Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model.

AIMS: To develop a semi-mechanistic hepatic compartmental model to predict the effects of rifampicin, a known inducer of CYP3A4 enzyme, on the metabolism of five drugs, in the hope of informing dose adjustments to avoid potential drug-drug interactions. METHODS: A search was conducted for DDI studies on the interactions between rifampicin and CYP substrates that met specific criteria, including the availability of plasma concentration-time profiles, physical and absorption parameters, pharmacokinetic parameters, and the use of healthy subjects at therapeutic doses. The semi-mechanistic model utilized in this study was improved from its predecessors, incorporating additional parameters such as population data (specifically for Chinese and Caucasians), virtual individuals, gender distribution, age range, dosing time points, and coefficients of variation. RESULTS: Optimal parameters were identified for our semi-mechanistic model by validating it with clinical data, resulting in a maximum difference of approximately 2-fold between simulated and observed values. PK data of healthy subjects were used for most CYP3A4 substrates, except for gilteritinib, which showed no significant difference between patients and healthy subjects. Dose adjustment of gilteritinib co-administered with rifampicin required a 3-fold increase of the initial dose, while other substrates were further tuned to achieve the desired drug exposure. CONCLUSIONS: The pharmacokinetic parameters AUCR and CmaxR of drugs metabolized by CYP3A4, when influenced by Rifampicin, were predicted by the semi-mechanistic model to be approximately twice the empirically observed values, which suggests that the semi-mechanistic model was able to reasonably simulate the effect. The doses of four drugs adjusted via simulation to reduce rifampicin interaction.

4-Fluoro-2-(phenyl-amino)-benzoic acid.

The title compound, C13H10FNO2, was obtained by the reaction of 2-bromo-4-fluoro-benzoic acid with aniline. There are two independent mol-ecules, A and B, in the asymmetric unit, with slight conformational differences: the dihedral angles between the aromatic rings are 55.63 (5) and 52.65 (5)°. Both mol-ecules feature an intra-molecular N-H⋯O hydrogen bond. In the crystal, the mol-ecules are linked by pairwise O-H⋯O hydrogen bonds to form A-B acid-acid dimers and weak C-H⋯F inter-actions further connect the dimers.

Nonlinear encoding in diffractive information processing using linear optical materials.

Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

Occurrence of microplastics in the Haima cold seep area of the South China Sea.

The pollution of deep-sea microplastics has received increasing attention. As a special ecosystem in the deep sea, the cold seep area is of great significance for studying the distribution of microplastics in the deep sea. In this work, the distribution and characteristics of microplastics in seawater, sediments, and shellfish in the Haima cold seep area and the correlation between the characteristics of microplastics in different media and the type of media were studied. Microplastics were found in all three media. The abundance of microplastics in different samples from the Haima cold seep area ranged 1.8-3.8 items/L for the seawater, 11.47-96.8 items/kg (d.w.) for the surface sediments, and 0-5 items/individual (0-0.714 items/g) for the shellfish. The amount of microplastics ingested by shellfish varied among different species. The microplastics in these three media were mainly fibrous, dark-colored, small-sized rayon, polyethylene terephthalate (PET), and polyethylene (PE). In the correlation analysis of microplastic characteristics among the three media, it was found that the characteristics of microplastics in different media in the same area were closely related, and each pair of variables showed a significant positive correlation (P ≤ 0.05). The distinctive geographical conditions would accelerate the interchange of microplastics among various media. Principal component analysis showed that habitat contribute to microplastic feature differences in shellfish. Differences in correlation were observed between the characteristics of shellfish microplastics in different regions and the characteristics of microplastics in surrounding seawater and sediments.

A Mid-Infrared Perfect Metasurface Absorber with Tri-Band Broadband Scalability.

Metasurfaces have emerged as a unique group of two-dimensional ultra-compact subwavelength devices for perfect wave absorption due to their exceptional capabilities of light modulation. Nonetheless, achieving high absorption, particularly with multi-band broadband scalability for specialized scenarios, remains a challenge. As an example, the presence of atmospheric windows, as dictated by special gas molecules in different infrared regions, highly demands such scalable modulation abilities for multi-band absorption and filtration. Herein, by leveraging the hybrid effect of Fabry-Perot resonance, magnetic dipole resonance and electric dipole resonance, we achieved multi-broadband absorptivity in three prominent infrared atmospheric windows concurrently, with an average absorptivity of 87.6% in the short-wave infrared region (1.4-1.7 µm), 92.7% in the mid-wave infrared region (3.2-5 µm) and 92.4% in the long-wave infrared region (8-13 µm), respectively. The well-confirmed absorption spectra along with its adaptation to varied incident angles and polarization angles of radiations reveal great potential for fields like infrared imaging, photodetection and communication.

All-optical complex field imaging using diffractive processors.

Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.

All-optical phase conjugation using diffractive wavefront processing.

Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with applications ranging from imaging to beam focusing. Here, we present a diffractive wavefront processor to approximate all-optical phase conjugation. Leveraging deep learning, a set of diffractive layers was optimized to all-optically process an arbitrary phase-aberrated input field, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated this wavefront processor by 3D-fabricating diffractive layers and performing OPC on phase distortions never seen during training. Employing terahertz radiation, our diffractive processor successfully performed OPC through a shallow volume that axially spans tens of wavelengths. We also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and multi-wavelength nature, this diffractive wavefront processor can be used for various applications, e.g., turbidity suppression and aberration correction across different spectral bands.

Pyramid diffractive optical networks for unidirectional image magnification and demagnification.

Diffractive deep neural networks (D2NNs) are composed of successive transmissive layers optimized using supervised deep learning to all-optically implement various computational tasks between an input and output field-of-view. Here, we present a pyramid-structured diffractive optical network design (which we term P-D2NN), optimized specifically for unidirectional image magnification and demagnification. In this design, the diffractive layers are pyramidally scaled in alignment with the direction of the image magnification or demagnification. This P-D2NN design creates high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction-achieving the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor volume. Furthermore, the P-D2NN design maintains its unidirectional image magnification/demagnification functionality across a large band of illumination wavelengths despite being trained with a single wavelength. We also designed a wavelength-multiplexed P-D2NN, where a unidirectional magnifier and a unidirectional demagnifier operate simultaneously in opposite directions, at two distinct illumination wavelengths. Furthermore, we demonstrate that by cascading multiple unidirectional P-D2NN modules, we can achieve higher magnification factors. The efficacy of the P-D2NN architecture was also validated experimentally using terahertz illumination, successfully matching our numerical simulations. P-D2NN offers a physics-inspired strategy for designing task-specific visual processors.

Selective Extraction of Critical Metals from Spent Li-Ion Battery Cathode: Cation-Anion Coordination and Anti-Solvent Crystallization.

Owing to continuing global use of lithium-ion batteries (LIBs), in particular in electric vehicles (EVs), there is a need for sustainable recycling of spent LIBs. Deep eutectic solvents (DESs) are reported as "green solvents" for low-cost and sustainable recycling. However, the lack of understanding of the coordination mechanisms between DESs and transition metals (Ni, Mn and Co) and Li makes selective separation of transition metals with similar physicochemical properties practically difficult. Here, it is found that the transition metals and Li have a different stable coordination structure with the different anions in DES during leaching. Further, based on the different solubility of these coordination structures in anti-solvent (acetone), a leaching and separation process system is designed, which enables high selective recovery of transition metals and Li from spent cathode LiNi1/3Co1/3Mn1/3O2 (NCM111), with recovery of acetone. Recovery of spent LiCoO2 (LCO) cathode is also evidenced and a significant selective recovery for Co and Li is established, together with recovery and reuse of acetone and DES. It is concluded that the tuning of cation-anion coordination structure and anti-solvent crystallization are practical for selective recovery of critical metal resources in the spent LIBs recycling.

Association between sugar-free beverage intake and childhood obesity among Chinese children and adolescents.

BACKGROUND: The relationship between sugar-free beverage (SFB) intake and childhood obesity among Chinese children is unknown. OBJECTIVES: To describe the status of SFB consumption among children and adolescents in China and assess the association between SFB intake and different types of obesity. METHODS: The study was based on the baseline data of an ongoing cohort project named Evaluation and Monitoring on School-based Nutrition and Growth in Shenzhen (EMSNGS). Food frequency questionnaires were used to collect information on SFB consumption in 3227 students aged 9-17. Physical and clinical examinations were conducted by trained investigators and clinicians. Multivariable binary logistic regression models were performed to assess the association between SFB intake and general obesity, overweight/obesity, abdominal obesity, metabolically unhealthy overweight (MUOW)/metabolically unhealthy obesity (MUO). RESULTS: The median age of the participants was 13.28 years. Among the participants, 55.2% were boys, and 66.1% were adolescents. The median SFB consumption was 16.67 mL/d. After adjusting for potential confounding factors, each 100 mL increase in daily SFB intake was associated with an increased risk of overweight/obesity (OR = 1.14; 95%CI: 1.06-1.23), abdominal obesity (OR = 1.12; 95%CI: 1.03-1.23), and MUOW/MUO (OR = 1.12; 95%CI: 1.02-1.21), respectively. Stratified analyses showed that family income may have an impact on the association between SFB intake and overweight/obesity (P for interaction = 0.021) and abdominal obesity (P for interaction = 0.031). CONCLUSION: SFB intake was positively associated with childhood obesity in Chinese children, particularly among individuals with high-income families.

All-optical image denoising using a diffractive visual processor.

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling.

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

Virtual histological staining of unlabeled autopsy tissue.

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.