Optimal dosing regimen of caspofungin in adolescents with allogeneic haematopoietic stem cell transplantation.

OBJECTIVES: The optimal dosing regimen of caspofungin in adolescents undergoing allogeneic haematopoietic stem cell transplantation against Candida spp. is unknown. The study aimed to compare body surface area (BSA)-based and fixed dosing regimens through population pharmacokinetic (PPK) analysis and to optimize dosing regimens likely to achieve therapeutic exposures. METHODS: Opportunistic sampling was used to collect plasma concentrations through a prospective observational pharmacokinetic study. PPK analysis and Monte Carlo simulations (n = 1000) were performed using NONMEM. RESULTS: A total of 86 samples of 30 adolescents (12-17 years old) were best described by a two-compartment pharmacokinetic model. BSA is the only covariate on clearance and central volume of distribution. For Candida glabrata and Candida albicans, a standard dosing regimen could achieve at least a 90% probability of target attainment for the indicator of AUC0-24/MIC90. Dosing regimen simulations identified a BSA cut-off value of 1.3 m2, where a fixed loading dose (LD) is preferred when BSA ≥ 1.3 m2 and a BSA-based LD is preferred when BSA < 1.3 m2. For maintenance dose (MD), however, the BSA-based dose was proposed, regardless of BSA. The current maximum dosing regimen of LD 70 mg/day and MD 70 mg/day could not result in sufficient antifungal exposure for Candida parapsilosis with MIC90 of 1 mg/L. Furthermore, an LD of 70 mg/day and MD of 60 mg/m2/day rendered 90.4% steady-state trough concentration (Ctrough) over 1 mg/L in the virtual population. CONCLUSIONS: Our study proposed optimized dosing regimens of caspofungin based on AUC0-24/MIC90 or Ctrough, which may support further individualized treatment.

Easily misdiagnosed X-linked adrenoleukodystrophy.

BACKGROUND: Addison's disease and X-linked adrenoleukodystrophy (X-ALD) (Addison's-only) are two diseases that need to be identified. Addison's disease is easy to diagnose clinically when only skin and mucosal pigmentation symptoms are present. However, X-ALD (Addison's-only) caused by ABCD1 gene variation is ignored, thus losing the opportunity for early treatment. This study described two patients with initial clinical diagnosis of Addison's disease. However, they rapidly developed neurological symptoms triggered by infection. After further genetic testing, the two patients were diagnosed with X-ALD. METHODS: We retrospectively analyzed X-ALD patients admitted to our hospital. Clinical features, laboratory test results, and imaging data were collected. Whole-exome sequencing was used in molecular genetics. RESULTS: Two patients were included in this study. Both of them had significantly increased adrenocorticotropic hormone level and skin and mucosal pigmentation. They were initially clinically diagnosed with Addison's disease and received hydrocortisone treatment. However, both patients developed progressive neurological symptoms following infectious disease. Further brain magnetic resonance imaging was completed, and the results suggested demyelinating lesions. Molecular genetics suggested variations in the ABCD1 gene, which were c.109_110insGCCA (p.C39Pfs*156), c.1394-2 A > C (NM_000033), respectively. Therefore, the two patients were finally diagnosed with X-ALD, whose classification had progressed from X-ALD (Addison's-only) to childhood cerebral adrenoleukodystrophy (CCALD). Moreover, the infection exacerbates the demyelinating lesions and accelerates the onset of neurological symptoms. Neither the two variation sites in this study had been previously reported, which extends the ABCD1 variation spectrum. CONCLUSIONS: Patients with only symptoms of adrenal insufficiency cannot be simply clinically diagnosed with Addison's disease. Being alert to the possibility of ABCD1 variation is necessary, and complete genetic testing is needed as soon as possible to identify X-ALD (Addison's-only) early to achieve regular monitoring of the disease and receive treatment early. In addition, infection, as a hit factor, may aggravate demyelinating lesions of CCALD. Thus, patients should be protected from external environmental factors to delay the progression of cerebral adrenoleukodystrophy.

Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.

INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C2h), as an indicator of safety and efficacy, are important for optimizing therapy. OBJECTIVE: The objective of this study was to establish machine learning (ML) models to predict the C2h, that can be used for establishing an individualized dosing regimen in clinical practice. METHODS: Published population pharmacokinetic (PopPK) models for adults were searched based on PubMed and ultimately four reliable models were selected for simulating individual C2h datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C2h obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C2h. Real-world data were used for predictive performance evaluations. Virtual trials were used to compare ML-optimized doses with PopPK model-optimized doses. RESULTS: Categorical boosting (CatBoost) exhibited the highest prediction ability. Target C2h can be predicted using the ML model combined with the dosing regimen and three covariates (N-acetyltransferase 2 [NAT2] genotypes, weight and race [Asians and Africans]). Real-world data validation results showed that the ML model can achieve an overall prediction accuracy of 93.4%. Using the final ML model, the mean absolute prediction error value decreased by 45.7% relative to the average of PopPK models. Using the ML-optimized dosing regimen, the probability of target attainment increased by 43.7% relative to the PopPK model-optimized dosing regimens. CONCLUSION: Machine learning models were developed with great predictive performance, which can be used to determine the individualized initial dose of isoniazid in adult patients.

Optimal use of ß-lactams in neonates: machine learning-based clinical decision support system.

BACKGROUND: Accurate prediction of the optimal dose for ß-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in making optimal dose selections. METHODS: Five ß-lactam antibiotics (amoxicillin, ceftazidime, cefotaxime, meropenem and latamoxef), commonly used to treat neonatal sepsis, were selected. The CDSS was constructed by incorporating the drug, patient, dosage, pharmacodynamic, and microbiological factors. The CatBoost ML algorithm was used to build the CDSS. Real-world studies were used to evaluate the CDSS performance. Virtual trials were used to compare the CDSS-optimized doses with guideline-recommended doses. FINDINGS: For a specific drug, by entering the patient characteristics and pharmacodynamic (PD) target (50%/70%/100% fraction of time that the free drug concentration is above the minimal inhibitory concentration [fT > MIC]), the CDSS can determine whether the planned dosing regimen will achieve the PD target and suggest an optimal dose. The prediction accuracy of all five drugs was >80.0% in the real-world validation. Compared with the PopPK model, the overall accuracy, precision, recall, and F1-Score improved by 10.7%, 22.1%, 64.2%, and 43.1%, respectively. Using the CDSS-optimized doses, the average probability of target concentration attainment increased by 58.2% compared to the guideline-recommended doses. INTERPRETATION: An ML-based CDSS was successfully constructed to assist clinicians in selecting optimal ß-lactam antibiotic doses. FUNDING: This work was supported by the National Natural Science Foundation of China; Distinguished Young and Middle-aged Scholar of Shandong University; National Key Research and Development Program of China.

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future.

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

Ratiometric fluorescence platform for the ultrasensitive detection of kanamycin based on split aptamer co-recognition triggers Mg2+-DNAzyme-driven DNA walker systems.

In this work, a novel 3D-DNA walker signal amplification strategy was designed to construct a fluorescent aptasensor for the detection of kanamycin (KAN). The aptasensor utilizes split aptamers for the synergistic recognition of KAN. The presence of KAN induces the split aptamers recombination to form the Mg2+-DNAzyme structure, which is activated by Mg2+ to drive the 3D-DNA walker process for cascading signal amplification. Employing gold nanoflowers (AuNFs) as walking substrate material increases the local DNA concentration to enhance the walker efficiency. The prepared fluorescent aptasensor achieved efficient and sensitive detection of KAN with satisfactory results in the concentration range of 1 × 10-8 - 1 × 10-3 µg/kg and the detection limit of 5.63 fg/kg. Meanwhile, the designed fluorescent aptasensor exhibited favorable specificity, anti-interference, storage stability and reproducibility, and verified the feasibility of its application in milk samples. The present work provides an effective tool for the regulation of KAN contamination in animal-derived foods with promising prospects.

Efficacy, Safety, and Population Pharmacokinetics of Eltrombopag in Children with Different Severities of Aplastic Anemia.

Eltrombopag was approved as a first-line treatment for patients older than 2 years old with severe aplastic anemia (SAA). However, data on eltrombopag in children with different types of aplastic anemia (AA), especially non-severe AA (NSAA), are limited. We performed a prospective, single-arm, and observational study to investigate eltrombopag's efficacy, safety, and pharmacokinetics in children with NSAA, SAA, and very severe AA (VSAA). The efficacy and safety were assessed every 3 months. The population pharmacokinetic (PPK) model was used to depict the pharmacokinetic profile of eltrombopag. Twenty-three AA children with an average age of 7.9 (range of 3.0-14.0) years were enrolled. The response (complete and partial response) rate was 12.5%, 50.0%, and 100.0% after 3, 6, and 12 months in patients with NSAA. For patients with SAA and VSAA, these response rates were 46.7%, 61.5%, and 87.5%. Hepatotoxicity occurred in one patient. Fifty-three blood samples were used to build the PPK model. Body weight was the only covariate for apparent clearance (CL/F) and volume of distribution. The allele-T carrier of adenosine triphosphate-binding cassette transporter G2 was found to increase eltrombopag's clearance. However, when normalized by weight, the clearance between the wild-type and variant showed no statistical difference. In patients with response, children with NSAA exhibited lower area under the curve from time zero to infinity, higher CL/F, and higher weight-adjusted CL/F than those with SAA or VSAA. However, the differences were not statistically significant. The results may support further individualized treatment of eltrombopag in children with AA.

Population pharmacokinetics and dose optimization of levofloxacin in elderly patients with pneumonia.

AIMS: Levofloxacin is a quinolone antibiotic with a broad antibacterial spectrum. It is frequently used in elderly patients with pneumonia. The pharmacokinetic profile of elderly patients changes with age, but data on the pharmacokinetics of levofloxacin in these patients are limited. The aim of this study was to establish a population pharmacokinetic model of levofloxacin in elderly patients with pneumonia and to optimize individualized dosing regimens based on this newly developed model. METHODS: This is a prospective, open-label pharmacokinetic study in elderly patients with pneumonia. Blood samples were collected using an opportunistic approach. The plasma concentrations of levofloxacin were determined by high-performance liquid chromatography. A population pharmacokinetic model was established using nonlinear mixed-effect model software. Monte Carlo simulations were used for dose simulation and dose optimization. RESULTS: Data from 51 elderly patients with pneumonia were used for the population pharmacokinetic analysis. A one-compartment model with first-order elimination was most suitable for describing the data, and the estimated glomerular filtration rate was the only covariate that had a significant impact on the model. The final model estimated that the mean clearance of levofloxacin in elderly patients with pneumonia was 5.26 L/h. Monte Carlo simulation results showed that the optimal dosing regimen for levofloxacin was 750 mg once a day in elderly patients with pneumonia, with a minimum inhibitory concentration of 2 mg/L. CONCLUSIONS: The population pharmacokinetic model of levofloxacin in elderly patients with pneumonia was established, and the dose optimization of levofloxacin was completed through Monte Carlo simulation.

Extracellular vesicles separated from goat milk by differential centrifugation coupled with sodium citrate pretreatments.

Satisfactory separation of milk-derived extracellular vesicles (MEVs) is important for the downstream analysis of the functions and properties of MEVs. However, the presence of abundant proteins in milk hindered the separation of MEVs. In this study, three pretreatment methods, including sodium citrate (SC), acetic acid (AA), and high-speed centrifugation, were adopted to separate MEVs from goat milk while minimizing the impact of protein. The MEVs were then characterized by nanoparticle tracking, transmission electron microscopy and western blotting experiments. The results indicated that pretreatments with AA and SC greatly decreased the impact of casein, but AA pretreatment damaged the surface structure of MEVs. Additionally, the differential centrifugation process resulted in a slight loss of MEVs. Overall, MEVs with small size and high purity can be obtained under 125 k × g centrifugation combined with SC pretreatment, which suggests a promising method for separation of MEVs from goat milk.

Machine Learning: A New Approach for Dose Individualization.

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti-infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

Acceptance test of PET/CT based on national standard and the NEMA measurement program / 中国医学装备

Objective:To perform acceptance test and performance assessment for Siemens Biograph Vision 600 positron-emission tomography/computed tomography(PET/CT)according to the national health industry standard WS 817-2023.Methods:Spatial resolution,sensitivity,scatter fraction,count loss and random coincidence,correction accuracy of count loss and random coincidence,time-of-flight(TOF)resolution of the PET component within the PET/CT system were tested through the measurement program(NU2-2018)of National Electrical Manufacturers Association(NEMA),which was installed inside of the equipment,in accordance with the requirement of national health industry standard WS 817-2023.The PET/CT registration accuracy was measured through Gantry_offset acquisition program that was built into the equipment.Results:The transversely and axially spatial resolutions of Biograph Vision 600 PET/CT were respectively 3.69 mm and 4.10 mm at 1 cm away from the center of visual field,and were respectively 4.26 mm and 4.89 mm at 10 cm away from the center of visual field,and were respectively 4.68 mm and 4.89 mm at 20 cm away from the center of visual field.The sensitivity of 10 cm away from center and radial of visual field were respectively 16.12 kcps/MBq and 16.00 kcps/MBq.The peak value of noise equivalent count rate(NECR)was 281.60 kcps,and the corresponding radioactivity concentration of peak value was 30.69 kBq/ml.The NECR peak value,scatter fraction and maximum value of the error of relative count rate were respectively 38.17% and 4.0%.The TOF resolution was 209.87 ps when the radioactivity concentration was 5.3 kBq/mL.The registration accuracy values of Biograph Vision 600 PET/CT were 0.347 mm,-0.226 mm and 3.659 mm at the directions of x,y and z axis.Conclusion:It is feasible to perform the acceptance test according to the WS 817-2023 standard through uses the NEMA NU2-2018 standard measurement program that is installed inside of the equipment.The performance indicators can meet requirement of standard as the current national standard GB/T 18988.1-2013 and the health industry standard WS 817-2023 that will being implemented in the test of Biograph Vision 600 PET/CT,which can pass acceptance.

Establishment and Evaluation of Animal Model of Acute Myocardial Infarction with Syndrome of Qi and Yin Deficiency / 中国实验方剂学杂志

ObjectiveTo explore the establishment and evaluation methods of the rat model of acute myocardial infarction （AMI） in coronary heart disease with the syndrome of Qi and Yin deficiency by sleep deprivation （SD） combined with isoproterenol （ISO） and preliminarily explore its biological basis. MethodForty SD rats were assigned into normal （no treatment）， SD （treatment in modified multi-platform water environment for 96 h）， ISO （subcutaneous injection of ISO at 100 mg·kg-1 once every other day for a total of 2 times）， and SD+ISO （injection of 100 mg·kg-1 ISO after SD for 72 h and 96 h） groups. The cardiac function was detected by small animal echocardiography. The serum levels of creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， and cardiac troponin T （cTnT） were measured by biochemical methods. The pathological changes of the myocardial tissue were observed by hematoxylin-eosin staining. The general state， body weight， grip strength， body temperature， behaviors in open field test， serum levels of cyclic adenosine monophosphate （cAMP）， cyclic guanosine monophosphate （cGMP）， cAMP/cGMP ratio， red （R）， green （G）， blue （B） values of the tongue surface， and pulse amplitude were observed and measured to evaluate the modeling results. Enzyme-linked immunosorbent assay was employed to determine the serum levels of interleukin-18 （IL-18）， tumor necrosis factor-α （TNF-α）， superoxide dismutase （SOD）， malondialdehyde （MDA）， corticotropin-releasing factor （CRF）， adrenocorticotropic hormone （ACTH）， triiodothyronine （T3）， tetraiodothyronine （T4）， cluster of differentiation 4 （CD4）， and cluster of differentiation 8 （CD8）. ResultIn terms of disease indicators， the ISO and SD+ISO groups had lower cardiac function indicators than the normal group （P<0.01）. The levels of CK， CM-MB， LDH and cTnT elevated in each model group compared with the normal group （P<0.01）. The pathological changes of myocardial tissue were obvious in the ISO and SD+ISO groups. In terms of syndrome indicators， compared with the normal group， the SD and SD+ISO groups showed decreased body weight at each time point （P<0.01）， and the ISO group showed decreased body weight at the time points of 48 h and 72 h （P<0.05， P<0.01）. The paw temperature and rectal temperature increased in the SD group （P<0.01）. The model groups showed weakened grasp strength， lowered R， G， and B values of the tongue surface （P<0.01）， prolonged immobility time （P<0.01）， reduced total distance and number of entering the central area （P<0.01）， decreased average speed （P<0.05， P<0.01）， and increased cAMP and cGMP （P<0.05， P<0.01）. The cAMP/cGMP ratio was increased in the SD+ISO group （P<0.01）， and the pulse amplitude was decreased in the SD and SD+ISO groups （P<0.01）. In terms of serological indicators，compared with the normal group， the levels of IL-18， TNF-α， SOD and MDA were significantly increased in the ISO and SD+ISO groups （P<0.01）， the CRF， ACTH， CORT， T3， T4， CD4 and CD8 in the model groups were increased （P<0.05， P<0.01）. ConclusionSleep deprivation for 96 h combined with high-dose ISO can successfully establish a rat model of acute myocardial infarction in coronary heart disease with the syndrome of Qi and Yin deficiency. The model evaluation system can be built with disease indicators of western medicine， histopathological indicators， macroscopic indicators of traditional Chinese medicine， and serological indicators.

Population pharmacokinetics of antibacterial agents in the older population: a literature review.

INTRODUCTION: Older individuals face an elevated risk of developing bacterial infections. The optimal use of antibacterial agents in this population is challenging because of age-related physiological alterations, changes in pharmacokinetics (PK) and pharmacodynamics (PD), and the presence of multiple underlying diseases. Therefore, population pharmacokinetics (PPK) studies are of great importance for optimizing individual treatments and prompt identification of potential risk factors. AREA COVERED: Our search involved keywords such as 'elderly,' 'old people,' and 'geriatric,' combined with 'population pharmacokinetics' and 'antibacterial agents.' This comprehensive search yielded 11 categories encompassing 28 antibacterial drugs, including vancomycin, ceftriaxone, meropenem, and linezolid. Out of 127 studies identified, 26 (20.5%) were associated with vancomycin, 14 (11%) with meropenem, and 14 (11%) with piperacillin. Other antibacterial agents were administered less frequently. EXPERT OPINION: PPK studies are invaluable for elucidating the characteristics and relevant factors affecting the PK of antibacterial agents in the older population. Further research is warranted to develop and validate PPK models for antibacterial agents in this vulnerable population.

Accuracy of antibiotic concentrations in drug dispensing in neonates: a laboratory-based study.

BACKGROUND: Antibacterial therapy plays a crucial role in neonatal infections. The efficacy of antibacterial agents is closely related to the actual dose given to neonates. So we evaluated factors potentially affecting the actual dose of intravenous antibiotics during dispensing process in neonates. METHODS: Meropenem, cefoperazone/sulbactam and piperacillin/tazobactam with two strengths were used to evaluate three methods. Method A (MA) was diluted once and the volumes of 5% glucose for MA were meropenem 4.00 mL, cefoperazone/sulbactam 3.00 mL, piperacillin/tazobactam 9.00 mL. Method B (MB) differed by doubling the volume of 5% glucose. The difference in method C (MC) involved diluting with 5% glucose twice. The concentrations were measured by high-performance liquid chromatography. Relative error (RE) was used to evaluate the preparation accuracy. RESULTS: The RE values using MA/MB/MC were: (1) meropenem 0.5 g: 15.1%, 8.0%, 10.4%; 0.25 g: 7.8%, 3.1%, 6.0%; (2) cefoperazone/sulbactam 1.5 g: 13.6%, 4.2%, 3.4%; 0.75 g: 8.8%, 3.5%, 4.0%; (3) piperacillin/tazobactam 4.5 g: 18.2%, 8.7%, 6.3%; 562.5 mg: 8.1%, 2.8%, 6.1%. MB was better than MA in all three drugs. No difference in RE values was found between single and double dilution, except meropenem with 0.25 g. Using MB, meropenem and piperacillin/tazobactam with small drug strength had higher accuracy in preparation. CONCLUSIONS: MB was suitable for neonatal drug dispensing because of its high accuracy and simple operation. Drugs with small strength were promoted due to the high accuracy.

Galactooligosaccharide Mediates NF-κB Pathway to Improve Intestinal Barrier Function and Intestinal Microbiota.

The use of antibiotics to treat diarrhea and other diseases early in life can lead to intestinal disorders in infants, which can cause a range of immune-related diseases. Intestinal microbiota diversity is closely related to dietary intake, with many oligosaccharides impacting intestinal microorganism structures and communities. Thus, oligosaccharide type and quantity are important for intestinal microbiota construction. Galactooligosaccharides (GOS) are functional oligosaccharides that can be supplemented with infant formula. Currently, information on GOS and its impact on intestinal microbiota diversity and disorders is lacking. Similarly, GOS is rarely reported within the context of intestinal barrier function. In this study, 16S rRNA sequencing, gas chromatography, and immunohistochemistry were used to investigate the effects of GOS on the intestinal microbiota and barrier pathways in antibiotic-treated mouse models. The results found that GOS promoted Bifidobacterium and Akkermansia proliferation, increased short-chain fatty acid levels, increased tight junction protein expression (occludin and ZO-1), increased secretory immunoglobulin A (SIgA) and albumin levels, significantly downregulated NF-κB expression, and reduced lipopolysaccharide (LPS), interleukin-IL-1ß (IL-1ß), and IL-6 levels. Also, a high GOS dose in ampicillin-supplemented animals provided resistance to intestinal damage.

A first-in-human phase 1 study of simnotrelvir, a 3CL-like protease inhibitor for treatment of COVID-19, in healthy adult subjects.

Safe and efficacious antiviral therapeutics are in urgent need for the treatment of coronavirus disease 2019. Simnotrelvir is a selective 3C-like protease inhibitor that can effectively inhibit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated the safety, tolerability, and pharmacokinetics of dose escalations of simnotrelvir alone or with ritonavir (simnotrelvir or simnotrelvir/ritonavir) in healthy subjects, as well as the food effect (ClinicalTrials.gov Identifier: NCT05339646). The overall incidence of adverse events (AEs) was 22.2% (17/72) and 6.3% (1/16) in intervention and placebo groups, respectively. The simnotrelvir apparent clearance was 135-369 L/h with simnotrelvir alone, and decreased significantly to 19.5-29.8 L/h with simnotrelvir/ritonavir. The simnotrelvir exposure increased in an approximately dose-proportional manner between 250 and 750 mg when co-administered with ritonavir. After consecutive twice daily dosing of simnotrelvir/ritonavir, simnotrelvir had a low accumulation index ranging from 1.39 to 1.51. The area under the curve of simnotrelvir increased 44.0 % and 47.3 % respectively, after high fat and normal diet compared with fasted status. In conclusion, simnotrelvir has adequate safety and tolerability. Its pharmacokinetics indicated a trough concentration above the level required for 90 % inhibition of SARS-CoV-2 in vitro at 750 mg/100 mg simnotrelvir/ritonavir twice daily under fasted condition, supporting further development using this dosage as the clinically recommended dose regimen.

Controlled Encapsulation of Gold Nanoparticles into Zr-Metal-Organic Frameworks with Improved Detection Limitation of Volatile Organic Compounds via Surface-Enhanced Raman Scattering.

Volatile organic compounds (VOCs) have harmful effects on human health and the environment but detecting low levels of VOCs is challenging due to a lack of reliable biomarkers. However, incorporating gold nanoparticles (Au NPs) into metal-organic frameworks (MOFs) shows promise for VOC detection. In this study, we developed nanoscale Au@UiO-66 that exhibited surface-enhanced Raman scattering (SERS) activity even at very low levels of toluene vapors (down to 1.0 ppm) due to the thickness of the shell and strong π-π interactions between benzenyl-type linkers and toluene. The UiO-66 shell also increased the thermal stability of the Au NPs, preventing aggregation up to 550 °C. This development may be useful for sensitive detection of VOCs for environmental protection purposes.

Use of Geopolymer and Carbon Fiber-Reinforced Polymer for Repairing Reinforced Concrete Deck Soffit.

This study aimed to assess the feasibility of utilizing geopolymer for repairing reinforced concrete beams. Three types of beam specimens were fabricated: benchmark specimens without any grooves, rectangular-grooved beams, and square-grooved beams. The repair materials employed included geopolymer material, and epoxy resin mortar, while carbon fiber sheets were used as reinforcement in select cases. The repair materials were applied to the rectangular and square-grooved specimens, with the carbon fiber sheets attached to the tension side of the specimens. To evaluate the flexural strength of the concrete specimens, a third-point loading test was conducted. The test results indicated that the geopolymer exhibited higher compressive strength and shrinkage rate compared to the epoxy resin mortar. Furthermore, the specimens reinforced with carbon fiber sheets demonstrated even greater strength than the benchmark specimens. In terms of flexural strength under cyclic third-point loading tests, the carbon fiber-reinforced specimens exhibited the ability to withstand over 200 cycles of repeated loading at 0.8 times the ultimate load. In contrast, the benchmark specimens could only withstand seven cycles. These findings highlight that the use of carbon fiber sheets not only enhances compressive strength but also improves resistance to cyclic loading.

Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates.

BACKGROUND AND OBJECTIVE: High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C0) and steady-state area-under-curve (AUC0-24) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions. METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation. RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%. CONCLUSION: C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.