Flexible electrochemical energy storage devices and related applications: recent progress and challenges.

Given the escalating demand for wearable electronics, there is an urgent need to explore cost-effective and environmentally friendly flexible energy storage devices with exceptional electrochemical properties. However, the existing types of flexible energy storage devices encounter challenges in effectively integrating mechanical and electrochemical performances. This review is intended to provide strategies for the design of components in flexible energy storage devices (electrode materials, gel electrolytes, and separators) with the aim of developing energy storage systems with excellent performance and deformability. Firstly, a concise overview is provided on the structural characteristics and properties of carbon-based materials and conductive polymer materials utilized in flexible energy storage devices. Secondly, the fabrication process and strategies for optimizing their structures are summarized. Subsequently, a comprehensive review is presented regarding the applications of carbon-based materials and conductive polymer materials in various fields of flexible energy storage, such as supercapacitors, lithium-ion batteries, and zinc-ion batteries. Finally, the challenges and future directions for next-generation flexible energy storage systems are proposed.

Comparing Artificial Intelligence-Based Versus Conventional Endotracheal Tube Monitoring Systems in Clinical Practice.

Endotracheal tube dislodgement is a common patient safety incident in clinical settings. Current clinical practices, primarily relying on bedside visual inspections and equipment checks, often fail to detect endotracheal tube displacement or dislodgement promptly. This study involved the development of a deep learning, artificial intelligence (AI)-based system for monitoring tube displacement. We also propose a randomized crossover experiment to evaluate the effectiveness of this AI-based monitoring system compared to conventional methods. The assessment will focus on immediacy in detecting and handling of tube anomalies, the completeness and accuracy of shift transitions, and the degree of innovation diffusion. The findings from this research are expected to offer valuable insights into the development and integration of AI in enhancing care provision and facilitating innovation diffusion in medical and nursing research.

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.

Exosomal microRNAs in hepatocellular carcinoma, expanding research field.

In this editorial we comment on the review by Wang et al published in the recent issue of the World Journal of Gastroenterology in 2023. Small extracellular vesicles (exosomes) play important roles in the tumor microenvironment. In this review, the authors introduce the following points: (1) The composition and function of exosomal microRNAs (miRNAs) of different cell origins in hepatocellular carcinoma (HCC); (2) the crosstalk between exosomal miRNAs from stromal cells and immune cells in the tumor microenvironment and the progression of HCC; and (3) the potential applicability of exosomal miRNAs derived from mesen-chymal stem cells in the treatment of HCC. In addition, the potential applicability of exosomal miRNAs derived from mesenchymal stem cells in the treatment of HCC was introduced. In this review, the authors give us an overview of the exosomal RNA and summarize the function of exosomal RNA in HCC, which provides a deeper understanding of exosomal miRNAs to the readers.

A role for curcumin in preventing liver fibrosis in animals: a systematic review and meta-analysis.

Objective: This meta-analysis aimed to determine the efficacy of curcumin in preventing liver fibrosis in animal models. Methods: A systematic search was conducted on studies published from establishment to November 2023 in PubMed, Web of Science, Embase, Cochrane Library, and other databases. The methodological quality was assessed using Sycle's RoB tool. An analysis of sensitivity and subgroups were performed when high heterogeneity was observed. A funnel plot was used to assess publication bias. Results: This meta-analysis included 24 studies involving 440 animals with methodological quality scores ranging from 4 to 6. The results demonstrated that curcumin treatment significantly improved Aspartate aminotransferase (AST) [standard mean difference (SMD) = -3.90, 95% confidence interval (CI) (-4.96, -2.83), p < 0.01, I2 = 85.9%], Alanine aminotransferase (ALT)[SMD = - 4.40, 95% CI (-5.40, -3.40), p < 0.01, I2 = 81.2%]. Sensitivity analysis of AST and ALT confirmed the stability and reliability of the results obtained. However, the funnel plot exhibited asymmetry. Subgroup analysis based on species and animal models revealed statistically significant differences among subgroups. Furthermore, curcumin therapy improved fibrosis degree, oxidative stress level, inflammation level, and liver synthesis function in animal models of liver fibrosis. Conclusion: Curcumin intervention not only mitigates liver fibrosis but also enhances liver function, while concurrently modulating inflammatory responses and antioxidant capacity in animal models. This result provided a strong basis for further large-scale animal studies as well as clinical trials in humans in the future. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42024502671.

Integration of single-cell and bulk RNA sequencing data reveals that CYTOR is a potential prognostic and immunotherapeutic response marker for skin cutaneous melanoma.

Skin cutaneous melanoma (SKCM) is a highly malignant tumor that is prone to immune escape and distant metastasis. Immunotherapy is considered to be the best treatment for patients with SKCM. However, not all patients benefit from it. We observed a significant differential expression of the lncRNA CYTOR in patients with SKCM based on single-cell and bulk RNA sequencing data mining results. The results showed that compared to normal tissue lncRNA CYTOR expression was significantly upregulated in SKCM tissue. Subsequently, we validated this finding in clinical samples, and we also found that the expression of lncRNA CYTOR in SKCM was higher as it progressed. lncRNA CYTOR was differentially expressed in patients who responded to immunotherapy, suggesting that it may serve as a biomarker to predict the efficacy of SKCM immunotherapy. In-depth analysis revealed that lncRNA CYTOR expression was strongly correlated with immune cell infiltration, immune response, and immune checkpoint expression. Meanwhile, our experiments revealed that CYTOR affects SKCM cell invasion and clone formation and is associated with the activation of the EMT pathway. In summary, our findings illustrate, for the first time, the value of CYTOR as a potential prognostic and immunotherapeutic response marker in SKCM.

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.

Growth of Size-Tunable Ag2O Polyhedra and Revelation of Their Bulk and Surface Lattices.

By primarily adjusting the reagent amounts, particularly the volume of AgNO3 solution introduced, Ag2O cubes with decreasing sizes from 440 to 79 nm, octahedra from 714 to 106 nm, and rhombic dodecahedra from 644 to 168 nm are synthesized. 733 nm cuboctahedra are also prepared for structural analysis. With in-house X-ray diffraction (XRD) peak calibration, shape-related peak shifts are recognizable. Synchrotron XRD measurements at 100 K reveal the presence of bulk and surface layer lattices. Bulk cell constants also deviate slightly. They show a negative thermal expansion behavior with shrinking cell constants at higher temperatures. The Ag2O crystals exhibit size- and facet-dependent optical properties. Bandgaps red-shift continuously with increasing particle sizes. Optical facet effect is also observable. Moreover, synchrotron XRD peaks of a mixture of Cu2O rhombicuboctahedra and edge- and corner-truncated cubes exposing all three crystal faces can be deconvoluted into three components with the bulk and the [111] microstrain phase as the major component. Interestingly, while the unheated Cu2O sample shows clear diffraction peak asymmetry, annealing the sample to 450 K yields nearly symmetric peaks even when returning the sample to room temperature, meaning even moderately high temperatures can permanently change the crystal lattice.

Population pharmacokinetics of daptomycin in critically ill patients receiving extracorporeal membrane oxygenation.

BACKGROUND: Daptomycin is widely used in critically ill patients for Gram-positive bacterial infections. Extracorporeal membrane oxygenation (ECMO) is increasingly used in this population and can potentially alter the pharmacokinetic (PK) behaviour of antibiotics. However, the effect of ECMO has not been evaluated in daptomycin. Our study aims to explore the effect of ECMO on daptomycin in critically ill patients through population pharmacokinetic (PopPK) analysis and to determine optimal dosage regimens based on both efficacy and safety considerations. METHODS: A prospective, open-label PK study was carried out in critically ill patients with or without ECMO. The total concentration of daptomycin was determined by UPLC-MS/MS. NONMEM was used for PopPK analysis and Monte Carlo simulations. RESULTS: Two hundred and ninety-three plasma samples were collected from 36 critically ill patients, 24 of whom received ECMO support. A two-compartment model with first-order elimination can best describe the PK of daptomycin. Creatinine clearance (CLCR) significantly affects the clearance of daptomycin while ECMO has no significant effect on the PK parameters. Monte Carlo simulations showed that, when the MICs for bacteria are  ≥1 mg/L, the currently recommended dosage regimen is insufficient for critically ill patients with CLCR > 30 mL/min. Our simulations suggest 10 mg/kg for patients with CLCR between 30 and 90 mL/min, and 12 mg/kg for patients with CLCR higher than 90 mL/min. CONCLUSIONS: This is the first PopPK model of daptomycin in ECMO patients. Optimal dosage regimens considering efficacy, safety, and pathogens were provided for critical patients based on pharmacokinetic-pharmacodynamic analysis.

Mitochondrial quality control in human health and disease.

Mitochondria, the most crucial energy-generating organelles in eukaryotic cells, play a pivotal role in regulating energy metabolism. However, their significance extends beyond this, as they are also indispensable in vital life processes such as cell proliferation, differentiation, immune responses, and redox balance. In response to various physiological signals or external stimuli, a sophisticated mitochondrial quality control (MQC) mechanism has evolved, encompassing key processes like mitochondrial biogenesis, mitochondrial dynamics, and mitophagy, which have garnered increasing attention from researchers to unveil their specific molecular mechanisms. In this review, we present a comprehensive summary of the primary mechanisms and functions of key regulators involved in major components of MQC. Furthermore, the critical physiological functions regulated by MQC and its diverse roles in the progression of various systemic diseases have been described in detail. We also discuss agonists or antagonists targeting MQC, aiming to explore potential therapeutic and research prospects by enhancing MQC to stabilize mitochondrial function.

Measurements of peri-prostatic adipose tissue by MRI predict bone metastasis in patients with newly diagnosed prostate cancer.

Objectives: To investigate the role of MRI measurements of peri-prostatic adipose tissue (PPAT) in predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa). Methods: We performed a retrospective study on 156 patients newly diagnosed with PCa by prostate biopsy between October 2010 and November 2022. Clinicopathologic characteristics were collected. Measurements including PPAT volume and prostate volume were calculated by MRI, and the normalized PPAT (PPAT volume/prostate volume) was computed. Independent predictors of BM were determined by univariate and multivariate logistic regression analysis, and a new nomogram was developed based on the predictors. Receiver operating characteristic (ROC) curves were used to estimate predictive performance. Results: PPAT and normalized PPAT were associated with BM (P<0.001). Normalized PPAT positively correlated with clinical T stage(cT), clinical N stage(cN), and Grading Groups(P<0.05). The results of ROC curves indicated that PPAT and normalized PPAT had promising predictive value for BM with the AUC of 0.684 and 0.775 respectively. Univariate and multivariate analysis revealed that high normalized PPAT, cN, and alkaline phosphatase(ALP) were independently predictors of BM. The nomogram was developed and the concordance index(C-index) was 0.856. Conclusions: Normalized PPAT is an independent predictor for BM among with cN, and ALP. Normalized PPAT may help predict BM in patients with newly diagnosed prostate cancer, thus providing adjunctive information for BM risk stratification and bone scan selection.

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.

Single Cell Map of Human Azoospermia Testis Caused by Cyclophosphamide Chemotherapy.

Chemotherapeutic drugs will affect the process of spermatogenesis. However, most current studies on the effects of chemotherapeutic drugs on spermatogenesis are based on mouse models, with a shortage of human body evidence. In addition, the mechanism of chemotherapeutic drugs causing spermatogenesis disorder is not clear. Therefore, we have collected the testicular tissues of an inguinal-lipoma patient whose testes were resected after chemotherapy and a patient who had normal spermatogenesis disorder and underwent single-nucleus RNA sequencing (snRNA-Seq). After quality control, we obtained a total of 27,957 high-quality cells, including 18,612 normal cells and 9,345 drug-treated cells, which were all used in analyzing the mechanism of chemotherapeutic drugs causing spermatogenesis disorder. This study has provided data resources and references for exploring the mechanism of chemotherapeutic drugs causing spermatogenesis disorder with the insight of protecting the spermatogenic abilities of male tumor patients receiving chemotherapy.

Subgrouping testicular germ cell tumors based on immunotherapy and chemotherapy associated lncRNAs.

Testicular germ cell tumors (TGCT) are the most common reproductive system malignancies in men aged 15-44 years, accounting for 95 % of all testicular tumors. Our previous studies have been shown that long non-coding RNAs (lncRNAs), such as LINC00313, TTTY14 and RFPL3S, were associated with development of TGCT. Subgrouping TGCT according to differential expressed lncRNAs and immunological characteristics is helpful to comprehensively describe the characteristics of TGCT and implement precise treatment. In this study, the TGCT transcriptome data in The Cancer Genome Atlas Program (TCGA) database was used to perform consensus clustering analysis to construct a prognostic model for TGCT. TGCT was divided into 3 subtypes C1, C2, and C3 based on the differentially expressed lncRNAs. C1 subtype was sensitive to chemotherapy drugs, while the C2 subtype was not sensitive to chemotherapy drugs, and C3 subtype may benefit from immunotherapy. We defined the C1 subtype as epidermal progression subtype, the C2 subtype as mesenchymal progression subtype, and the C3 subtype as T cell activation subtype. Subgrouping based on differentially expressed genes (DEGs) and immunological characteristics is helpful for the precise treatment of TGCT.

Mangiferin alleviates diabetic pulmonary fibrosis in mice via inhibiting endothelial-mesenchymal transition through AMPK/FoxO3/SIRT3 axis.

Diabetes mellitus results in numerous complications. Diabetic pulmonary fibrosis (DPF), a late pulmonary complication of diabetes, has not attracted as much attention as diabetic nephropathy and cardiomyopathy. Mangiferin (MF) is a natural small molecular compound that exhibits a variety of pharmacological effects including anti-inflammatory, anti-cancer, anti-diabetes, and anti-fibrosis effects. In this study, we investigated whether long-term diabetes shock induces DPF, and explored whether MF had a protective effect against DPF. We first examined the lung tissues and sections of 20 diabetic patients obtained from discarded lung surgical resection specimens and found that pulmonary fibrosis mainly accumulated around the pulmonary vessels, accompanied by significantly enhanced endothelial-mesenchymal transition (EndMT). We established a mouse model of DPF by STZ injections. Ten days after the final STZ injection, the mice were administered MF (20, 60 mg/kg, i.g.) every 3 days for 4 weeks, and kept feeding until 16 weeks and euthanized. We showed that pulmonary fibrotic lesions were developed in the diabetic mice, which began around the pulmonary vessels, while MF administration did not affect long-term blood glucose levels, but dose-dependently alleviated diabetes-induced pulmonary fibrosis. In human umbilical vein endothelial cells (HUVECs), exposure to high glucose (33.3 mM) induced EndMT, which was dose-dependently inhibited by treatment with MF (10, 50 µM). Furthermore, MF treatment promoted SIRT3 expression in high glucose-exposed HUVECs by directly binding to AMPK to enhance the activity of FoxO3, which finally reversed diabetes-induced EndMT. We conclude that MF attenuates DPF by inhibiting EndMT through the AMPK/FoxO3/SIRT3 axis. MF could be a potential candidate for the early prevention and treatment of DPF.

Compressive buttress compared with off-axial screw fixation for vertical femoral neck fractures in young adults: a prospective, randomized controlled trial.

BACKGROUND: To compare the clinical outcomes of compressive buttress screw (CBS) fixation, a novel screw fixation strategy, to off-axial screw fixation (off-axial partial threaded cannulated screw, OPTCS) for vertical femoral neck fractures (FNFs) in young adults. METHODS: A total of 146 adults younger than 55 years old with high-energy Pauwels type III FNFs were randomized to receive CBS fixation or OPTCS fixation. Primary outcomes were complication rates, including fixation failure, fracture nonunion, and avascular necrosis of the femoral head (ANFH) at 24 months after treatment. Fixation loosening, femoral neck shortening and varus collapse, patient function and quality of life using the Harris hip score (HHS), and EuroQol-5 dimensional-5 levels (EQ-5D-5L) questionnaire (including EQ-5D-5L and EQ-VAS) were assessed as secondary outcomes at 24 months. RESULTS: CBS and OPTCS fixation groups were similar with regard to demographics at baseline. At 24 months, patients in the CBS fixation cohort had a significantly lower rate of fixation failure (10.5% vs. 25.0%, p = 0.041) and fracture nonunion (1.8% vs. 18.3%, p = 0.003) compared with patients who received OPTCS fixation. There was no difference in rate of ANFH (7.0% vs. 11.7%, p = 0.389) between groups. Additionally, patients managed with CBS fixation showed significantly less fixation loosening (19.3% vs. 58.3%, p < 0.001), less severe femoral neck shortening and varus collapse (10.5% vs. 25.0%, p = 0.007), higher HHS (93 vs. 83, p = 0.001) and more excellent grade (68.4% vs. 36.7%, p = 0.008), higher EQ-5D-5L (0.814 vs, 0.581, p < 0.001) and EQ-VAS (85 vs. 80, p = 0.002). CONCLUSION: CBS screw fixation confers significantly lower complication rate in addition to higher functional and quality of life outcomes for young adults with high-energy FNF compared with OPTCS fixation. TRIAL REGISTRATION: This prospective, randomized controlled trial was approved by the institutional review board of our center, Ethics Committee of Shanghai sixth people's Hospital, and registered at www.chictr.org.cn (Approval Number: ChiCTR1900026283; Registered 29 September 2019-Retrospectively registered, https://www.chictr.org.cn/showproj.html?proj=43164 ).

Long-term storage modifies the microRNA expression profile of cryopreserved human semen.

The global practice of cryopreservation of human semen is commonplace in Assisted Reproductive Technology (ART) labs and sperm banks. However, information on the effects of long-term cryopreservation on semen is limited to clinical data summaries and descriptions. For this study, we prepared 4 semen specimens of fresh semen, 4 specimens cryostored for at least 1 year, 3 specimens cryostored for at least 5 years, 4 specimens cryostored for at least 10 years, and 3 specimens cryostored for at least 15 years. Total RNA was extracted from each sample, amplified, labeled, and mapped to the known primary microRNA (miRNA) in the miRBase database, enabling the prediction of novel miRNAs. We found that cryopreservation can lead to changes in miRNA expression, and with the increase in storage time, these changes became more pronounced. Meanwhile, the expression of let-7d-3p, let-7c-5p and let-7i-3p miRNAs changed dynamically over cryostorage time in frozen-thawed human sperm. Furthermore, we analyzed the time-dependent dynamics of cryostorage-expressed miRNAs and their target mRNAs and found that half of the target genes were expressed in oocytes. These intersection genes were mainly enriched in cancer and cytoskeletal signaling pathways. Our findings showed that the miRNA expression profile of cryopreserved human semen is modified by long-term storage. Furthermore, as the storage time increases, the impact on human sperm becomes more pronounced in terms of miRNAs, which may have an effect on subsequent fertilization and embryonic development.

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.

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.