A novel single-cell model reveals ferroptosis-associated biomarkers for individualized therapy and prognostic prediction in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a pressing need for improved therapeutic response and prognosis prediction. This study delves into a novel predictive model related to ferroptosis, a regulated cell death mechanism disrupting metabolic processes. RESULTS: Single-cell sequencing data analysis identified subpopulations of HCC cells exhibiting activated ferroptosis and distinct gene expression patterns compared to normal tissues. Utilizing the LASSO-Cox algorithm, we constructed a model with 10 single-cell biomarkers associated with ferroptosis, namely STMN1, S100A10, FABP5, CAPG, RGCC, ENO1, ANXA5, UTRN, CXCR3, and ITM2A. Comprehensive analyses using these biomarkers revealed variations in immune infiltration, tumor mutation burden, drug sensitivity, and biological functional profiles between risk groups. Specific associations were established between particular immune cell subtypes and certain gene expression patterns. Treatment response analyses indicated potential benefits from anti-tumor immune therapy for the low-risk group and chemotherapy advantages for the high-risk group. CONCLUSIONS: The integration of this single-cell level model with clinicopathological features enabled accurate overall survival prediction and effective risk stratification in HCC patients. Our findings illuminate the potential of ferroptosis-related genes in tailoring therapy and prognosis prediction for HCC, offering novel insights into the intricate interplay among ferroptosis, immune response, and HCC progression.

How Grammar Conveys Meaning: Language-Specific Spatial Encoding Patterns and Cross-Language Commonality in Higher-Order Neural Space.

Languages come in different forms but have shared meanings to convey. Some meanings are expressed by sentence structure and morphologic inflections rather than content words, such as indicating time frame using tense. This fMRI study investigates whether there is cross-language common representation of grammatical meanings that can be identified from neural signatures in the bilingual human brain. Based on the representations in intersentence neural similarity space, identifying grammatical construction of a sentence in one language by models trained on the other language resulted in reliable accuracy. By contrast, cross-language identification of grammatical construction by spatially matched activation patterns was only marginally accurate. Brain locations representing grammatical meaning in the two languages were interleaved in common regions bilaterally. The locations of voxels representing grammatical features in the second language were more varied across individuals than voxels representing the first language. These findings suggest grammatical meaning is represented by language-specific activation patterns, which is different from lexical semantics. Commonality of grammatical meaning is neurally reflected only in the interstimulus similarity space.SIGNIFICANCE STATEMENT Whether human brain encodes sentence-level meanings beyond content words in different languages similarly has been a long-standing question. We characterize the neural representations of similar grammatical meanings in different languages. Using complementary analytic approaches on fMRI data, we show that the same grammatical meaning is neurally represented as the common pattern of neural distances between sentences. The results suggest the possibility of identifying specific grammatical meaning expressed by different morphologic and syntactic implementations of different languages. The neural realization of grammatical meanings is constrained by the specific language being used, but the relationships between the neural representations of sentences are preserved across languages. These findings have some theoretical implications on a distinction between grammar and lexical meanings.

Genome-wide Association Study of Exercise-induced Skeletal Muscle Hypertrophy and the Construction of Predictive Model.

To investigate inter-individual differences in muscle thickness of Rectus Femoris (MTRF) following 12 weeks of Resistance Training (RT) or High-Intensity Interval Training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conducted musculoskeletal ultrasound assessments of the MTRF response in 440 physically inactive adults after the 12-week exercise period. A Genome-wide Association study (GWAS) was employed to identify variants associated with MTRF response, separately for RT and HIIT. Utilizing polygenic predictor score (PPS), we estimated the genetic contribution to exercise-induced hypertrophy. Predictive models for MTRF response were constructed using Random Forest (RF), Support Vector Mac (SVM), and Generalized Linear Model (GLM) in 10 cross-validated approach. MTRF increased significantly after both RT (8.8%, P<0.05) and HIIT (5.3%, P<0.05), but with considerable inter-individual differences (RT: -13.5~38.4%, HIIT: -14.2%~30.7%). Eleven lead SNPs in RT and eight lead SNPs in HIIT were identified at a significance level of P<1×10-5. The PPS was associated with MTRF response, explaining 47.2% of the variation in response to RT and 38.3% of the variation in response to HIIT. Notably, the GLM and SVM predictive models exhibited superior performance in comparison to RF models (p<0.05), and the GLM demonstrated optimal performance with an AUC of 0.809 (95%CI:0.669-0.949). Factors such as PPS, baseline MTRF, and exercise protocol exerted influence on the MTRF response to exercise, with PPS being the primary contributor. The GLM and SVM predictive model, incorporating both genetic and phenotypic factors, emerged as promising tools for predicting exercise-induced skeletal muscle hypertrophy.

Identifying individual's distractor suppression using functional connectivity between anatomical large-scale brain regions.

Distractor suppression (DS) is crucial in goal-oriented behaviors, referring to the ability to suppress irrelevant information. Current evidence points to the prefrontal cortex as an origin region of DS, while subcortical, occipital, and temporal regions are also implicated. The present study aimed to examine the contribution of communications between these brain regions to visual DS. To do it, we recruited two independent cohorts of participants for the study. One cohort participated in a visual search experiment where a salient distractor triggering distractor suppression to measure their DS and the other cohort filled out a Cognitive Failure Questionnaire to assess distractibility in daily life. Both cohorts collected resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate function connectivity (FC) underlying DS. First, we generated predictive models of the DS measured in visual search task using resting-state functional connectivity between large anatomical regions. It turned out that the models could successfully predict individual's DS, indicated by a significant correlation between the actual and predicted DS (r = 0.32, p < 0.01). Importantly, Prefrontal-Temporal, Insula-Limbic and Parietal-Occipital connections contributed to the prediction model. Furthermore, the model could also predict individual's daily distractibility in the other independent cohort (r = -0.34, p < 0.05). Our findings showed the efficiency of the predictive models of distractor suppression encompassing connections between large anatomical regions and highlighted the importance of the communications between attention-related and visual information processing regions in distractor suppression. Current findings may potentially provide neurobiological markers of visual distractor suppression.

Multicancer screening test based on the detection of circulating non haematological proliferating atypical cells.

BACKGROUND: the problem in early diagnosis of sporadic cancer is understanding the individual's risk to develop disease. In response to this need, global scientific research is focusing on developing predictive models based on non-invasive screening tests. A tentative solution to the problem may be a cancer screening blood-based test able to discover those cell requirements triggering subclinical and clinical onset latency, at the stage when the cell disorder, i.e. atypical epithelial hyperplasia, is still in a subclinical stage of proliferative dysregulation. METHODS: a well-established procedure to identify proliferating circulating tumor cells was deployed to measure the cell proliferation of circulating non-haematological cells which may suggest tumor pathology. Moreover, the data collected were processed by a supervised machine learning model to make the prediction. RESULTS: the developed test combining circulating non-haematological cell proliferation data and artificial intelligence shows 98.8% of accuracy, 100% sensitivity, and 95% specificity. CONCLUSION: this proof of concept study demonstrates that integration of innovative non invasive methods and predictive-models can be decisive in assessing the health status of an individual, and achieve cutting-edge results in cancer prevention and management.

Development and Validation of a Scoring System to Predict Response to Obeticholic Acid in Primary Biliary Cholangitis.

BACKGROUND & AIMS: Obeticholic acid (OCA) is the only licensed second-line therapy for primary biliary cholangitis (PBC). With novel therapeutics in advanced development, clinical tools are needed to tailor the treatment algorithm. We aimed to derive and externally validate the OCA response score (ORS) for predicting the response probability of individuals with PBC to OCA. METHODS: We used data from the Italian RECAPITULATE (N = 441) and the IBER-PBC (N = 244) OCA real-world prospective cohorts to derive/validate a score including widely available variables obtained either pre-treatment (ORS) or also after 6 months of treatment (ORS+). Multivariable Cox regressions with backward selection were applied to obtain parsimonious predictive models. The predicted outcomes were biochemical response according to POISE (alkaline phosphatase [ALP]/upper limit of normal [ULN]<1.67 with a reduction of at least 15%, and normal bilirubin), or ALP/ULN<1.67, or Normal range criteria (NR: normal ALP, alanine aminotransferase [ALT], and bilirubin) up to 24 months. RESULTS: Depending on the response criteria, ORS included age, pruritus, cirrhosis, ALP/ULN, ALT/ULN, GGT/ULN, and bilirubin. ORS+ also included ALP/ULN and bilirubin after 6 months of OCA therapy. Internally validated c-statistics for ORS were 0.75, 0.78, and 0.72 for POISE, ALP/ULN<1.67, and NR response, which raised to 0.83, 0.88, and 0.81 with ORS+, respectively. The respective performances in validation were 0.70, 0.72, and 0.71 for ORS and 0.80, 0.84, and 0.78 for ORS+. Results were consistent across groups with mild/severe disease. CONCLUSIONS: We developed and externally validated a scoring system capable to predict OCA response according to different criteria. This tool will enhance a stratified second-line therapy model to streamline standard care and trial delivery in PBC.

Development of an MRI-Based Prediction Model for Anti-TNF Treatment Failure in Perianal Crohn's Disease: A Multicenter Study.

BACKGROUND & AIMS: Clinical and radiologic variables associated with perianal fistula (PAF) outcomes are poorly understood. We developed prediction models for anti-tumor necrosis factor (TNF) treatment failure in patients with Crohn's disease-related PAF. METHODS: In a multicenter retrospective study between 2005 and 2022 we included biologic-naive adults (>17 years) who initiated their first anti-TNF therapy for PAF after pelvic magnetic resonance imaging (MRI). Pretreatment MRI studies were prospectively reread centrally by blinded radiologists. We developed and internally validated a prediction model based on clinical and radiologic parameters to predict the likelihood of anti-TNF treatment failure, clinically, at 6 months. We compared our model and a simplified version of MRI parameters alone with existing imaging-based PAF activity indices (MAGNIFI-CD and modified Van Assche MRI scores) by De Long statistical test. RESULTS: We included 221 patients: 32 ± 14 years, 60% males, 76% complex fistulas; 68% treated with infliximab and 32% treated with adalimumab. Treatment failure occurred in 102 (46%) patients. Our prediction model included age at PAF diagnosis, time to initiate anti-TNF treatment, and smoking and 8 MRI characteristics (supra/extrasphincteric anatomy, fistula length >4.3 cm, primary tracts >1, secondary tracts >1, external openings >1, tract hyperintensity on T1-weighted imaging, horseshoe anatomy, and collections >1.3 cm). Our full and simplified MRI models had fair discriminatory capacity for anti-TNF treatment failure (concordance statistic, 0.67 and 0.65, respectively) and outperformed MAGNIFI-CD (P = .002 and < .0005) and modified Van Assche MRI scores (P < .0001 and < .0001), respectively. CONCLUSIONS: Our risk prediction models consisting of clinical and/or radiologic variables accurately predict treatment failure in patients with PAF.

A Machine Learning Algorithm Avoids Unnecessary Paracentesis for Exclusion of SBP in Cirrhosis in Resource-Limited Settings.

BACKGROUND AND AIMS: Despite the poor prognosis associated with missed or delayed spontaneous bacterial peritonitis (SBP) diagnosis, <15% get timely paracentesis, which persists despite guidelines/education in the US. Measures to exclude SBP non-invasively where timely paracentesis cannot be performed could streamline this burden. METHODS: Using Veterans Health Administration Corporate Data Warehouse (VHA-CDW) we included cirrhosis patients between 2009-2019 who underwent timely paracentesis and collected relevant clinical information (demographics, cirrhosis severity, medications, vitals, and comorbidities). XGBoost-models were trained on 75% of the primary cohort, with 25% reserved for testing. The final model was further validated in two cohorts: Validation cohort #1: In VHA-CDW, those without prior SBP who received 2nd early paracentesis, and Validation cohort #2: Prospective data from 276 non-electively admitted University hospital patients. RESULTS: Negative predictive values (NPV) at 5,10 & 15% probability cutoffs were examined. Primary cohort: n=9,643 (mean age 63.1±8.7 years, 97.2% men, SBP:15.0%) received first early paracentesis. Testing-set NPVs for SBP were 96.5%, 93.0% and 91.6% at the 5%, 10% and 15% probability thresholds respectively. In Validation cohort #1: n=2844 (mean age 63.14±8.37 years, 97.1% male, SBP: 9.7%) with NPVs were 98.8%, 95.3% and 94.5%. In Validation cohort #2: n=276 (mean age 56.08±9.09, 59.6% male, SBP: 7.6%) with NPVs were 100%, 98.9% and 98.0% The final ML model showed the greatest net benefit on decision-curve analyses. CONCLUSIONS: A machine learning model generated using routinely collected variables excluded SBP with high negative predictive value. Applying this model could ease the need to provide paracentesis in resource-limited settings by excluding those unlikely to have SBP.

Generation and External Validation of a Histologic Transformation Risk Model for Patients with Follicular Lymphoma.

Follicular lymphoma (FL) is the most frequent indolent lymphoma. Some patients (10%-15%) experience histologic transformation (HT) to a more aggressive lymphoma, usually diffuse large B-cell lymphoma (DLBCL). This study aimed to validate and improve a genetic risk model to predict HT at diagnosis.We collected mutational data from diagnosis biopsies of 64 FL patients. We combined them with the data from a previously published cohort (total n = 104; 62 from nontransformed and 42 from patients who did transform to DLBCL). This combined cohort was used to develop a nomogram to estimate the risk of HT. Prognostic mutated genes and clinical variables were assessed using Cox regression analysis to generate a risk model. The model was internally validated by bootstrapping and externally validated in an independent cohort. Its performance was evaluated using a concordance index and a calibration curve. The clinicogenetic nomogram included the mutational status of 3 genes (HIST1HE1, KMT2D, and TNFSR14) and high-risk Follicular Lymphoma International Prognostic Index and predicted HT with a concordance index of 0.746. Patients were classified as being at low or high risk of transformation. The probability HT function at 24 months was 0.90 in the low-risk group vs 0.51 in the high-risk group and, at 60 months, 0.71 vs 0.15, respectively. In the external validation cohort, the probability HT function in the low-risk group was 0.86 vs 0.54 in the high-risk group at 24 months, and 0.71 vs 0.32 at 60 months. The concordance index in the external cohort was 0.552. In conclusion, we propose a clinicogenetic risk model to predict FL HT to DLBLC, combining genetic alterations in HIST1H1E, KMT2D, and TNFRSF14 genes and clinical features (Follicular Lymphoma International Prognostic Index) at diagnosis. This model could improve the management of FL patients and allow treatment strategies that would prevent or delay transformation.

Development and validation of predictive models for myopia onset and progression using extensive 15-year refractive data in children and adolescents.

BACKGROUND: Global myopia prevalence poses a substantial public health burden with vision-threatening complications, necessitating effective prevention and control strategies. Precise prediction of spherical equivalent (SE), myopia, and high myopia onset is vital for proactive clinical interventions. METHODS: We reviewed electronic medical records of pediatric and adolescent patients who underwent cycloplegic refraction measurements at the Eye & Ear, Nose, and Throat Hospital of Fudan University between January 2005 and December 2019. Patients aged 3-18 years who met the inclusion criteria were enrolled in this study. To predict the SE and onset of myopia and high myopia in a specific year, two distinct models, random forest (RF) and the gradient boosted tree algorithm (XGBoost), were trained and validated based on variables such as age at baseline, and SE at various intervals. Outputs included SE, the onset of myopia, and high myopia up to 15 years post-initial examination. Age-stratified analyses and feature importance assessments were conducted to augment the clinical significance of the models. RESULTS: The study enrolled 88,250 individuals with 408,255 refraction records. The XGBoost-based SE prediction model consistently demonstrated robust and better performance than RF over 15 years, maintaining an R2 exceeding 0.729, and a Mean Absolute Error ranging from 0.078 to 1.802 in the test set. Myopia onset prediction exhibited strong area under the curve (AUC) values between 0.845 and 0.953 over 15 years, and high myopia onset prediction showed robust AUC values (0.807-0.997 over 13 years, with the 14th year at 0.765), emphasizing the models' effectiveness across age groups and temporal dimensions on the test set. Additionally, our classification models exhibited excellent calibration, as evidenced by consistently low brier score values, all falling below 0.25. Moreover, our findings underscore the importance of commencing regular examinations at an early age to predict high myopia. CONCLUSIONS: The XGBoost predictive models exhibited high accuracy in predicting SE, onset of myopia, and high myopia among children and adolescents aged 3-18 years. Our findings emphasize the importance of early and regular examinations at a young age for predicting high myopia, thereby providing valuable insights for clinical practice.

Clinical data mining: challenges, opportunities, and recommendations for translational applications.

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Enhanced predictive validity of integrative models for refractory hyperthyroidism considering baseline and early therapy characteristics: a prospective cohort study.

BACKGROUND: A subset of Graves' disease (GD) patients develops refractory hyperthyroidism, posing challenges in treatment decisions. The predictive value of baseline characteristics and early therapy indicators in identifying high risk individuals is an area worth exploration. METHODS: A prospective cohort study (2018-2022) involved 597 newly diagnosed adult GD patients undergoing methimazole (MMI) treatment. Baseline characteristics and 3-month therapy parameters were utilized to develop predictive models for refractory GD, considering antithyroid drug (ATD) dosage regimens. RESULTS: Among 346 patients analyzed, 49.7% developed ATD-refractory GD, marked by recurrence and sustained Thyrotropin Receptor Antibody (TRAb) positivity. Key baseline factors, including younger age, Graves' ophthalmopathy (GO), larger goiter size, and higher initial free triiodothyronine (fT3), free thyroxine (fT4), and TRAb levels, were all significantly associated with an increased risk of refractory GD, forming the baseline predictive model (Model A). Subsequent analysis based on MMI cumulative dosage at 3 months resulted in two subgroups: a high cumulative dosage group (average ≥ 20 mg/day) and a medium-low cumulative dosage group (average < 20 mg/day). Absolute values, percentage changes, and cumulative values of thyroid function and autoantibodies at 3 months were analyzed. Two combined predictive models, Model B (high cumulative dosage) and Model C (medium-low cumulative dosage), were developed based on stepwise regression and multivariate analysis, incorporating additional 3-month parameters beyond the baseline. In both groups, these combined models outperformed the baseline model in terms of discriminative ability (measured by AUC), concordance with actual outcomes (66.2% comprehensive improvement), and risk classification accuracy (especially for Class I and II patients with baseline predictive risk < 71%). The reliability of the above models was confirmed through additional analysis using random forests. This study also explored ATD dosage regimens, revealing differences in refractory outcomes between predicted risk groups. However, adjusting MMI dosage after early risk assessment did not conclusively improve the prognosis of refractory GD. CONCLUSION: Integrating baseline and early therapy characteristics enhances the predictive capability for refractory GD outcomes. The study provides valuable insights into refining risk assessment and guiding personalized treatment decisions for GD patients.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

A multiparametric risk table for loss of clinical remission status in patients with rheumatoid arthritis: A starter study post-hoc analysis.

OBJECTIVE: This post-hoc analysis was carried out on data acquired in the longitudinal Sonographic Tenosynovitis/arthritis Assessment in Rheumatoid Arthritis Patients in Remission (STARTER) study. Its primary aim was to determine the predictive clinical and MSUS features factors for disease flare in RA patients in clinical remission, whilst its secondary aim was to evaluate the probability of disease flare based on clinical and MSUS features. METHODS: The analysis included a total of 389 RA patients in DAS28-defined remission. All patients underwent a MSUS examination according to OMERACT guidelines. Logistic regression and results presented as OR and 95%CI were used for the evaluation of the association between selected variables and disease flare. Significant clinical and MSUS features were incorporated into a risk table to predict disease flare within 12 months in RA remission patients. RESULTS: Within 12 months, 137(35%) RA patients experienced a disease flare. RA patients who experienced a flare disease differed from persistent remission for ACPA positivity (75.9%vs62.3%; p= 0.007), percentage of sustained clinical remission at baseline (44.1%vs68.5%; p= 0.001) and synovium PD signal presence (58.4%vs33.3%; p< 0.001). Based on these results, the three features were considered in a predictive model of disease flare with adjOR 3.064(95%CI 1.728-5.432). Finally, a risk table was constructed including the three significant predictive factors of disease flare within 12 months from the enrolment. CONCLUSION: An adaptive flare prediction model tool, based on data available in outpatient setting, were developed as a multiparametric risk table. If confirmed by the external validation, this tool might support the definition of therapeutic strategies in RA patients in DAS28-defined remission status.

Predictive model for CAR-T cell therapy success in patients with relapsed/refractory B-cell acute lymphoblastic leukaemia.

Chimeric antigen receptor T-cell (CAR-T) therapy has demonstrated remarkable efficacy in treating relapsed/refractory acute B-cell lymphoblastic leukaemia (R/R B-ALL). However, a subset of patients does not benefit from CAR-T therapy. Our study aims to identify predictive indicators and establish a model to evaluate the feasibility of CAR-T therapy. Fifty-five R/R B-ALL patients and 22 healthy donors were enrolled. Peripheral blood lymphocyte subsets were analysed using flow cytometry. Sensitivity, specificity, accuracy, positive and negative predictive values and receiver operating characteristic (ROC) areas under the curve (AUC) were determined to evaluate the predictive values of the indicators. We identified B lymphocyte, regulatory T cell (Treg) and peripheral blood minimal residual leukaemia cells (B-MRD) as indicators for predicting the success of CAR-T cell preparation with AUC 0.936, 0.857 and 0.914. Furthermore, a model based on CD3+ T count, CD4+ T/CD8+ T ratio, Treg and extramedullary diseases (EMD) was used to predict the response to CAR-T therapy with AUC of 0.938. Notably, a model based on CD4+ T/CD8+ T ratio, B, Treg and EMD were used in predicting the success of CAR-T therapy with AUC 0.966 [0.908-1.000], with specificity (92.59%) and sensitivity (91.67%). In the validated group, the predictive model predicted the success of CAR-T therapy with specificity (90.91%) and sensitivity (100%). We have identified several predictive indicators for CAR-T cell therapy success and a model has demonstrated robust predictive capacity for the success of CAR-T therapy. These results show great potential for guiding informed clinical decisions in the field of CAR-T cell therapy.

A single-center, retrospective study of hospitalized patients with lower respiratory tract infections: clinical assessment of metagenomic next-generation sequencing and identification of risk factors in patients.

INTRODUCTION: Lower respiratory tract infections(LRTIs) in adults are complicated by diverse pathogens that challenge traditional detection methods, which are often slow and insensitive. Metagenomic next-generation sequencing (mNGS) offers a comprehensive, high-throughput, and unbiased approach to pathogen identification. This retrospective study evaluates the diagnostic efficacy of mNGS compared to conventional microbiological testing (CMT) in LRTIs, aiming to enhance detection accuracy and enable early clinical prediction. METHODS: In our retrospective single-center analysis, 451 patients with suspected LRTIs underwent mNGS testing from July 2020 to July 2023. We assessed the pathogen spectrum and compared the diagnostic efficacy of mNGS to CMT, with clinical comprehensive diagnosis serving as the reference standard. The study analyzed mNGS performance in lung tissue biopsies and bronchoalveolar lavage fluid (BALF) from cases suspected of lung infection. Patients were stratified into two groups based on clinical outcomes (improvement or mortality), and we compared clinical data and conventional laboratory indices between groups. A predictive model and nomogram for the prognosis of LRTIs were constructed using univariate followed by multivariate logistic regression, with model predictive accuracy evaluated by the area under the ROC curve (AUC). RESULTS: (1) Comparative Analysis of mNGS versus CMT: In a comprehensive analysis of 510 specimens, where 59 cases were concurrently collected from lung tissue biopsies and BALF, the study highlights the diagnostic superiority of mNGS over CMT. Specifically, mNGS demonstrated significantly higher sensitivity and specificity in BALF samples (82.86% vs. 44.42% and 52.00% vs. 21.05%, respectively, p < 0.001) alongside greater positive and negative predictive values (96.71% vs. 79.55% and 15.12% vs. 5.19%, respectively, p < 0.01). Additionally, when comparing simultaneous testing of lung tissue biopsies and BALF, mNGS showed enhanced sensitivity in BALF (84.21% vs. 57.41%), whereas lung tissues offered higher specificity (80.00% vs. 50.00%). (2) Analysis of Infectious Species in Patients from This Study: The study also notes a concerning incidence of lung abscesses and identifies Epstein-Barr virus (EBV), Fusobacterium nucleatum, Mycoplasma pneumoniae, Chlamydia psittaci, and Haemophilus influenzae as the most common pathogens, with Klebsiella pneumoniae emerging as the predominant bacterial culprit. Among herpes viruses, EBV and herpes virus 7 (HHV-7) were most frequently detected, with HHV-7 more prevalent in immunocompromised individuals. (3) Risk Factors for Adverse Prognosis and a Mortality Risk Prediction Model in Patients with LRTIs: We identified key risk factors for poor prognosis in lower respiratory tract infection patients, with significant findings including delayed time to mNGS testing, low lymphocyte percentage, presence of chronic lung disease, multiple comorbidities, false-negative CMT results, and positive herpesvirus affecting patient outcomes. We also developed a nomogram model with good consistency and high accuracy (AUC of 0.825) for predicting mortality risk in these patients, offering a valuable clinical tool for assessing prognosis. CONCLUSION: The study underscores mNGS as a superior tool for lower respiratory tract infection diagnosis, exhibiting higher sensitivity and specificity than traditional methods.

Risk factors for long-term invasive mechanical ventilation: a longitudinal study using German health claims data.

BACKGROUND: Long-term invasive mechanical ventilation (IMV) is a major burden for those affected and causes high costs for the health care system. Early risk assessment is a prerequisite for the best possible support of high-risk patients during the weaning process. We aimed to identify risk factors for long-term IMV within 96 h (h) after the onset of IMV. METHODS: The analysis was based on data from one of Germany's largest statutory health insurance funds; patients who received IMV ≥ 96 h and were admitted in January 2015 at the earliest and discharged in December 2017 at the latest were analysed. OPS and ICD codes of IMV patients were considered, including the 365 days before intubation and 30 days after discharge. Long-term IMV was defined as evidence of invasive home mechanical ventilation (HMV), IMV ≥ 500 h, or readmission with (re)prolonged ventilation. RESULTS: In the analysis of 7758 hospitalisations, criteria for long-term IMV were met in 38.3% of cases, of which 13.9% had evidence of HMV, 73.1% received IMV ≥ 500 h and/or 40.3% were re-hospitalised with IMV. Several independent risk factors were identified (p < 0.005 each), including pre-diagnoses such as pneumothorax (OR 2.10), acute pancreatitis (OR 2.64), eating disorders (OR 1.99) or rheumatic mitral valve disease (OR 1.89). Among ICU admissions, previous dependence on an aspirator or respirator (OR 5.13), and previous tracheostomy (OR 2.17) were particularly important, while neurosurgery (OR 2.61), early tracheostomy (OR 3.97) and treatment for severe respiratory failure such as positioning treatment (OR 2.31) and extracorporeal lung support (OR 1.80) were relevant procedures in the first 96 h after intubation. CONCLUSION: This comprehensive analysis of health claims has identified several risk factors for the risk of long-term ventilation. In addition to the known clinical risks, the information obtained may help to identify patients at risk at an early stage. Trial registration The PRiVENT study was retrospectively registered at ClinicalTrials.gov (NCT05260853). Registered at March 2, 2022.

Redox-Mediated Amination of Pyrogallol-Based Polyphenols.

Flavonoids are known to covalently modify amyloidogenic peptides by amination reactions. The underlying coupling process between polyphenols and N-nucleophiles is assessed by several in vitro and in silico approaches. The coupling reaction involves a sequence of oxidative dearomatization, amination, and reductive amination (ODARA) reaction steps. The C6-regioselectivity of the product is confirmed by crystallographic analysis. Under aqueous conditions, the reaction of baicalein with lysine derivatives yields C-N coupling as well as hydrolysis products of transient imine intermediates. The observed C-N coupling reactions work best for flavonoids combining a pyrogallol substructure with an electron-withdrawing group attached to the C4a-position. Thermodynamic properties such as bond dissociation energies also highlight the key role of pyrogallol units for the antioxidant ability. Combining the computed electronic properties and in vitro antioxidant assays suggests that the studied pyrogallol-containing flavonoids act by various radical-scavenging mechanisms working in synergy. Multivariate analysis indicates that a small number of descriptors for transient intermediates of the ODARA process generates a model with excellent performance (r=0.93) for the prediction of cross-coupling yields. The same model has been employed to predict novel antioxidant flavonoid-based molecules as potential covalent inhibitors, opening a new avenue to the design of therapeutically relevant anti-amyloid compounds.

Predicting personalized cumulative live birth rate after a complete in vitro fertilization cycle: an analysis of 32,306 treatment cycles in China.

BACKGROUND: The cumulative live birth rate (CLBR) has been regarded as a key measure of in vitro fertilization (IVF) success after a complete treatment cycle. Women undergoing IVF face great psychological pressure and financial burden. A predictive model to estimate CLBR is needed in clinical practice for patient counselling and shaping expectations. METHODS: This retrospective study included 32,306 complete cycles derived from 29,023 couples undergoing IVF treatment from 2014 to 2020 at a university-affiliated fertility center in China. Three predictive models of CLBR were developed based on three phases of a complete cycle: pre-treatment, post-stimulation, and post-treatment. The non-linear relationship was treated with restricted cubic splines. Subjects from 2014 to 2018 were randomly divided into a training set and a test set at a ratio of 7:3 for model derivation and internal validation, while subjects from 2019 to 2020 were used for temporal validation. RESULTS: Predictors of pre-treatment model included female age (non-linear relationship), antral follicle count (non-linear relationship), body mass index, number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, tubal factor, male factor, and scarred uterus. Predictors of post-stimulation model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. Predictors of post-treatment model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), cumulative Day-3 embryos live-birth capacity (non-linear relationship), number of previous IVF attempts, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. The C index of the three models were 0.7559, 0.7744, and 0.8270, respectively. All models were well calibrated (p = 0.687, p = 0.468, p = 0.549). In internal validation, the C index of the three models were 0.7422, 0.7722, 0.8234, respectively; and the calibration P values were all greater than 0.05. In temporal validation, the C index were 0.7430, 0.7722, 0.8234 respectively; however, the calibration P values were less than 0.05. CONCLUSIONS: This study provides three IVF models to predict CLBR according to information from different treatment stage, and these models have been converted into an online calculator ( https://h5.eheren.com/hcyc/pc/index.html#/home ). Internal validation and temporal validation verified the good discrimination of the predictive models. However, temporal validation suggested low accuracy of the predictive models, which might be attributed to time-associated amelioration of IVF practice.

Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study.

BACKGROUND: Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies. METHODS: This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients. RESULTS: Preoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p < 0.01) in the preoperative model, while age, WHO grade 4 and chemotherapy or radiotherapy treatments (t > 4.12, p < 0.0001) were most significant in the postoperative period. Postoperative predictive models specifically targeting the glioblastoma and IDH wildtype subgroups were also performed, with an AUC of 0.76 and 0.80, respectively. The 50 combinations of distinct risk factors accommodate diverse recurrence risks among glioma patients, and the nomograms visualizes the results for clinical practice. A stratified Cox model identified many prognostic factors for long-term recurrence, thereby facilitating the enhanced formulation of perioperative care plans for patients, and glioblastoma patients displayed a median progression-free survival (PFS) of only 11 months. CONCLUSION: The constructed preoperative and postoperative models reliably predicted short-term postoperative glioma recurrence in a substantial patient cohort. The combinations risk factors and nomograms enhance the operability of personalized therapeutic strategies and care regimens. Particular emphasis should be placed on patients with recurrence within six months post-surgery, and the corresponding treatment strategies require comprehensive clinical investigation.