Relationship of magnetic ordering and crystal structure in lanthanide ferromagnets Gd, Tb, Dy, and Ho at high pressures

The pressure dependence of the magnetic ordering temperatures for the lanthanide ferromagnets Gd, Tb, Dy, and Ho has been investigated in the pressure region up to 18 GPa by two types of magnetic measurements using a superconducting quantum interference device (SQUID). The present magnetic measurements enabled us to investigate the pressure dependence of the magnetization intensity at low magnetic fields as well as the magnetic ordering temperatures. Their results are interpreted in the light of such previous experiments as magnetic susceptibility, magnetization, electrical resistance, neutron diffraction, and Mössbauer spectroscopy measurements. All of the magnetic orderings in the above four elements were suppressed down to less than the detection level, being related to the structural transition. The ferromagnetic ordering in Gd, Tb, Dy, and Ho is stabilized in the hcp structure. The magnetic anomalies due to the helimagnetic ordering of Tb and Dy disappear at the Sm-to-dhcp transition and the hcp-to-Sm transition, respectively, while that of Ho disappears in the Sm-type phase near the Sm-to-dhcp transition

Turicibacter and Acidaminococcus predict immune-related adverse events and efficacy of immune checkpoint inhibitor

IntroductionImmune checkpoint inhibitors have had a major impact on cancer treatment. Gut microbiota plays a major role in the cancer microenvironment, affecting treatment response. The gut microbiota is highly individual, and varies with factors, such as age and race. Gut microbiota composition in Japanese cancer patients and the efficacy of immunotherapy remain unknown. MethodsWe investigated the gut microbiota of 26 patients with solid tumors prior to immune checkpoint inhibitor monotherapy to identify bacteria involved in the efficacy of these drugs and immune-related adverse events (irAEs).ResultsThe genera Prevotella and Parabacteroides were relatively common in the group showing efficacy towards the anti-PD-1 antibody treatment (effective group). The proportions of Catenibacterium (P = 0.022) and Turicibacter (P = 0.049) were significantly higher in the effective group than in the ineffective group. In addition, the proportion of Desulfovibrion (P = 0.033) was significantly higher in the ineffective group. Next, they were divided into irAE and non-irAE groups. The proportions of Turicibacter (P = 0.001) and Acidaminococcus (P = 0.001) were significantly higher in the group with irAEs than in those without, while the proportions of Blautia (P = 0.013) and the unclassified Clostridiales (P = 0.027) were significantly higher in the group without irAEs than those with. Furthermore, within the Effective group, Acidaminococcus and Turicibacter (both P = 0.001) were more abundant in the subgroup with irAEs than in those without them. In contrast, Blautia (P = 0.021) and Bilophila (P= 0.033) were statistically significantly more common in those without irAEs.DiscussionOur Study suggests that the analysis of the gut microbiota may provide future predictive markers for the efficacy of cancer immunotherapy or the selection of candidates for fecal transplantation for cancer immunotherapy

Novel quantitative immunohistochemical analysis for evaluating PD-L1 expression with phosphor-integrated dots for predicting the efficacy of patients with cancer treated with immune checkpoint inhibitors

IntroductionProgrammed cell death ligand 1 (PD-L1) expression in tumor tissues is measured as a predictor of the therapeutic efficacy of immune checkpoint inhibitors (ICIs) in many cancer types. PD-L1 expression is evaluated by immunohistochemical staining using 3,3´-diaminobenzidine (DAB) chronogenesis (IHC-DAB); however, quantitative and reproducibility issues remain. We focused on a highly sensitive quantitative immunohistochemical method using phosphor-integrated dots (PIDs), which are fluorescent nanoparticles, and evaluated PD-L1 expression between the PID method and conventional DAB method.MethodsIn total, 155 patients with metastatic or recurrent cancer treated with ICIs were enrolled from four university hospitals. Tumor tissue specimens collected before treatment were subjected to immunohistochemical staining with both the PID and conventional DAB methods to evaluate PD-L1 protein expression.ResultsPD-L1 expression assessed using the PID and DAB methods was positively correlated. We quantified PD-L1 expression using the PID method and calculated PD-L1 PID scores. The PID score was significantly higher in the responder group than in the non-responder group. Survival analysis demonstrated that PD-L1 expression evaluated using the IHC-DAB method was not associated with progression-free survival (PFS) or overall survival (OS). Yet, PFS and OS were strikingly prolonged in the high PD-L1 PID score group.ConclusionQuantification of PD-L1 expression as a PID score was more effective in predicting the treatment efficacy and prognosis of patients with cancer treated with ICIs. The quantitative evaluation of PD-L1 expression using the PID method is a novel strategy for protein detection. It is highly significant that the PID method was able to identify a group of patients with a favorable prognosis who could not be identified by the conventional DAB method

On Missingness Features in Machine Learning Models for Critical Care: Observational Study

BackgroundMissing data in electronic health records is inevitable and considered to be nonrandom. Several studies have found that features indicating missing patterns (missingness) encode useful information about a patient’s health and advocate for their inclusion in clinical prediction models. But their effectiveness has not been comprehensively evaluated. ObjectiveThe goal of the research is to study the effect of including informative missingness features in machine learning models for various clinically relevant outcomes and explore robustness of these features across patient subgroups and task settings. MethodsA total of 48,336 electronic health records from the 2012 and 2019 PhysioNet Challenges were used, and mortality, length of stay, and sepsis outcomes were chosen. The latter dataset was multicenter, allowing external validation. Gated recurrent units were used to learn sequential patterns in the data and classify or predict labels of interest. Models were evaluated on various criteria and across population subgroups evaluating discriminative ability and calibration. ResultsGenerally improved model performance in retrospective tasks was observed on including missingness features. Extent of improvement depended on the outcome of interest (area under the curve of the receiver operating characteristic [AUROC] improved from 1.2% to 7.7%) and even patient subgroup. However, missingness features did not display utility in a simulated prospective setting, being outperformed (0.9% difference in AUROC) by the model relying only on pathological features. This was despite leading to earlier detection of disease (true positives), since including these features led to a concomitant rise in false positive detections. ConclusionsThis study comprehensively evaluated effectiveness of missingness features on machine learning models. A detailed understanding of how these features affect model performance may lead to their informed use in clinical settings especially for administrative tasks like length of stay prediction where they present the greatest benefit. While missingness features, representative of health care processes, vary greatly due to intra- and interhospital factors, they may still be used in prediction models for clinically relevant outcomes. However, their use in prospective models producing frequent predictions needs to be explored further