Baseline gut microbiota profiles affect treatment response in patients with depression.

The role of the gut microbiota in the pathophysiology of depression has been explored in numerous studies, which have confirmed that the baseline gut microbial profiles of patients with depression differ from those of healthy individuals. The gut microbiome affects metabolic activity in the immune and central nervous systems and regulates intestinal ecology through the neuroendocrine system. Additionally, baseline changes in the gut microbiota differed among patients with depression who demonstrated varying treatment response. Currently, probiotics are an emerging treatment for depression; however, the efficacy of modulating the gut microbiota in the treatment of depression remains uncertain. Additionally, the mechanisms by which changes in the gut microbiota affect treatment response in patients with depression remain unclear. In this review, we aimed to summarize the differences in the baseline gut microbiota between the remission and non-remission groups after antidepressant therapy. Additionally, we summarized the possible mechanisms that may contribute to antidepressant resistance through the effects of the gut microbiome on the immune and nervous systems, various enzymes, bioaccumulation, and blood-brain barrier, and provide a basis for treating depression by targeting the gut microbiota.

Comparing the Pre-writing Skills of Diplegic Cerebral Palsy Children to Those of Normal Children.

INTRODUCTION: The pencil grasp and drawing patterns are specific to different age levels. So, if one knows a certain pattern for that particular age, it will guide the intervention plan for children with cerebral palsy (CP). The chances of improvement in diplegic CP are possible with the help of early intervention; therefore, early intervention is only possible if one knows the areas of delay and the age at which the intervention should be started. MATERIAL AND METHODS: It was a cross-sectional, case-control study. A total of 60 children were selected for the study, of which 30 (50%) were normal and 30 (50%) had diplegic cerebral palsy. A convenient sampling method is used for evaluation. RESULTS: The t-value for pencil grasp between the two groups, i.e., normal and CP diplegic, was 3.515 (P=0.001), revealing a significant difference in the grasp pattern of the two groups. Similarly, the t-value for drawing patterns between the two groups, i.e., normal and CP diplegic, was 5.796 (P = 0.001). A significant difference was found in the drawing patterns of both groups. CONCLUSION: Our study found that diplegic CP children performed lower on the Erhardt Developmental Prehension Assessment (EDPA) and showed larger variation in the pencil grasp and drawing than the normal children.

Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging.

Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix's conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.

The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation.

Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes. These molecular responses to dexamethasone were detected in circulating and pulmonary monocytes, and they were directly linked to survival. Monocyte single-cell RNA sequencing (scRNA-seq)-derived signatures were enriched in whole blood transcriptomes of patients with fatal outcome in two independent cohorts, highlighting the potential for identifying non-responders refractory to dexamethasone. Our findings link the effects of dexamethasone to specific immunomodulation and reversal of monocyte dysregulation, and they highlight the potential of single-cell omics for monitoring in vivo target engagement of immunomodulatory drugs and for patient stratification for precision medicine approaches.

Insights into treatment-specific prognostic somatic mutations in NSCLC from the AACR NSCLC GENIE BPC cohort analysis.

BACKGROUND: Treatment of non-small lung cancer (NSCLC) has evolved in recent years, benefiting from advances in immunotherapy and targeted therapy. However, limited biomarkers exist to assist clinicians and patients in selecting the most effective, personalized treatment strategies. Targeted next-generation sequencing-based genomic profiling has become routine in cancer treatment and generated crucial clinicogenomic data over the last decade. This has made the development of mutational biomarkers for drug response possible. METHODS: To investigate the association between a patient's responses to a specific somatic mutation treatment, we analyzed the NSCLC GENIE BPC cohort, which includes 2,004 tumor samples from 1,846 patients. RESULTS: We identified somatic mutation signatures associated with response to immunotherapy and chemotherapy, including carboplatin-, cisplatin-, pemetrexed- or docetaxel-based chemotherapy. The prediction power of the chemotherapy-associated signature was significantly affected by epidermal growth factor receptor (EGFR) mutation status. Therefore, we developed an EGFR wild-type-specific mutation signature for chemotherapy selection. CONCLUSION: Our treatment-specific gene signatures will assist clinicians and patients in selecting from multiple treatment options.

Metabolic Response to Small Molecule Therapy in Colorectal Cancer Tracked with Raman Spectroscopy and Metabolomics.

Despite numerous screening tools for colorectal cancer (CRC), 25% of patients are diagnosed with advanced disease.  Novel diagnostic technologies that are early, accurate, and rapid are imperative to assess the therapeutic efficacy of clinical drugs and identify new biomarkers of treatment response. Here Raman spectroscopy (RS) was used to track metabolic reprogramming in KRAS-mutant HCT116 and SW837 cells, and KRAS wild-type CC cells. RS combined with multivariate analysis methods distinguished nonresponsive, partially responsive, and responsive cells treated with cetuximab, a monoclonal antibody for EGFR inhibition, sotorasib, a clinically approved KRAS inhibitor, and various doses of trametinib, an inhibitor of the MAPK pathway. Cells treated with a combination of subtoxic doses of trametinib and BKM120, an inhibitor of the PI3K pathway, showed a synergistic response between the two pathways. Using a supervised machine learning regression model, we established a scoring methodology trained to a priori predict therapeutic response to new treatment combinations. RS metabolites were verified with mass spectrometry, and enrichment pathways were identified, including amino acid, purine, and nicotinate and nicotinamide metabolism that differentiated monotherapy from combination therapy. Our approach may ultimately be applicable to patient-derived primary cells and cultures of patient tumors to predict effective drugs for individualized care.

Anxiety symptoms predict head and neck cancer survival: Exploring mediation by systemic inflammation and tumor response to treatment.

BACKGROUND: Head and neck cancers (HNC) are associated with high rates of anxiety. Anxiety has been linked to biological pathways implicated in cancer progression, though little is known about its effects on overall survival. We hypothesized that higher pretreatment anxiety levels in patients with HNC would predict poorer 2-year overall survival and expected this relationship to be mediated by both systemic inflammation and tumor response to treatment. METHODS: Patients (N = 394) reported anxiety symptomatology via the GAD-7 at treatment planning. Pre-treatment hematology workup provided an index of systemic inflammation (SII; N = 292). Clinical data review yielded tumor response and overall survival. Logistic and multiple regressions and Cox proportional hazard models tested hypothesized relationships. RESULTS: Higher pretreatment anxiety levels were significantly associated with poorer 2-year survival (hazard ratio [HR], 1.039; 95% confidence interval [CI], 1.014-1.066, p = 0.002). The association between anxiety and SII was not significant, though anxiety was associated with poorer tumor response (odds ratio [OR], 1.033; 95% CI, 1.001-1.066, p = 0.043). Tumor response fully mediated the relationship between anxiety symptoms and 2-year survival (HR, 9.290, 95% CI, 6.152-14.031, p < 0.001). CONCLUSIONS: Anxiety was associated with overall survival. Tumor response, but not systemic inflammation, emerged as a potential biological pathway mediating this effect. Screening for anxiety may be beneficial to help prospectively address these concerns and ameliorate potentially detrimental impact on clinically meaningful cancer outcomes.

Serum GM-CSF level is a predictor of treatment response to tocilizumab in rheumatoid arthritis patients: a prospective observational cohort study.

BACKGROUND: The aim of this prospective observational cohort study was to unveil the predictors of treatment response to tocilizumab (TCZ) therapy in rheumatoid arthritis (RA) patients, in terms of clinical characteristics and serum proinflammatory cytokines, especially to explore the predictive value of granulocyte macrophage-colony stimulating factor (GM-CSF). METHODS: Active adult RA patients with inadequate response to MTX intending to receive TCZ therapy were recruited prospectively in the study. A total of 174 severe RA patients were included for the identification of the associations between treatment response and the following characteristic features: demographics, medications, disease activity, serum proinflammatory cytokines and so on. RESULTS: Disease duration (OR = 0.996), tender joint count (TJC)/68 (OR = 0.943), neutrophil ratio (W4/baseline) (OR = 0.224), the high level of GM-CSF > 5 ng/ml (OR = 0.414) at baseline were the independent adverse predictors of good response assessed by clinical disease activity index (CDAI) at week 24 (W24) for TCZ therapy in RA patients. Moreover, DAS28-ESR (OR = 2.951, P = 0.002) and the high level of GM-CSF > 10 ng/ml at baseline (OR = 5.419, P = 0.002) were independent predictors of poor response, but not the high level of GM-CSF > 5 ng/ml (OR = 2.713, P = 0.054). The patients in the high GM-CSF group had significantly higher DAS28-ESR and serum levels of cytokines (IL-17A, IL-1ß, IL-6, TNF-α) at baseline, as well as significantly higher rate of non-good response (62.8% vs. 39.4%, P = 0.010) and poor response (27.9% vs. 9.1%, P = 0.004) than the low GM-CSF group at W24. In addition, poor responders had significantly higher levels of GM-CSF with concomitant increase in the serum levels of IL-17A and IL-1ß at baseline than those in moderate and good response groups, while serum levels of IL-6 and TNF-α at baseline were not significantly different in three response groups. CONCLUSION: The high levels of GM-CSF (> 5 ng/ml and > 10 ng/ml) at baseline were the independent predictors of non-good response and poor response to TCZ at W24 respectively. The high level of GM-CSF at baseline is a marker of high disease activity and a predictor of poor response to TCZ in severe RA patients, which may facilitate the development of individualized treatment strategies for refractory RA.

Mechanistic in silico explorations of the immunogenic and synergistic effects of radiotherapy and immunotherapy: a critical review.

Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.

GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.

OBJECTIVE: Vital rules learned from FDG-PET radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (Gray Wolf Optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer. Approach: Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (∆SUVmean⩾20% decline) for classification and a continuous response measure (∆SUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression. Main results: GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC:0.58-0.86 vs. 0.52-0.78, p=0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE:0.162-0.192) performed better numerically for low-dimensional models (p=0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE:0.189-0.219, p<0.004). Significance: The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.

Predicting Clinical Improvement in Early Psychosis Using Circuit-Based Resting-State Functional Magnetic Resonance Imaging.

BACKGROUND AND HYPOTHESIS: Identifying biomarkers that predict treatment response in early psychosis (EP) is a priority for psychiatry research. Previous work suggests that resting-state connectivity biomarkers may have promise as predictive measures, although prior results vary considerably in direction and magnitude. Here, we evaluated the relationship between intrinsic functional connectivity of the attention, default mode, and salience resting-state networks and 12-month clinical improvement in EP. STUDY DESIGN: Fifty-eight individuals with EP (less than 2 years from illness onset, 35 males, average age 20 years) had baseline and follow-up clinical data and were included in the final sample. Of these, 30 EPs showed greater than 20% improvement in Brief Psychiatric Rating Scale (BPRS) total score at follow-up and were classified as "Improvers." STUDY RESULTS: The overall logistic regression predicting Improver status was significant (χ2 = 23.66, Nagelkerke's R2 = 0.45, P < .001, with 85% concordance). Significant individual predictors of Improver status included higher default mode within-network connectivity, higher attention-default mode between-network connectivity, and higher attention-salience between-network connectivity. Including baseline BPRS as a predictor increased model significance and concordance to 92%, and the model was not significantly influenced by the dose of antipsychotic medication (chlorpromazine equivalents). Linear regression models predicting percent change in BPRS were also significant. CONCLUSIONS: Overall, these results suggest that resting-state functional magnetic resonance imaging connectivity may serve as a useful biomarker of clinical outcomes in recent-onset psychosis.

Clinical metabolomics investigation of rheumatoid arthritis patients receiving ayurvedic whole system intervention.

BACKGROUND: Arthritis is a common clinical condition seen in Ayurveda clinics. Clinical trials have reported Ayurvedic interventions to be of benefits in many arthritic conditions including Rheumatoid Arthritis (RA). No mechanistic details however are available about how such interventions on their own or as a combination of whole system Ayurveda might be working. OBJECTIVE: The study aims to evaluate simultaneously the clinical outcome of Ayurveda whole system (AWS) intervention in RA patients and identifying the serum metabolic signatures which could be useful for diagnosing the disease and monitoring treatment response. MATERIAL AND METHODS: RA patients (n = 37) simultaneously diagnosed as Amavata fulfilling the specific inclusion and exclusion criteria were recruited in the study and were given Ayurveda whole system (AWS) intervention comprised of oral medicines, local therapy and dietary recommendation for 3 months. The clinical and serum metabolic changes were investigated for pre-treatment RA patients (baseline RA group, n = 37) and post-treatment RA patients (following treatment of 6-weeks (RA_F, n = 26) and three months (RA_T, n = 36). For comparative serum metabolomics analysis, 57 normal healthy control (HC) subjects were also involved and the serum metabolic profiles were measured at high-field 800 MHz NMR spectrometer. The serum metabolic profiles were compared using multivariate statistical analysis and discriminatory metabolic features were evaluated for diagnostic potential using receiver operating characteristic (ROC) curve analysis. RESULTS: A significant reduction in DAS-28 ESR, AAM Score, total swollen joints, total tender joints were observed following AWS intervention. The clinical outcomes were concordant with changes in metabolic profiles of RA patients as these were also shifting towards the normal levels following the intervention. Compared to healthy control (HC) subjects, the sera of baseline RA patients were characterised by increased circulatory level of succinate, lysine, mannose, creatine, and 3-Hydroxybutyrate (3-HB) and decreased levels of alanine. The present study also evaluated the serum metabolic ratios for their discriminatory and diagnostic potential and notably, six metabolic ratios (KHR, KThR, KVR, GHR, PTR and SHR) were found significantly altered (elevated) in baseline RA patients. However, in RA patients receiving AWS treatment, these metabolic changes showed marked convergence towards the metabolic signatures of healthy controls. CONCLUSION: This first of its kind study clearly shows the clinical efficacy of Ayurvedic Whole System (AWS) intervention in the management of Rheumatoid Arthritis (RA), as demonstrated by significant improvements in key clinical parameters. The intervention not only alleviated symptoms but also induced a profound metabolic shifting towards normalization; thus, underscoring the potential of AWS intervention to modulate cellular metabolism in a manner that facilitates a return to homeostasis in RA patients. However, future studies are imperative to confirm these preliminary observations and delineate the underlying mechanisms of action of intervention in cases of RA.

Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.

Construction and validation of a prognostic and therapeutic cuproptosis- and immune-related gene signature in hepatocellular carcinoma.

Background: Abnormal accumulation of copper could induce cell death and tumor growth, and affect tumor immune escape by regulating programmed cell death ligand 1 (PD-L1) expression. This study aims to establish and verify a risk signature based on cuproptosis- and immune-related genes (CIRGs) for hepatocellular carcinoma (HCC) management. Methods: HCC RNA-seq and clinical data were obtained from open databases. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were utilized to screen CIRGs and develop a risk signature. The signature's value for clinical applications, functional enrichment, tumor mutation burden (TMB), and immune profile analyses were investigated systematically. Results: A risk signature was developed utilizing seven CIRGs, and it performed well in predicting the prognosis of HCC patients in both the training and external validation cohorts. The model's risk score was discovered to be related to important clinical features. Top 15 mutated genes in HCC were significantly different among different risk groups. High-risk patients showed higher TMB, and high TMB was closely identified with a poorer prognosis. Immune profile analyses showed that immune infiltration level was higher in low-risk patients than high-risk patients, and the level of immune checkpoint genes expression varied significantly between patients in two different risk groups. Low-risk patients responded well to immunotherapy treatment, whereas high-risk patients were more sensitive to sorafenib, doxorubicin, gemcitabine and AKT (also known as protein kinase B) inhibitors. Conclusions: The established risk signature based on CIRGs can not only well predict the prognosis of HCC patients but is also promising in evaluating TMB and treatment response to immunotherapy, targeted therapy and chemotherapy, which has the potential to assist in the clinical management of HCC.

Predictors of response to Radioactive Iodine Therapy in Intermediate and high risk patients with papillary thyroid carcinoma.

BACKGROUND: Radioactive iodine (RAI) therapy is the standard treatment approach after total thyroidectomy in patients with papillary thyroid carcinoma (PTC). We aimed to identify predictive factors of response to the treatment in intermediate and high-risk patients with PTC. In addition, the impact of multiple RAI treatments was explored. METHODS: In a 3-year retrospective study, data from intermediate and high-risk patients with PTC who received RAI therapy following total thyroidectomy, were analyzed by the end of year-one and year-three. Demographic data, tumor size, capsular/vascular invasion, extrathyroidal extension, local or distant metastasis, initial dose and cumulative dose of RAI, serum thyroglobulin(Tg), antithyroglobulin antibody(TgAb), and imaging findings were investigated. Patients with an excellent response to a single dose of RAI treatment, after three years of follow-up were classified as the "Responder group". Excellent response was defined as stimulated serum Tg less than 1 ng/ml, or unstimulated serum Tg less than 0.2 ng/ml in TgAb-negative patients with negative imaging scans. RESULTS: 333 patient records with a complete data set were analyzed in this study. After three years of initial treatment, 271 patients were non-responders (NR) and 62 were responders (R). At baseline, the median pre-ablation serum Tg level was 5.7 ng/ml in the NR group, and 1.25 ng/ml in the R group (P < 0.001). TSH-Stimulated serum Tg greater than 15.7 ng/ml, was associated with response failure even after multiple RAI therapy, AUC: 0.717(0.660-0.774), sensitivity: 52.5%, specificity: 89.47%, P < 0.001. On the other hand, multiple RAI therapy was associated with excellent response in 16.2% of the patients. The chance of ER was decreased by 74% if initial post-operation ultrasound imaging confirmed the presence of locoregional involvement, OR 0.26, (95% CI: 0.12-0.55), P < 0.001. CONCLUSION: Stimulated serum Tg and locoregional involvement after total thyroidectomy are predictive factors of non-response to RAI therapy in intermediate and high-risk patients with PTC. In addition, a minority of patients achieve excellent response after multiple RAI therapy.

The use of adjuvant chemotherapy combined with concurrent chemoradiotherapy enhances survival rates in cases of locally advanced nasopharyngeal carcinoma.

This study aimed to evaluate the impact of adjuvant chemotherapy on survival rates, adverse events, and quality of life (QOL) in patients with locally advanced nasopharyngeal carcinoma (NPC). A retrospective cohort study was conducted, including patients with firstly histologically confirmed non-metastatic stage III-IVB NPC between February 2018 and February 2020, and with continuous follow-up data available, were chosen from the medical records of the affiliated hospital of Qingdao University and Zibo Central Hospital. There were 395 patients receiving concurrent chemoradiotherapy (CCRT) with adjuvant chemotherapy (adjuvant chemotherapy group) and 428 patients receiving CCRT alone (control group). The two groups were compared for treatment response, adverse events, and QOL scores. Besides, Kaplan-Meier plots, and multivariate COX analysis were conducted. The adjuvant chemotherapy group demonstrated a significantly higher overall survival and disease-free survival compared to the control group. The use of adjuvant chemotherapy was significantly correlated with improved overall survival and disease-free survival. Adjuvant chemotherapy was associated with reduced local recurrence and distant metastasis rates. However, higher rates of adverse events were observed in the adjuvant chemotherapy group. QOL scores for physical functioning, emotional functioning, and overall quality of life were higher in the adjuvant chemotherapy group. The findings of this study indicate that adjuvant chemotherapy in locally advanced NPC is associated with improved treatment response, extended overall and disease-free survivals, and better QOL, despite higher rates of adverse events.

Rare HCV subtypes and retreatment outcomes in a cohort of European DAA-experienced patients.

Background and Aims: Data on the prevalence and characteristics of so-called rare HCV genotypes (GTs) in larger cohorts is limited. This study investigates the frequency of rare GT and resistance-associated substitutions and the efficacy of retreatment in a European cohort. Methods: A total of 129 patients with rare GT1-6 were included from the European resistance database. NS3, NS5A, and NS5B were sequenced and clinical parameters and retreatment efficacies were collected retrospectively. Results: Overall 1.5% (69/4,656) of direct-acting antiviral (DAA)-naive and 4.4% (60/1,376) of DAA-failure patients were infected with rare GT. Although rare GTs were almost equally distributed throughout GT1-6 in DAA-naive patients, we detected mainly rare GT4 (47%, 28/60 GT4; of these n = 17, subtype 4r) and GT3 (25%, 15/60 GT3, of these n = 8, subtype 3b) among DAA-failures. A total of 62% (37/60) of DAA failures had not responded to first-generation regimes and the majority was infected with rare GT4 (57%, 21/37). In contrast, among patients with failure to pangenotypic DAA regimens (38%, 23/60), infections with rare GT3 were overrepresented (57%, 13/23). Although NS5A RASs were uncommon in rare GT2, GT5a, and GT6, we observed combined RASs in rare GT1, GT3, and GT4 at positions 28, 30, 31, which can be considered as inherent. DAA failures with completed follow-up of retreatment, achieved a high SVR rate (94%, 45/48 modified intention-to-treat analysis; 92%, 45/49 intention-to-treat). Three patients with GT4f, 4r, or 3b, respectively, had virological treatment failure. Conclusions: In this European cohort, rare HCV GT were uncommon. Accumulation of specific rare GT in DAA-failure patients suggests reduced antiviral activities of DAA regimens. The limited global availability of pangenotypic regimens for first line therapy as well as multiple targeted regimens for retreatment could result in HCV elimination targets being delayed. Impact and implications: Data on the prevalence and characteristics of rare HCV genotypes (GT) in larger cohorts are still scarce. This study found low rates of rare HCV GTs among European HCV-infected patients. In direct-acting antiviral (DAA)-failure patients, rare GT3 subtypes accumulated after pangenotypic DAA treatment and rare GT4 after first generation DAA failure and viral resistance was detected at NS5A positions 28, 30, and 31. The limited global availability of pangenotypic DAA regimens for first line therapy as well as multiple targeted regimens for retreatment could result in HCV elimination targets being delayed.

Electrophysiological Markers Predicting Antipsychotic Treatment Response in Patients with Schizophrenia: A Retrospective Study.

Purpose: This study aimed to provide an objective means of predicting treatment responses in patients with schizophrenia using quantitative electroencephalography (qEEG) as an electrophysiological indicator. We obtained qEEG recordings from patients with schizophrenia and explored them for patterns indicative of treatment responsiveness. Patients and Methods: The study included 68 patients had been diagnosed with schizophrenia spectrum disorder. After retrospectively gathering demographic information, clinical data such as qEEG, Positive and Negative Syndrome Scale (PANSS), a multiple regression analysis was performed. This analysis employed baseline qEEG findings as independent variables and PANSS score changes as dependent variables to discern causal relationships. Results: The mean age of the participants was 38.4 years(SD =13.73). The mean PANSS score on admission was 92.97, decreasing to 67.41 at discharge. Multiple regression analysis revealed that delta waves in T4 (ß=0.346, t=3.165, p=0.002), and high-beta waves in Fp2 (ß=0.231, t=2.361, p=0.021) were associated with PANSS changes in absolute power. In addition, the delta waves of O2 (ß=0.250, t=3.288, p=0.002); beta waves of T3 (ß=-1.463, t=-5.423, p<0.001) and O2 (ß=0.551, t=3.366, p=0.001); high beta waves of Fp1 (ß=0.307, t=4.026, p<0.001), T3 (ß=0.855, t=4.414, p<0.001) and T6 (ß=-0.838, t=-4.559, p<0.001) of absolute power using the Z-score were also related to PANSS changes. Pearson's correlation analysis showed that only delta waves at Cz (r= 0.246, p=0.043) in absolute power correlated with changes in the PANSS. Conclusion: We found that certain qEEG wave patterns in patients with schizophrenia prior to antipsychotic treatment were linked to PANSS changes before and after treatment. Delta waves and beta waves, primarily in the frontal and temporal regions, were found to be significantly associated with changes in PANSS scores. In the future, the qEEG indicators identified in this study could serve as electrophysiological markers for predicting antipsychotic treatment responses in patients with schizophrenia.

Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: a multicenter study.

PURPOSE: Neoadjuvant chemoradiotherapy has been the standard practice for patients with locally advanced rectal cancer. However, the treatment response varies greatly among individuals, how to select the optimal candidates for neoadjuvant chemoradiotherapy is crucial. This study aimed to develop an endoscopic image-based deep learning model for predicting the response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. METHODS: In this multicenter observational study, pre-treatment endoscopic images of patients from two Chinese medical centers were retrospectively obtained and a deep learning-based tumor regression model was constructed. Treatment response was evaluated based on the tumor regression grade and was defined as good response and non-good response. The prediction performance of the deep learning model was evaluated in the internal and external test sets. The main outcome was the accuracy of the treatment prediction model, measured by the AUC and accuracy. RESULTS: This deep learning model achieved favorable prediction performance. In the internal test set, the AUC and accuracy were 0.867 (95% CI: 0.847-0.941) and 0.836 (95% CI: 0.818-0.896), respectively. The prediction performance was fully validated in the external test set, and the model had an AUC of 0.758 (95% CI: 0.724-0.834) and an accuracy of 0.807 (95% CI: 0.774-0.843). CONCLUSION: The deep learning model based on endoscopic images demonstrated exceptional predictive power for neoadjuvant treatment response, highlighting its potential for guiding personalized therapy.

Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis.

Background: Disease-modifying antirheumatic drugs (bDMARDs) have shown efficacy in treating Rheumatoid Arthritis (RA). Predicting treatment outcomes for RA is crucial as approximately 30% of patients do not respond to bDMARDs and only half achieve a sustained response. This study aims to leverage machine learning to predict both initial response at 6 months and sustained response at 12 months using baseline clinical data. Methods: Baseline clinical data were collected from 154 RA patients treated at the University Hospital in Erlangen, Germany. Five machine learning models were compared: Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), K-nearest neighbors (KNN), Support Vector Machines (SVM), and Random Forest. Nested cross-validation was employed to ensure robustness and avoid overfitting, integrating hyperparameter tuning within its process. Results: XGBoost achieved the highest accuracy for predicting initial response (AUC-ROC of 0.91), while AdaBoost was the most effective for sustained response (AUC-ROC of 0.84). Key predictors included the Disease Activity Score-28 using erythrocyte sedimentation rate (DAS28-ESR), with higher scores at baseline associated with lower response chances at 6 and 12 months. Shapley additive explanations (SHAP) identified the most important baseline features and visualized their directional effects on treatment response and sustained response. Conclusions: These findings can enhance RA treatment plans and support clinical decision-making, ultimately improving patient outcomes by predicting response before starting medication.