on the patient's family and society. Currently, there is no cure for ASD, and some medications that can improve its symptoms are often accompanied by adverse effects. Among many complementary and alternative medicine (CAM) therapies, acupuncture has shown promising application potential, but after years of practice, it has not been recognized as the preferred CAM therapy for ASD. Therefore, we analyzed and discussed the clinical study reports of acupuncture in the treatment of ASD in the past 15 years from the aspects of study subjects, group setting, intervention modalities, acupoint selection, outcome evaluation, and safety. The data accumulated at present are not sufficient to support the clinical effectiveness of acupuncture in ASD and to justify its use in clinical practice. They provide, however, initial evidence of possible effectiveness and encourage further investigation in order to reach firm conclusions. Based on a comprehensive analysis, we believed that following the Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA) and Consolidated Standards of Reporting Trials (CONSORT), screening the optimal combination of acupoints applying a rigorous scientific study design, and performing the related functional experiments may be the effective way to convincingly test the hypothesis that acupuncture may be beneficial in ASD patients. The significance of this review is to provide a reference for researchers to carry out high-quality clinical trials of acupuncture in the treatment of ASD from the perspective of the combination of modern medicine and traditional Chinese medicine.

A Case of an 82-Year-Old Man with a Spinal Extradural Malignant Ossifying Fibromyxoid Tumor.

BACKGROUND Ossifying fibromyxoid tumor (OFMT) is a rare mesenchymal tumor predominantly involving the subcutaneous tissues or skeletal muscles in the proximal extremities, typically in middle-aged men. OFMT in the spine is extremely rare, with only 3 previously reported cases in the literature. CASE REPORT Here, we present a rare case of an 82-year-old man presenting with paresthesia of both arms and weakness of both legs, who underwent magnetic resonance imaging (MRI) of the spine, which showed an aggressive extradural tumor. Following surgical debulking, histology examination revealed a tumor of stromal origin with myxoid and ossifying components and pleomorphic features. Overall findings were suggestive of a malignant OFMT. The patient underwent postoperative adjuvant radiotherapy. However, the first follow-up MRI study at 8 months showed residual tumor, which also demonstrated avid tracer uptake on technetium-99m scintigraphy and PET-CT scans. The second MRI follow-up about 9 months later showed several metastatic foci along the craniospinal axis. Despite subsequent resection of the spinal metastasis, the patient eventually died of sepsis about 21 months after the initial tumor diagnosis. CONCLUSIONS We presented a case of extradural spinal malignant OFMT and highlighted the difficulty distinguishing this rare primary tumor from spinal metastases. In this case, MRI signal intensities and identifying intratumoral bone formation, combined with histopathology following surgical resection, confirmed the diagnosis. This case has also shown the importance of follow-up by a multidisciplinary team to monitor for the recurrence of primary OFMT.

Quorum sensing system effectively enhances DegU-mediated degradation of pyrethroids by Bacillus subtilis.

The contamination of the natural environment is a growing concern that threatens all life forms, including microorganisms. Bacteria protect themselves by initiating quorum sensing (QS), a bacterial cell-cell communication, to generate adaptive responses to these pollutants. Bacillus subtilis has a typical QS ComQXPA system that regulates the phosphorylation of the transcription factor DegU (DegU-P), and thus can mediate the expression of various downstream genes under different stress conditions. Herein, we found that cesB, a gene of Bacillus subtilis 168, plays a key role in pyrethroid degradation, and cesB-mediated degradation could be enhanced by coordinating with the ComX communication system. Using ß-cypermethrin (ß-CP) as a paradigm, we demonstrated that DegU-P increased upon exposure to ß-CP, thus facilitating ß-CP degradation by binding to the upstream regulatory regions of cesB, leading to the activation of the expression of cesB. Further, we showed that the expression of different levels of phosphorylated DegU in a degU deletion strain resulted in varying degrees of ß-CP degradation efficiency, with phosphorylated DegUH12L achieving 78.39% degradation efficiency on the first day, surpassing the 56.27% degradation efficiency in the wild type strain. Consequently, based on the conserved regulatory mechanism of ComQXPA system, we propose that DegU-P-dependent regulation serves as a conserved defense mechanism owing to its ability to fine-tune the expression of genes involved in the degradation of pollutants upon exposure to different pesticides.

Molecular Mechanisms of Tebuconazole Affecting the Social Behavior and Reproduction of Zebrafish.

The aim of this study was to explore the underlying mechanism of adverse effects caused by tebuconazole (TEB) on the reproduction of aquatic organisms In the present study, in order to explore the effects of TEB on reproduction, four-month-old zebrafish were exposed to TEB (0, DMSO, 0.4 mg/L, 0.8 mg/L, and 1.6 mg/L) for 21 days. After exposure, the accumulations of TEB in gonads were observed and the cumulative egg production was evidently decreased. The decline of fertilization rate in F1 embryos was also observed. Then the changes in sperm motility and histomorphology of gonads were discovered, evaluating that TEB had adverse effects on gonadal development. Additionally, we also found the alternations of social behavior, 17ß-estradiol (E2) level, and testosterone (T) level. Furthermore, the expression levels of genes involved in the hypothalamic-pituitary-gonadal (HPG) axis and social behavior were remarkably altered. Taken together, it could be concluded that TEB affected the egg production and fertilization rate by interfering with gonadal development, sex hormone secretion, and social behavior, which were eventually attributed to the disruption of the expressions of genes associated with the HPG axis and social behavior. This study provides a new perspective to understanding the mechanism of TEB-induced reproductive toxicity.

Cascade of Denoising and Mapping Neural Networks for MRI R2* Relaxometry of Iron-Loaded Liver.

MRI of effective transverse relaxation rate (R2*) measurement is a reliable method for liver iron concentration quantification. However, R2* mapping can be degraded by noise, especially in the case of iron overload. This study aimed to develop a deep learning method for MRI R2* relaxometry of an iron-loaded liver using a two-stage cascaded neural network. The proposed method, named CadamNet, combines two convolutional neural networks separately designed for image denoising and parameter mapping into a cascade framework, and the physics-based R2* decay model was incorporated in training the mapping network to enforce data consistency further. CadamNet was trained using simulated liver data with Rician noise, which was constructed from clinical liver data. The performance of CadamNet was quantitatively evaluated on simulated data with varying noise levels as well as clinical liver data and compared with the single-stage parameter mapping network (MappingNet) and two conventional model-based R2* mapping methods. CadamNet consistently achieved high-quality R2* maps and outperformed MappingNet at varying noise levels. Compared with conventional R2* mapping methods, CadamNet yielded R2* maps with lower errors, higher quality, and substantially increased efficiency. In conclusion, the proposed CadamNet enables accurate and efficient iron-loaded liver R2* mapping, especially in the presence of severe noise.

Efficacy of Tai Chi on lower limb function of Parkinson's disease patients: A systematic review and meta-analysis.

Background: At present, the effect of Tai Chi (TC) on lower limb function in patients with Parkinson's disease (PD) is controversial. Therefore, we conducted a meta-analysis on the influence of TC on lower limb function in PD patients. Methods: According to the PRISMA guidelines, seven databases were searched. Randomized controlled trials (RCTS) were selected and screened according to inclusion and exclusion criteria. We assessed the quality of the studies using the Cochrane Risk of Bias tool and then extracted the characteristics of the included studies. The random effect model was adopted, and heterogeneity was measured by I 2 statistic. Results: A total of 441 articles were screened, and 10 high-quality RCTs were with a total of 532 patients with PD met Our inclusion criteria. Meta-analysis showed that compared To control groups TC improved several outcomes. TC significantly improved motor function (SMD = -0.70; 95% CI = -0.95, -0.45; p < 0.001; I 2 = 35%), although The results were not statistically significant for The subgroup analysis of TC duration (SMD = -0.70; 95% CI = -0.95, -0.45; p = 0.88; I 2 = 0%;). TC significantly improved balance function (SMD = 0.89; 95% CI = 0.51, 1.27; p < 0.001; I 2 = 54%), functional walking capacity (SMD = -1.24; 95% CI = -2.40, -0.09; p = 0.04; I 2 = 95%), and gait velocity (SMD = 0.48; 95% CI = -0.02, 0.94; p = 0.04; I 2 = 78%), But Did Not improve endurance (SMD = 0.31; 95% CI = -0.12, 0.75; p = 0.16; I 2 = 0%), step length (SMD = 0.01; 95% CI = -0.34, 0.37; p = 0.94; I 2 = 29%), and cadence (SMD = 0.06; 95% CI = -0.25, 0.36; p = 0.70; I 2 = 0%). Conclusion: TC has beneficial effects on motor function, balance function, functional walking ability, and gait velocity, but does not improve walking endurance, stride length, and cadence.

Identifying Immune-Specific Subtypes of Adrenocortical Carcinoma Based on Immunogenomic Profiling.

BACKGROUND: The tumor immune microenvironment (TIME) of adrenocortical carcinoma (ACC) is heterogeneous. However, a classification of ACC based on the TIME remains unexplored. METHODS: We hierarchically clustered ACC based on the enrichment levels of twenty-three immune signatures to identify its immune-specific subtypes. Furthermore, we comprehensively compared the clinical and molecular profiles between the subtypes. RESULTS: We identified two immune-specific subtypes of ACC: Immunity-H and Immunity-L, which had high and low immune signature scores, respectively. We demonstrated that this subtyping method was stable and reproducible by analyzing five different ACC cohorts. Compared with Immunity-H, Immunity-L had lower levels of immune cell infiltration, worse overall and disease-free survival prognosis, and higher tumor stemness, genomic instability, proliferation potential, and intratumor heterogeneity. Furthermore, the ACC driver gene CTNNB1 was more frequently mutated in Immunity-L than in Immunity-H. Several proteins, such as mTOR, ERCC1, Akt, ACC1, Cyclin_E1, ß-catenin, FASN, and GAPDH, were more highly expressed in Immunity-L than in Immunity-H. In contrast, p53, Syk, Lck, PREX1, and MAPK were more highly expressed in Immunity-H. Pathway and gene ontology analysis showed that the immune, stromal, and apoptosis pathways were highly enriched in Immunity-H, while the cell cycle, steroid biosynthesis, and DNA damage repair pathways were highly enriched in Immunity-L. CONCLUSIONS: ACC can be classified into two stable immune-related subtypes, which have significantly different antitumor responses, molecular characteristics, and clinical outcomes. This subtyping may provide clinical implications for prognostic and immunotherapeutic stratification of ACC.

ATAXIC: An Algorithm to Quantify Transcriptomic Perturbation Heterogeneity in Single Cancer Cells.

The single-cell RNA sequencing (scRNA-seq) has recently been widely utilized to quantify transcriptomic profiles in single cells of bulk tumors. The transcriptomic profiles in single cells facilitate the investigation of intratumor heterogeneity that is unlikely confounded by the nontumor components. We proposed an algorithm (ATAXIC) to quantify the heterogeneity of transcriptomic perturbations (TPs) in single cancer cells. ATAXIC calculated the TP heterogeneity level of a single cell based on the standard deviations of the absolute z-scored gene expression values for tens of thousands of genes, reflecting the asynchronous degree of transcriptomic alterations relative to the central (mean) tendency. By analyzing scRNA-seq datasets for eight cancer types, we revealed that ATAXIC scores were likely to correlate positively with the enrichment scores of various proliferation and oncogenic signatures, DNA damage repair, treatment resistance, and unfavorable phenotypes and outcomes in cancer. The ATAXIC scores varied among different cancer types, with lung cancer and melanoma having the lowest average scores and clear cell renal cell carcinoma having the highest average scores. The low TP heterogeneity in lung cancer and melanoma could bestow relatively higher response rates to immune checkpoint inhibitors on both cancer types. In conclusion, ATAXIC is a useful algorithm to quantify the TP heterogeneity in single cancer cells, as well as providing new insights into tumor biology.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics.

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

ALB, HP, OAF and RBP4 as novel protein biomarkers for identifying cured patients with pulmonary tuberculosis by DIA.

BACKGROUND: Pulmonary tuberculosis (TB) is a serious infectious disease that lacks robust blood-based biomarkers to identify cured TB. Some discharged patients are not fully cured and may relapse or even develop multidrug-resistant TB. This study is committed to finding proteomic-based plasma biomarkers to support establishing laboratory standards for clinical TB cure. METHODS: Data-independent acquisition (DIA) was used to obtain the plasma protein expression profiles of TB patients at different treatment stages compared with healthy controls. Multivariate statistical methods and bioinformatics were used to analyze the data. RESULTS: Bioinformatic analysis suggests coagulation dysfunction and vitamin and lipid metabolism disturbances in TB. Albumin (ALB), haptoglobin (HP), out at first protein homolog (OAF), and retinol-binding protein 4 (RBP4) can be used to establish a diagnostic model for the efficacy evaluation of TB with an area under the curve of 0.963, which could effectively distinguish untreated TB patients from cured patients. CONCLUSIONS: Our research demonstrated that ALB, HP, OAF and RBP4 can be potential biomarkers for evaluating the efficacy of TB. These findings may provide experimental data for establishing the laboratory indicators of clinical TB cure and providing clinicians with new targets for exploring the underlying mechanisms of TB pathogenesis.

Novel Plasma Proteomic Biomarkers for Early Identification of Induction Chemotherapy Beneficiaries in Locoregionally Advanced Nasopharyngeal Carcinoma.

Background: Induction chemotherapy (IC) can alleviate locoregionally advanced nasopharyngeal carcinoma (LA-NPC), but effectiveness differs between patients, toxicity is problematic, and effective blood-based IC efficacy predictors are lacking. Here, we aimed to identify biomarkers for early identification of IC beneficiaries. Methods: Sixty-four pairs of matched plasma samples collected before and after IC from LA-NPC patients including 34 responders and 30 non-responders, as well as 50 plasma samples of healthy individuals, were tested using data-independent acquisition mass spectrometry. The proteins associated with clinical traits or IC benefits were investigated by weighted gene co-expression network analysis (WGCNA) and soft cluster analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional annotations were performed to determine the potential function of the identified proteins. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of candidate biomarkers in predicting IC beneficiaries. Results: Compared with healthy individuals, 1027 differentially expressed proteins (DEPs) were found in the plasma of LA-NPC patients. Based on feedback from IC outcomes, 463 DEPs were identified in the pre-IC plasma between responders and non-responders. A total of 1212 DEPs represented the proteomic changes before and after IC in responders, while 276 DEPs were identified in post-IC plasma between responders and non-responders. WGCNA identified nine protein co-expression modules correlated with clinical traits. Soft cluster analysis identified four IC benefits-related protein clusters. Functional enrichment analysis showed that these proteins may play a role in IC via immunity, complement, coagulation, glycosaminoglycan and serine. Four proteins differentially expressed in all group comparisons, paraoxonase/arylesterase 1 (PON1), insulin-like growth factor-binding protein 3 (IGFBP-3), rheumatoid factor D5 light chain (v-kappa-3) and RNA helicase (DDX55), were associated with clinical traits or IC benefits. A four-protein model accurately identified potential IC beneficiaries (AUC=0.95) while diagnosing LA-NPC (AUC=0.92), and the prediction performance was verified using the models to confirm the effective IC (AUC=0.97) and evaluate IC outcome (AUC=0.94). Conclusion: The plasma protein profiles among IC responders and non-responders were different. PON1, IGFBP3, v-kappa-3 and DDX55 could serve as potential biomarkers for early identification of IC beneficiaries for individualised treatment of LA-NPC.

Plasma Quantitative Lipid Profiles: Identification of CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1) as Novel Biomarkers for Pre-warning and Prognosis in Acute Myocardial Infarction.

This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.