Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches.

: This study implements a multi-dimensional methodology to systematically identify potential natural antibiotics derived from the medicinal plants utilized in Ayurvedic practices. : Two primary analytical techniques are employed to explore the antibiotic potential of the medicinal plants. The initial approach utilizes a supervised network analysis, which involves the application of distance measurement algorithms to scrutinize the interconnectivity and relational patterns within the network derived from Ayurvedic formulae.

Theoretical atomic momentum spectroscopy of diatomic, triatomic and polyatomic molecules.

We present a general theory of quantum chemistry-based atomic momentum spectroscopy (QC-AMS) for predicting electron-atom Compton profiles due to the intramolecular motion of each atom in diatomic, triatomic and polyatomic molecules. The atomic motion is assumed to be decomposable into normal-mode molecular vibrations and molecular rotations, and the latter are treated classically. An accuracy assessment of the general theory is performed through comparisons with the AMS Compton profiles of HD and NO, predicted by the full quantum chemistry-based AMS theory that is precise but can work only for diatomic molecules.

Exploring the Trade-Off in the Variational Information Bottleneck for Regression with a Single Training Run.

An information bottleneck (IB) enables the acquisition of useful representations from data by retaining necessary information while reducing unnecessary information. In its objective function, the Lagrange multiplier β controls the trade-off between retention and reduction. This study analyzes the Variational Information Bottleneck (VIB), a standard IB method in deep learning, in the settings of regression problems and derives its optimal solution.

Discovering natural products as potential inhibitors of SARS-CoV-2 spike proteins.

The ongoing global pandemic caused by the SARS-CoV-2 virus has demanded the urgent search for effective therapeutic interventions. In response, our research aimed at identifying natural products (NPs) with potential inhibitory effects on the entry of the SARS-CoV-2 spike (S) protein into host cells. Utilizing the Protein Data Bank Japan (PDBJ) and BindingDB databases, we isolated 204 S-glycoprotein sequences and conducted a clustering analysis to identify similarities and differences among them.

Interpret Gaussian Process Models by Using Integrated Gradients.

Gaussian process regression (GPR) is a nonparametric probabilistic model capable of computing not only the predicted mean but also the predicted standard deviation, which represents the confidence level of predictions. It offers great flexibility as it can be non-linearized by designing the kernel function, made robust against outliers by altering the likelihood function, and extended to classification models. Recently, models combining deep learning with GPR, such as Deep Kernel Learning GPR, have been proposed and reported to achieve higher accuracy than GPR.

Identifying Potential Natural Antibiotics from Unani Formulas through Machine Learning Approaches.

The Unani Tibb is a medical system of Greek descent that has undergone substantial dissemination since the 11th century and is currently prevalent in modern South and Central Asia, particularly in primary health care. The ingredients of Unani herbal medicines are primarily derived from plants. Our research aimed to address the pressing issues of antibiotic resistance, multi-drug resistance, and the emergence of superbugs by examining the molecular-level effects of Unani ingredients as potential new natural antibiotic candidates.

Development and Validation of the Meiji Nutritional Profiling System per Serving Size.

Serving size may be the appropriate reference for calculating food nutritional value. We aimed to assess the nutritional values of Japanese foods based on serving sizes rather than per 100 g by adapting the Meiji Nutritional Profiling System (Meiji NPS). Given the variability in serving sizes across countries, we used Japanese serving sizes to calculate the Meiji NPS scores.

Design of Fragrance Formulations with Antiviral Activity Using Bayesian Optimization.

In case of future viral threats, including the proposed Disease X that has been discussed since the emergence of the COVID-19 pandemic in March 2020, our research has focused on the development of antiviral strategies using fragrance compounds with known antiviral activity. Despite the recognized antiviral properties of mixtures of certain fragrance compounds, there has been a lack of a systematic approach to optimize these mixtures. Confronted with the significant combinatorial challenge and the complexity of the compound formulation space, we employed Bayesian optimization, guided by Gaussian Process Regression (GPR), to systematically explore and identify formulations with demonstrable antiviral efficacy.

Statistical Modeling of Within-Laboratory Precision Using a Hierarchical Bayesian Approach.

Background: Reproducibility has been well studied in the field of food analysis; the RSD is said to follow a Horwitz curve with certain exceptions. However, little systematic research has been done on predicting repeatability or intermediate precision.

Objective: We developed a regression method to estimate within-laboratory SDs using hierarchical Bayesian modeling and analyzing duplicate measurement data obtained from actual laboratory tests.

Transabdominal levatorplasty technique in laparoscopic mesh rectopexy for rectal prolapse.

Rectal prolapse is characterized by a full-thickness intussusception of the rectal wall and is associated with a spectrum of coexisting anatomic abnormalities. We developed the transabdominal levatorplasty technique for laparoscopic rectopexy, inspired by Altemeier's procedure. In this method, following posterior mesorectum dissection, we expose the levator ani muscle just behind the anorectal junction.

The efficacy of simple oral nutritional supplements versus usual care in postoperative patients with gastric cancer: study protocol for a multicenter, open-label, parallel, randomized controlled trial.

Background: Body weight loss (BWL) after gastrectomy impact on the short- and long-term outcomes. Oral nutritional supplement (ONS) has potential to prevent BWL in patients after gastrectomy. However, there is no consistent evidence supporting the beneficial effects of ONS on BWL, muscle strength and health-related quality of life (HRQoL).

Explainable deep-learning-based ischemia detection using hybrid O-15 HO perfusion positron emission tomography and computed tomography imaging with clinical data.

Background: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 HO perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imaging. The classifier uses polar map images with numerical data and visualizes data findings.

Methods: A DLmodel was implemented and evaluated on 138 individuals, consisting of a combined image-and data-based classifier considering 35 clinical, CTA, and PET variables.

Quantitative evaluation model of variable diagnosis for chest X-ray images using deep learning.

The purpose of this study is to demonstrate the use of a deep learning model in quantitatively evaluating clinical findings typically subject to uncertain evaluations by physicians, using binary test results based on routine protocols. A chest X-ray is the most commonly used diagnostic tool for the detection of a wide range of diseases and is generally performed as a part of regular medical checkups. However, when it comes to findings that can be classified as within the normal range but are not considered disease-related, the thresholds of physicians' findings can vary to some extent, therefore it is necessary to define a new evaluation method and quantify it.

Improving gastrointestinal quality of life: romidepsin to tucidinostat in a case of angioimmunoblastic T cell lymphoma.

Relapsed/refractory (R/R) peripheral T cell lymphoma (PTCL) has a poor prognosis, with limited treatment options and generally no durable response. However, long-term remission with the histone deacetylase (HDAC) inhibitor romidepsin has been reported, especially in angioimmunoblastic T cell lymphoma (AITL). Recently, tucidinostat, a novel oral HDAC inhibitor that selectively inhibits class I and class IIb HDACs, was approved for R/R PTCL in China and Japan.

Small animal PET with spontaneous inhalation of O-labelled oxygen gases: Longitudinal assessment of cerebral oxygen metabolism in a rat model of neonatal hypoxic-ischaemic encephalopathy.

Perinatal hypoxic-ischaemic encephalopathy (HIE) is the leading cause of irreversible brain damage resulting in serious neurological dysfunction among neonates. We evaluated the feasibility of positron emission tomography (PET) methodology with O-labelled gases without intravenous or tracheal cannulation for assessing temporal changes in cerebral blood flow () and cerebral metabolic rate for oxygen () in a neonatal HIE rat model. Sequential PET scans with spontaneous inhalation of O-gases mixed with isoflurane were performed over 14 days after the hypoxic-ischaemic insult in HIE pups and age-matched controls.

Application of a Two-Dimensional Mapping-Based Visualization Technique: Nutrient-Value-Based Food Grouping.

Worldwide, several food-based dietary guidelines, with diverse food-grouping methods in various countries, have been developed to maintain and promote public health. However, standardized international food-grouping methods are scarce. In this study, we used two-dimensional mapping to classify foods based on their nutrient composition.

Information maximization-based clustering of histopathology images using deep learning.

Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment becomes complicated and it does not always lead to a cure. Histopathological diagnosis is the usual approach for cancer diagnosis.

Ensemble Machine Learning Approaches Based on Molecular Descriptors and Graph Convolutional Networks for Predicting the Efflux Activities of MDR1 and BCRP Transporters.

Multidrug resistance (MDR1) and breast cancer resistance protein (BCRP) play important roles in drug absorption and distribution. Computational prediction of substrates for both transporters can help reduce time in drug discovery. This study aimed to predict the efflux activity of MDR1 and BCRP using multiple machine learning approaches with molecular descriptors and graph convolutional networks (GCNs).

Prevalence and clinical outcomes of vitamin D deficiency among Japanese multiple myeloma patients: a single-center observational study.

Purpose: Vitamin D plays a crucial role in skeletal metabolism and holds significant importance in the pathophysiology of multiple myeloma (MM). This study aimed to determine the prevalence of vitamin D deficiency among Japanese MM patients and its correlation with clinical outcomes.

Methods: Serum 25-hydroxyvitamin D (25(OH)D) levels were assessed in 68 MM patients at a single institution in Japan, analyzing their association with clinical status, laboratory parameters including procollagen type 1 N-propeptide (P1NP) and tartrate-resistant acid phosphatase 5b (TRACP-5b), health-related quality of life (HR-QOL) scores, and overall survival.