Cyclic stretch induced epigenetic activation of periodontal ligament cells.

Periodontal ligament (PDL) cells play a crucial role in maintaining periodontal integrity and function by providing cell sources for ligament regeneration. While biophysical stimulation is known to regulate cell behaviors and functions, its impact on epigenetics of PDL cells has not yet been elucidated. Here, we aimed to investigate the cytoskeletal changes, epigenetic modifications, and lineage commitment of PDL cells following the application of stretch stimuli to PDL. PDL cells were subjected to stretching (0.1 Hz, 10 %). Subsequently, changes in focal adhesion, tubulin, and histone modification were observed. The survival ability in inflammatory conditions was also evaluated. Furthermore, using a rat hypo-occlusion model, we verified whether these phenomena are observed in vivo. Stretched PDL cells showed maximal histone 3 acetylation (H3Ace) at 2 h, aligning perpendicularly to the stretch direction. RNA sequencing revealed stretching altered gene sets related to mechanotransduction, histone modification, reactive oxygen species (ROS) metabolism, and differentiation. We further found that anchorage, cell elongation, and actin/microtubule acetylation were highly upregulated with mechanosensitive chromatin remodelers such as H3Ace and histone H3 trimethyl lysine 9 (H3K9me3) adopting euchromatin status. Inhibitor studies showed mechanotransduction-mediated chromatin modification alters PDL cells behaviors. Stretched PDL cells displayed enhanced survival against bacterial toxin (C12-HSL) or ROS (H2O2) attack. Furthermore, cyclic stretch priming enhanced the osteoclast and osteoblast differentiation potential of PDL cells, as evidenced by upregulation of lineage-specific genes. In vivo, PDL cells from normally loaded teeth displayed an elongated morphology and higher levels of H3Ace compared to PDL cells with hypo-occlusion, where mechanical stimulus is removed. Overall, these data strongly link external physical forces to subsequent mechanotransduction and epigenetic changes, impacting gene expression and multiple cellular behaviors, providing important implications in cell biology and tissue regeneration.

Effectiveness of Extracorporeal Shock Wave Therapy after Botulinum Toxin Injection for Post-Stroke Upper Extremity Spasticity: A Randomized Controlled Study.

Post-stroke spasticity is a common complication that limits the functional performance of patients. Botulinum toxin (BTx) is an effective treatment for spasticity. Numerous researchers have applied extracorporeal shock wave therapy (ESWT) to address post-stroke spasticity, yielding positive clinical outcomes. We aimed to clarify the add-on effects of ESWT on BTx therapy for spasticity in patients with post-stroke. Sixteen eligible patients with upper extremity spasticity after stroke were recruited for this study. They were randomized to either a BTx with focused ESWT treatment group or a BTx alone group. Spasticity, measured using the modified Ashworth score (MAS) and modified Tardieu scale (MTS), showed statistically significant improvements in the elbow and wrist flexor muscles in both BTx + ESWT group and BTx alone groups. However, no significant differences were observed between the two groups with time flow. The BTx + ESWT group showed significantly decreased MAS of the finger flexors at follow-up and increased R1 (MTS) of the finger flexors at 3 weeks after treatment, which was not observed in the BTx alone group. This is the first study to identify the add-on effect of ESWT on BTx injections to improve post-stroke upper limb spasticity.

The characteristics and clinical outcomes of a pluripotent high-risk group with the potential to develop a diverse range of psychiatric disorders.

BACKGROUND: Recent studies have indicated that clinical high risk for psychosis (CHR-P) is highly specific for psychotic disorders other than pluripotential to various serious mental illnesses. However, not all CHR-P develop psychotic disorder only, and psychosis can occur in non-psychotic disorders as well. Our prospective cohort study aims to investigate the characteristics and clinical outcomes of a pluripotent high-risk group with the potential to develop a diverse range of psychiatric disorders. METHODS: The SPRIM study is a prospective naturalistic cohort program that focuses on the early detection of those at risk of developing serious mental illness, including psychosis (CHR-P), bipolar (CHR-B), and depressive disorder (CHR-D), as well as undifferentiated risk participants (UCHR). Our study has a longitudinal design with a baseline assessment and eight follow-up evaluations at 6, 12, 18, 24, 30, 36, 42, and 48 months to determine whether participants have transitioned to psychosis or mood disorders. RESULTS: The SPRIM sample consisted of 90 CHR participants. The total cumulative incidence rate of transition was 53.3% (95% CI 32.5-77.2). CHR-P, CHR-B, CHR-D, and UCHR had cumulative incidence rates of 13.7% (95% CI 3.4-46.4), 52.4% (95% CI 28.1-81.1), 66.7% (95% CI 24.6-98.6) and 54.3% (95% CI 20.5-93.1), respectively. The cumulative incidence of psychosis, bipolar, and depressive disorder among all participants was 3.3% (95% CI 0.8-11.5), 45.7% (95% CI 24.4-73.6), and 11.2% (95% CI 3.1-36.2), respectively. CONCLUSIONS: Our study suggests that the concept of pluripotent high-risk for a diverse range of psychiatric disorders is an integrative approach to examining transdiagnostic interactions between illnesses with a high transition rate and minimizing stigma.

Dietary carbohydrates regulate intestinal colonization and dissemination of Klebsiella pneumoniae.

Bacterial translocation from the gut microbiota is a source of sepsis in susceptible patients. Previous work suggests that overgrowth of gut pathobionts, including Klebsiella pneumoniae, increases the risk of disseminated infection. Our data from a human dietary intervention study found that, in the absence of fiber, K. pneumoniae bloomed during microbiota recovery from antibiotic treatment. We thus hypothesized that dietary nutrients directly support or suppress colonization of this gut pathobiont in the microbiota. Consistent with our study in humans, complex carbohydrates in dietary fiber suppressed the colonization of K. pneumoniae and allowed for recovery of competing commensals in mouse models. In contrast, through ex vivo and in vivo modeling, we identified simple carbohydrates as a limiting resource for K. pneumoniae in the gut. As proof of principle, supplementation with lactulose, a nonabsorbed simple carbohydrate and an FDA-approved therapy, increased colonization of K. pneumoniae. Disruption of the intestinal epithelium led to dissemination of K. pneumoniae into the bloodstream and liver, which was prevented by dietary fiber. Our results show that dietary simple and complex carbohydrates were critical not only in the regulation of pathobiont colonization but also disseminated infection, suggesting that targeted dietary interventions may offer a preventative strategy in high-risk patients.

Moderating AKT signaling with baicalein protects against weight loss by preventing muscle atrophy in a cachexia model caused by CT26 colon cancer.

Cancer cachexia is a type of energy-wasting syndrome characterized by fatigue, anorexia, muscle weakness, fat loss, and systemic inflammation. Baicalein, a flavonoid with bioactive properties, has demonstrated the ability to mitigate cardiac and skeletal muscle atrophy in different experimental settings. This effect is achieved through the inhibition of muscle proteolysis, suggesting its potential in preserving skeletal muscle homeostasis. In this study, we investigated the anti-cancer cachexia effects of baicalein in the regulation of muscle and fat wasting, both in vivo and in vitro. Baicalein attenuated body weight loss, including skeletal muscle and white adipose tissue (WAT), in CT26-induced cachectic mice. Moreover, baicalein increased muscle fiber thickness and suppressed the muscle-specific ubiquitin-protease system, including F-box only protein 32 and muscle RING-finger protein-1, by activating AKT phosphorylation both in vivo and in vitro. The use of LY294002, a particular inhibitor of AKT, eliminated the observed impact of baicalein on the improvement of muscle atrophy. In conclusion, baicalein inhibits muscle proteolysis and enhances AKT phosphorylation, indicating its potential role in cancer cachexia-associated muscle atrophy.

Influence of patient-clinician relationship style on acupuncture outcomes in functional dyspepsia: A multi-site randomized controlled trial in Korea.

INTRODUCTION: Research suggests that a warm and empathic "patient-centered" patient-clinician relationship produces better clinical outcomes when compared with a more neutral "disease-centered" relationship. Acupuncturists performed both styles of therapy for patients with functional dyspepsia in Korea. METHODS: The present randomized controlled trial assigned patients (n = 73) to identical acupuncture treatment with either patient-centered augmented care or disease-centered limited care. The Korean version of the Nepean Dyspepsia Index (NDI-K) was the primary outcome measure. Secondary outcome measures included Consultation And Relational Empathy (CARE) scale. RESULTS: Both groups showed improvement in NDI-K. Patient-centered augmented acupuncture produced less effective symptom improvement compared to disease-centered limited acupuncture (NDI-K sum score and frequency; P = 0.008 and P = 0.037 respectively). CARE scores were higher for the augmented versus limited group (P = 0.001), supporting the fidelity of the experimentally controlled patient/clinician relationship. There were no significant differences between the groups in any of other secondary outcomes. CONCLUSION: Patients demonstrated greater improvement following acupuncture conducted with a more neutral, "disease-centered" style of relationship. This result is counter to similar research conducted in Western countries and suggests that cultural factors can significantly shape optimum styles of acupuncture therapy. PRACTICE IMPLICATIONS: Clinicians should consider cultural differences when applying acupuncture therapy.

Proteomic profiling in the progression of psychosis: Analysis of clinical high-risk, first episode psychosis, and healthy controls.

BACKGROUND AND HYPOTHESIS: Recent evidence has highlighted the benefits of early detection and treatment for better clinical outcomes in patients with psychosis. Biological markers of the disease have become a focal point of research. This study aimed to identify protein markers detectable in the early stages of psychosis and indicators of progression by comparing them with those of healthy controls (HC) and first episode psychosis (FEP). STUDY DESIGN: The participants comprised 28 patients in the clinical high-risk (CHR) group, 49 patients with FEP, and 61 HCs aged 15-35 years. Blood samples were collected and analyzed using multiple reaction monitoring-mass spectrometry to measure the expression of 158 peptide targets. Data were adjusted for age, sex, and use of psychotropic drugs. STUDY RESULTS: A total of 18 peptides (17 proteins) differed significantly among the groups. The protein PRDX2 was higher in the FEP group than in the CHR and HC groups and showed increased expression according to disease progression. The levels of six proteins were significantly higher in the FEP group than in the CHR group. Nine proteins differed significantly in the CHR group compared to the other groups. Sixteen proteins were significantly correlated with symptom severity. These proteins are primarily related to the coagulation cascade, inflammatory response, brain structure, and synaptic plasticity. CONCLUSIONS: Our findings suggested that peripheral protein markers reflect disease progression in patients with psychosis. Further longitudinal research is needed to confirm these findings and to identify the specific roles of these markers in the pathogenesis of schizophrenia.

Pharmacotherapy for obsessive-compulsive disorder: Real-world evidence from a 10-year retrospective data analysis.

We investigated pharmacotherapy trends for obsessive-compulsive disorder (OCD) patients at a Korean tertiary hospital from 2008 to 2017. Out of 1894 patients, 82.9% received at least one psychotropic medication, with prescription rates increasing over time. The most frequently prescribed drug classes were selective serotonin reuptake inhibitors (SSRIs, 80.5%), anxiolytics (57.5%), antipsychotics (47.2%), other antidepressants (21.1%), and mood stabilizers (18.4%). Combination therapy was administered to 79.7% of medicated patients, with SSRIs, anxiolytics, and antipsychotics being the most common combination. Comorbidities significantly increased the prescription rates of all psychotropic classes (P < 0.001). Our study offers insights that may aid in bridging the gap between OCD treatment guidelines and real-world clinical practice.

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables.

The typically rugged nature of molecular free-energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free-energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for the data-driven discovery of CVs parametrizing the important large-scale motions of the system. A challenge to CV discovery is learning CVs invariant to the symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multimolecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate permutation invariant vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand the sampling of configurational phase space and obtain converged CVs and free-energy landscapes. We demonstrate the permutationally invariant network for enhanced sampling (PINES) approach in applications to the self-assembly of a 13-atom argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C45H92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.

Ligand-based magnetic extraction and safety assessment of zinc oxide nanoparticles in food products.

Zinc oxide (ZnO) is a zinc supplement widely used in health products and is approved by the FDA as Generally Regarded as Safe (GRAS). However, concerns have arisen regarding the potential health effects of nanoscale ZnO, as its reactivity differs from that of its bulk form. This has led to the need for an efficient method to extract ZnO from food products without altering its physicochemical properties, where conventional methods have proven to be inadequate. This study introduces an innovative approach using starch magnetic particles (SMPs) functionalized with a 12-amino acid peptide modified with five lysines (ZBP), that has specific affinity to ZnO. ZBP@SMPs effectively and rapidly extract intact ZnO from food products, achieving recovery efficiencies ranging from 60% to 90%, all while maintaining its morphology and crystallinity. The diameter of ZnO particles recovered from six commercial food products ranged from 25 to 500 nm, with 33% falling below 100 nm, highlighting the need for a size-dependent toxicity study. However, cytotoxicity assessment on human intestinal Caco-2 cells shows all ZnO samples affects cell proliferation and membrane integrity in a dose-dependent manner due to partial dissolution. This study contributes to understanding the safety of ZnO-containing food products and highlights potential health implications associated with their consumption.

A Study on Wheel Member Condition Recognition Using 1D-CNN.

The condition of a railway vehicle's wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance personnel and addressed after detection. As a result, there is a need for predictive technology concerning wheel conditions to prevent railway vehicle damage and potential accidents due to wheel defects. Insufficient predictive technology for railway vehicle's wheel conditions forms the background for this study. In this research, a real-time tire wear classification system for light-rail rubber tires was proposed to reduce operational costs, enhance safety, and prevent service delays. To perform real-time condition classification of rubber tires, operational data from railway vehicles, including temperature, pressure, and acceleration, were collected. These data were processed and analyzed to generate training data. A 1D-CNN model was employed to classify tire conditions, and it demonstrated exceptionally high performance with a 99.4% accuracy rate.

Time-resolving state-specific molecular dissociation with XUV broadband absorption spectroscopy.

The electronic and nuclear dynamics inside molecules are essential for chemical reactions, where different pathways typically unfold on ultrafast timescales. Extreme ultraviolet (XUV) light pulses generated by free-electron lasers (FELs) allow atomic-site and electronic-state selectivity, triggering specific molecular dynamics while providing femtosecond resolution. Yet, time-resolved experiments are either blind to neutral fragments or limited by the spectral bandwidth of FEL pulses. Here, we combine a broadband XUV probe pulse from high-order harmonic generation with an FEL pump pulse to observe dissociation pathways leading to fragments in different quantum states. We temporally resolve the dissociation of a specific O2+ state into two competing channels by measuring the resonances of ionic and neutral fragments. This scheme can be applied to investigate convoluted dynamics in larger molecules relevant to diverse science fields.

Impact of long-acting injectable aripiprazole on the concomitant medication and antipsychotic polypharmacy: a retrospective, observational study of 127 patients with psychosis.

Antipsychotic polypharmacy (APP) has become prevalent over the years, but several concerns have been raised over APP. Accumulating evidence suggests that aripiprazole long-acting injectable (LAI) may reduce the rate of APP, but the association remains speculative. This retrospective observational study included 127 patients with psychosis and observed them for 1.8 ±â€…1.3 years, up to 4 years. Prescription data of antipsychotics (APs), mood stabilisers, benzodiazepines, and anti-extrapyramidal side effect medications were obtained at baseline and the last observation. Daily chlorpromazine equivalent (CPZ) dose of APs decreased from 124.40 ±â€…235.35 mg to 77.95 ±â€…210.36 mg (P = 0.027). The daily dose of anticholinergics and beta-blockers also significantly decreased after introducing aripiprazole LAI. Among the patients having APP, the number of concurrent APs along with daily CPZ dose of APs decreased after initiation of aripiprazole LAI from 1.28 ±â€…0.62 to 0.85 ±â€…0.73 (P < 0.001) and 298.33 ±â€…308.70 mg to 155.43 ±â€…280.53 mg (P = 0.004), respectively. Treatment with aripiprazole LAI for up to 4 years in patients with psychosis was associated with a reduced number of prescribed APs in patients having an APP and a reduced dose of APs and concurrent psychotropic medications.

Analysis of Mechanical Properties and Structural Analysis According to the Multi-Layered Structure of Polyethylene-Based Self-Reinforced Composites.

In this research, a self-reinforced composite material was manufactured using a single polyethylene material, and this self-reinforced composite material has excellent recyclability and is environmentally friendly compared to composite materials composed of other types of material, such as glass fiber reinforced composites (GFRP) and carbon fiber reinforced composites (CFRP). In this research, the manufactured self-reinforced composite material consists of an outer layer and an inner layer. To manufacture the outer layer, low density polyethylene (LDPE) films were laminated on high density polyethylene (HDPE) fabrics and knitted fabrics, and composite materials were prepared at various temperatures using hot stamping. A 3D printing process was utilized to manufacture the inner layer. After designing a structure with a cross-sectional shape of a triangle, circle, or hexagon, the inner layer structure was manufactured by 3D printing high-density polyethylene material. As an adhesive film for bonding the outer layer and the inner layer, a polyethylene-based self-reinforced composite material was prepared using a low-density polyethylene material. Input data for simulation of self-reinforced composite materials were obtained through tensile property analysis using a universal testing machine (UTM, Shimadzu, Kyoto, Japan), and the physical property values derived as output data and actual experimental values were obtained. As a result of the comparison, the error rate between simulation data and experimental data was 5.4% when the shape of the inner layer of self-reinforced composite material was a hexagon, 3.6% when it was a circle, and 7.8% when a triangular shape showed the highest value. Simulation in a virtual space can reduce the time and cost required for actual research and can be important data for producing high-quality products.

A Study on Wheel Member Condition Recognition Using Machine Learning (Support Vector Machine).

The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%.

Lipid-Lowering Efficacy of Combination Therapy With Moderate-Intensity Statin and Ezetimibe Versus High-Intensity Statin Monotherapy: A Randomized, Open-Label, Non-Inferiority Trial From Korea.

Objective: This phase IV, multicenter, randomized controlled, open-label, and parallel clinical trial aimed to compare the efficacy and safety of ezetimibe and moderate intensity rosuvastatin combination therapy to that of high intensity rosuvastatin monotherapy in patients with atherosclerotic cardiovascular disease (ASCVD). Methods: This study enrolled patients with ASCVD and after a four-week screening period, patients were randomly assigned to receive either rosuvastatin and ezetimibe (RE 10/10 group) or high-intensity rosuvastatin (R20 group) only in a 1:1 ratio. The primary outcome was the difference in the percent change in the mean low-density lipoprotein cholesterol (LDL-C) level from baseline to 12 weeks between two groups after treatment. Results: The study found that after 12 and 24 weeks of treatment, the RE10/10 group had a greater reduction in LDL-C level compared to the R20 group (-22.9±2.6% vs. -15.6 ± 2.5% [p=0.041] and -24.2±2.5% vs. -12.9±2.4% [p=0.001] at 12 and 24 weeks, respectively). Moreover, a greater number of patients achieved the target LDL-C level of ≤70 mg/dL after the treatment period in the combination group (74.6% vs. 59.9% [p=0.012] and 76.2% vs. 50.8% [p<0.001] at 12 and 24 weeks, respectively). Importantly, there were no significant differences in the occurrence of overall adverse events and adverse drug reactions between two groups. Conclusion: Moderate-intensity rosuvastatin and ezetimibe combination therapy had better efficacy in lowering LDL-C levels without increasing adverse effects in patients with ASCVD than high-intensity rosuvastatin monotherapy. Trial Registration: ClinicalTrials.gov Identifier: NCT03494270.

ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network.

Recent developments in artificial neural networks and their learning algorithms have enabled new research directions in computer vision, language modeling, and neuroscience. Among various neural network algorithms, spiking neural networks (SNNs) are well-suited for understanding the behavior of biological neural circuits. In this work, we propose to guide the training of a sparse SNN in order to replace a sub-region of a cultured hippocampal network with limited hardware resources. To verify our approach with a realistic experimental setup, we record spikes of cultured hippocampal neurons with a microelectrode array (in vitro). The main focus of this work is to dynamically cut unimportant synapses during SNN training on the fly so that the model can be realized on resource-constrained hardware, e.g., implantable devices. To do so, we adopt a simple STDP learning rule to easily select important synapses that impact the quality of spike timing learning. By combining the STDP rule with online supervised learning, we can precisely predict the spike pattern of the cultured network in real-time. The reduction in the model complexity, i.e., the reduced number of connections, significantly reduces the required hardware resources, which is crucial in developing an implantable chip for the treatment of neurological disorders. In addition to the new learning algorithm, we prototype a sparse SNN hardware on a small FPGA with pipelined execution and parallel computing to verify the possibility of real-time replacement. As a result, we can replace a sub-region of the biological neural circuit within 22 µs using 2.5 × fewer hardware resources, i.e., by allowing 80% sparsity in the SNN model, compared to the fully-connected SNN model. With energy-efficient algorithms and hardware, this work presents an essential step toward real-time neuroprosthetic computation.

Association between initial pattern of clozapine titration, concentration-to-dose ratio, and incidence of fever in patients with schizophrenia spectrum disorders in a Korean tertiary hospital.

Safe and effective administration of clozapine requires careful monitoring for inflammatory reactions during the initial titration. The concentration-to-dose (C/D) ratio must be taken into account, which may vary among ethnicities. In this retrospective study, 1408 Korean schizophrenia inpatients were examined for during the first 8 weeks of clozapine titration. The average doses of clozapine administered during weeks 1, 2, 4, and 8 were 77.37, 137.73, 193.20, and 212.83 mg/day, with significantly lower doses for females than males. The average C/D ratio was significantly higher in females (1.75 ± 1.04 and 1.11 ± 0.67 ng/mL per mg/day). Patients with higher C/D ratios were more likely to experience fever and were prescribed lower doses of clozapine starting from week 4. In total, 22.1 % of patients developed a fever at an average of 15.74 days after initiating clozapine. Patients who developed a fever were younger, used more antipsychotics at baseline, had a higher C/D ratio, and had a higher incidence of an elevated C-reactive protein level. A higher C/D ratio, use of a greater number of antipsychotics at baseline, and concomitant olanzapine use were risk factors for the development of inflammatory reactions. The incidence of pneumonia, agranulocytosis, and myocarditis within 8 weeks were 3.7 %, 0.3 %, and 0.1 %. In summary, the target dose of clozapine titration is lower for Korean schizophrenia patients, with a higher C/D ratio and more frequent fever compared to Western patients; however, myocarditis occurs rarely. Our findings may contribute to the titration methods for clozapine for the East Asian population.

Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer.

The aim of our retrospective study is to develop and externally validate an 18F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The 18F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.

Colonization and Dissemination of Klebsiella pneumoniae is Dependent on Dietary Carbohydrates.

Dysbiosis of the gut microbiota is increasingly appreciated as both a consequence and precipitant of human disease. The outgrowth of the bacterial family Enterobacteriaceae is a common feature of dysbiosis, including the human pathogen Klebsiella pneumoniae . Dietary interventions have proven efficacious in the resolution of dysbiosis, though the specific dietary components involved remain poorly defined. Based on a previous human diet study, we hypothesized that dietary nutrients serve as a key resource for the growth of bacteria found in dysbiosis. Through human sample testing, and ex-vivo , and in vivo modeling, we find that nitrogen is not a limiting resource for the growth of Enterobacteriaceae in the gut, contrary to previous studies. Instead, we identify dietary simple carbohydrates as critical in colonization of K. pneumoniae . We additionally find that dietary fiber is necessary for colonization resistance against K. pneumoniae , mediated by recovery of the commensal microbiota, and protecting the host against dissemination from the gut microbiota during colitis. Targeted dietary therapies based on these findings may offer a therapeutic strategy in susceptible patients with dysbiosis.