Development and Validation of Upper Limb Lymphedema in Patients After Breast Cancer Surgery Using a Practicable Machine Learning Model: A Retrospective Cohort Study.

Objective: Upper limb lymphedema is one of the most common adverse events related to surgery owing to the large gap between guideline implementation and the intended clinical outcomes. However, the monitoring of limb lymphedema remains challenging because of vague clinical presentations. This study aimed to develop and validate practical predictive models for upper limb lymphedema through machine learning. Methods: We retrospectively collected clinical data to develop models for early risk prediction of upper limb lymphedema based on a single-center electronic health record data from patients who underwent breast cancer surgery from June 2021 through June 2023. For prediction model building, 70% and 30% of the data were randomly split into training and testing sets, respectively. We then developed an upper limb lymphedema prediction model using machine learning algorithms, which included random forest model (RFM), generalized logistic regression model (GLRM), and artificial neural network model (ANNM). For evaluating the model's performance, we used the area under the receiver operating characteristic curve (AUROC), calibration curve to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve analysis (DCA). Results: Of the 3201 patients screened for eligibility, 3160 participants were recruited for the prediction model. Among these, Body Mass Index (BMI), hypertension, TNM, lesion site, level of lymph node dissection(LNMD), treatment, and nurse were independent risk factors for upper limb lymphedema and were listed as candidate variables of ML-based prediction models. The RFM algorithm, in combination with seven candidate variables, demonstrated the highest prediction efficiency in both the training and internal verification sets, with an area under the curve (AUC) of 0.894 and 0.889 and a 95% confidence interval (CI) of 0.839-0.949 and 0.834-0.944, respectively. The other two types of prediction models had prediction efficiencies between AUCs of 0.731 and 0.819 and 95% CIs of 0.674-0.789 and 0.762-0.876, respectively. Conclusion: The interpretable predictive model helps physicians more accurately predict the upper limb lymphedema risk in patients undergoing breast cancer surgery. Especially for the RFM, this newly established machine learning-based model has shown good predictive ability for distinguishing high risk of upper limb lymphedema, which could facilitate future clinical decisions, hospital management, and improve outcomes.

[Molecular mechanisms underlying daptomycin resistance].

Due to potent bactericidal activity and low rate of drug-resistance, daptomycin is recognized as first line antibiotic to treat serious infections caused by drug-resistant Gram-positive pathogens. However, the incidence of daptomycin resistance is increasing due to its widespread application. Alteration of cell wall homeostasis and membrane phospholipid metabolism is involved in daptomycin resistance. The unique mode of action underlying daptomycin resistance in important pathogens, including Staphylococcus aureus and Enterococci, is presented in this paper.

The Discovery of an Iridium(III) Dimer Complex as a Potent Antibacterial Agent against Non-Replicating Mycobacterium smegmatis.

Novel agents are urgently needed to rapidly kill drug-resistant Mycobacterium tuberculosis. Noble metal complexes, particularly polypyridyl iridium complexes serving as therapeutic agents, have attracted considerable interest recently, due to their significant cytotoxic or antimicrobial activities. Here, we reported an polypyridyl iridium dimer complex [Ir(ppy)2Cl]2 (3), with ppy = phenylpyridine, which was found to be active against both exponential growing and non-replicating M. smegmatis, with minimum inhibitory concentration values of 2 µg/mL, and exhibited rapid bactericidal kinetics, killing pathogens within 30⁻60 min. Moreover, 3 was demonstrated to generate a large amount of reactive oxygen species and to be effective in drug-resistant strains. Taken together, the selectively active iridium(III) dimer complex showed promise for use as a novel drug candidate for the treatment of M. tuberculosis infection.

Synthesis of water-soluble curcumin derivatives and their inhibition on lysozyme amyloid fibrillation.

The potential application of curcumin was heavily limited in biomedicine because of its poor solubility in pure water. To circumvent the detracting feature, two novel water-soluble amino acid modified curcumin derivatives (MLC and DLC) have been synthesized through the condensation reaction between curcumin and Nα-Fmoc-Nε-Boc-l-lysine. Benefiting from the enhanced solubility of 3.32×10-2g/mL for MLC and 4.66×10-2g/mL for DLC, the inhibition effects of the as-prepared derivatives on the amyloid fibrillation of lysozyme (HEWL) were investigated detaily in water solution. The obtained results showed that the amyloid fibrillation of HEWL was inhibited to a great extent when the concentrations of MLC and DLC reach to 20.139mM and 49.622mM, respectively. The fluorescence quenching upon the addition of curcumin to HEWL provide a support for static and dynamic recombination quenching process. The binding driving force was assigned to classical hydrophobic interaction between curcumin derivatives and HEWL. In addition, UV-Vis absorption and circular dichroism (CD) spectra confirmed the change of the conformation of HEWL.

Discovery of an Octahedral Silicon Complex as a Potent Antifungal Agent.

Octahedral transition metal complexes have been shown to have tremendous applications in chemical biology and medicinal chemistry. Meanwhile, structural transition metals can be replaced by inert octahedral silicon in a proof-of-principle study. We here introduce the first example of octahedral silicon complexes, which can very well serve as an efficient antimicrobial agent. The typical silicon arenediolate complex 1 {[(phen)2Si(OO)](PF6)2, with phen = 1,10-phenanthroline, OO = 9,10-phenanthrenediolate} exhibited significant inhibition towards the growth of Cryptococcus neoformans with MIC and MFC values of 4.5 and 11.3 µM, respectively. Moreover, it was fungicidal against both proliferative and quiescent Cryptococcus cells. This work may set the stage for the development of novel antifungal drugs based upon hexacoodinate silicon scaffolds.