Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment.

Computational approaches using theoretical calculations and data scientific methods have become increasingly important in materials science and technology, with the development of relevant methodologies and algorithms, the availability of large materials data, and the enhancement of computer performance. As reviewed herein, we have developed computational methods for the design and prediction of inorganic materials with a particular focus on the exploration of semiconductors and dielectrics. High-throughput first-principles calculations are used to systematically and accurately predict the local atomic and electronic structures of polarons, point defects, surfaces, and interfaces, as well as bulk fundamental properties.

Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science.

Obtaining a fine approximation of a black-box function is important for understanding and evaluating innovative materials. Active learning aims to improve the approximation of black-box functions with fewer training data. In this study, we investigate whether active learning based on uncertainty sampling enables the efficient approximation of black-box functions in regression tasks using various material databases.

The long-term post-surgical outcome of intermediate anorectal malformation in our department.

Background: Posterior sagittal anorectoplasty and laparoscopic-assisted anorectal pull-through are preferred for anorectal malformation (ARM) today, while careful pull-through procedures with sacroperineal approach yield excellent outcomes. This study focuses on a pull-through procedure emphasizing continence mechanism preservation and compares outcomes with historical studies with various procedures.

Methods: Bowel function of patients with intermediate ARM followed up for over 10 years post-surgically was assessed.

Genetically encoded fluorescent sensors for visualizing polyamine levels, uptake, and distribution.

Polyamines are abundant and physiologically essential biomolecules that play a role in numerous processes, but are disrupted in diseases such as cancer, and cardiovascular and neurological disorders. Despite their importance, measuring free polyamine concentrations and monitoring their metabolism and uptake in cells in real-time remains impossible due to the lack of appropriate biosensors. Here we engineered, characterized, and validated the first genetically encoded biosensors for polyamines, named iPASnFRs.

Combination Therapy of Lenvatinib and Hepatic Arterial Infusion Chemotherapy Using Cisplatin With Lipiodol and 5-Fluorouracil: A Potential Breakthrough Therapy for Unresectable Advanced Hepatocellular Carcinoma.

Introduction: In 2021, the LEOPARD trial reported that the combination of lenvatinib+one-shot cisplatin infusion might contribute to improving the results of conventional advanced hepatocellular carcinoma (HCC) treatment. Thus, combination therapy with lenvatinib and catheterization has emerged as a focal point in treating advanced HCC. Conversely, the New FP regimen consists of low-dose cisplatin (CDDP) combined with 5-fluorouracil (5-FU) and lipiodol via hepatic arterial infusion chemotherapy (HAIC), with a high response rate of approximately 70%.

Extraction of local structure differences in silica based on unsupervised learning.

Silica exhibits a rich phase diagram with numerous stable structures existing at different temperature and pressure conditions, including its glassy form. In large-scale atomistic simulations, due to the small energy difference, several phases may coexist. While, in terms of long-range order, there are clear differences between these phases, their short- or medium-range structural properties are similar for many phases, thus making it difficult to detect the structural differences.

Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array.

Introduction: Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning.

Methods: Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included.

Rapidly growing intramuscular lipoma: a unique entity of benign lipomas in children.

This report presents a case of an intramuscular lipoma observed in the left back of a healthy female toddler. It was resected after 3 months of observation because of rapid enlargement, raising suspicion of malignancy. Histopathological examination confirmed a diagnosis of intramuscular lipoma without malignant and blastemal components.

Trends in endometrial carcinoma: experience of a single institute for four decades.

Objective: To determine the current prognosis of endometrial carcinoma in Japan by analyzing long-term trends in endometrial carcinoma at our hospital.

Methods: We divided 1463 patients with endometrial carcinoma who visited our hospital between 1984 and 2022 into group 1984-1991, group 1992-1999, group 2000-2006, group 2007-2014 and group 2015-2022. Trends were determined using the Jonckheere-Terpstra and Cochran-Armitage tests.

Refractory ovarian squamous cell carcinoma arising from a seromucinous borderline tumor with squamous overgrowth: A case report.

Ovarian squamous cell carcinoma (SCC) is rare, and most cases arise from ovarian teratomas. Herein, we present a case of ovarian SCC arising from an ovarian seromucinous borderline tumor (SMBT) with squamous overgrowth. A 71-year-old woman an underwent emergency laparotomy due to the rupture of a right ovarian tumor suspected to be a borderline or malignant tumor.

Human-Machine Collaboration for Accelerated Discovery of Promising Oxygen Evolution Electrocatalysts with On-Demand Elements.

A drastically efficient method for identifying electrocatalysts with desirable functionality is a pressing necessity for making a breakthrough in advanced water-electrolyzers toward large-scale green hydrogen production and addressing the significant challenge of carbon neutrality. Despite extensive investigations over the last several centuries, it remains a time-consuming task to identify even one promising affordable electrocatalyst without platinum-group-metal (PGM) for one electrochemical reaction due to its great complexities, particularly for the key anode reaction in the water-electrolyzer of the oxygen evolution reaction (OER). In this study, we demonstrate that a human-machine collaboration based on stepwise-evolving artificial intelligence (se-AI) can significantly shorten the development period of PGM-free multimetal OER electrocatalysts with performance beyond a PGM of RuO.

External Esophageal Stenting Technique in Palliation for Tracheal Agenesis in a Case of Esophageal Lung: A Lesson Learned from the Experience for Tracheomalacia.

Tracheal agenesis (TA) is a rare congenital anomaly with an incidence of 1 per 50,000 newborns. It appears at birth with severe respiratory distress, cyanosis, and inaudible crying. Prompt esophageal intubation and long-term management of the esophageal airway are essential to overcome this catastrophic condition.

Revealing factors influencing polymer degradation with rank-based machine learning.

The efficient treatment of polymer waste is a major challenge for marine sustainability. It is useful to reveal the factors that dominate the degradability of polymer materials for developing polymer materials in the future. The small number of available datasets on degradability and the diversity of their experimental means and conditions hinder large-scale analysis.

Mapping stress inside living cells by atomic force microscopy in response to environmental stimuli.

The response of cells to environmental stimuli, under either physiological or pathological conditions, plays a key role in determining cell fate toward either adaptive survival or controlled death. The efficiency of such a feedback mechanism is closely related to the most challenging human diseases, including cancer. Since cellular responses are implemented through physical forces exerted on intracellular components, more detailed knowledge of force distribution through modern imaging techniques is needed to ensure a mechanistic understanding of these forces.

Koopmans' Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules.

Density functional theory (DFT) is a significant computational tool that has substantially influenced chemistry, physics, and materials science. DFT necessitates parametrized approximation for determining an expected value. Hence, to predict the properties of a given molecule using DFT, appropriate parameters of the functional should be set for each molecule.

SLFN11 is a BRCA Independent Biomarker for the Response to Platinum-Based Chemotherapy in High-Grade Serous Ovarian Cancer and Clear Cell Ovarian Carcinoma.

BRCA1/2 mutations are robust biomarkers for platinum-based chemotherapy in epithelial ovarian cancers. However, BRCA1/2 mutations in clear cell ovarian carcinoma (CCC) are less frequent compared with high-grade serous ovarian cancer (HGSC). The discovery of biomarkers that can be applied to CCC is an unmet need in chemotherapy.

Determination of hyper-parameters in the atomic descriptors for efficient and robust molecular dynamics simulations with machine learning forces.

The atomic descriptors used in machine learning to predict forces are often high dimensional. In general, by retrieving a significant amount of structural information from these descriptors, accurate force predictions can be achieved. On the other hand, to acquire higher robustness for transferability without overfitting, sufficient reduction of descriptors should be necessary.

The Junction Between the Peristaltic and Non-peristaltic Bowel (Shore Break) is Found in the Transition Zone in Hirschsprung's Disease.

Background: The junction between the peristaltic and non-peristaltic bowel, which is named the "shore break" (SB), observed on endoscopy is thought to be the boundary between normal and abnormal motility in Hirschsprung's disease (HD). The transition zone (TZ), which is the histopathological border, does not have normal motility and should be resected. This study aimed to evaluate the histopathological findings of the SB and determine the association between the SB and TZ.